Simulator of interactions of the human immune system: Description of the model for the life cycle of human papillomavirus type 16 and therapeutic vaccines

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Escobar Ospina" "autores" => array:1 [ 0 => array:3 [ "nombre" => "M.E." "apellidos" => "Escobar Ospina" "email" => array:1 [ 0 => "meescobaro@unal.edu.co" ] ] ] "afiliaciones" => array:1 [ 0 => array:2 [ "entidad" => "Departamento de Ingeniería – Sistemas y Computación, Universidad Nacional, Bogotá, Colombia" "identificador" => "aff0005" ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Simulador de interacciones del sistema inmune humano: descripción del modelo para el ciclo de vida del virus de papiloma humano tipo 16 y vacunas terapéuticas" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:8 [ "identificador" => "fig0030" "etiqueta" => "Fig. 6" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr6.jpeg" "Alto" => 1604 "Ancho" => 2833 "Tamanyo" => 349312 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0030" "detalle" => "Fig. " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "Global parameters of the HPV16-ALIFE prototype. This Figure shows the components that allow the initial values of the model to be modified before activating the simulation. These components include: (A) Initial state of viral proteins and activity state of TLR ligands; (B) Initial parameters of therapeutic vaccine under evaluation; (C) Components associated with TLRs (left-side) and cytokines (right-side) signalling pathways. These components represent part of the GUI incorporated into the developed prototype. These parameters can also be modified at runtime." ] ] ] "textoCompleto" => "IntroductionDiseases caused by persistent infectious processes originating from the human papillomavirus type 16 (HPV16) still pose a great challenge to the global population, despite major advances in prevention, monitoring and control. This virus, considered an aetiological agent of several types of cancer (cervical, anal, oropharyngeal, vaginal, penile),<a class="elsevierStyleCrossRef" href="#bib0005">1</a> together with the complexity of the human immune system, and certain control strategies designed through the development of immunisation therapies, are the source of information for the model we present here.The purpose of our work is to contribute to research and development of therapeutic vaccines aimed at controlling the diseases caused by this virus through the construction of an artificial life model.Because immune system responses may differ among various HPV16-infected individuals, broad therapeutic vaccination could induce different effects in each treated patient. In this context, we believe that our work significantly contributes to healthcare informatics, as with our model we propose a futuristic vision of a specific, personalised, and autonomous therapeutic vaccination, which is currently within a virtual environment. This new approach implies that therapeutic vaccines implanted into the host can regulate the cancerous environment generated by the viral infection by means of an autonomous immunisation process. This autonomy within the model allows it to regulate and release, by itself, the number of doses needed by the host, starting at the appropriate time and according to the degree of progression that existing lesions show through the established biomarkers, which update their state depending on the evolution and conditions of their environment.Materials and methodsBackground supporting the HPV16-ALIFE modelThis model is conceptually based on complex systems theory and biological systems theory. Its conception is supported by both theories, primarily because the human immune system is considered a complex system,<a class="elsevierStyleCrossRef" href="#bib0010">2</a> and its essential function is realised when it activates its defence mechanisms in the host to counteract the effects of various infectious agents, including viruses.The two underlying theories, the reasons why the characterisation of the immune system recognises commonalities with both theories, and the biological background supporting the development of this model, are summarised in our previous publication.<a class="elsevierStyleCrossRef" href="#bib0015">3</a> This background includes characterisations of the virus and the human host immune system, the elements that comprise them and the interactions between them, considering how the processes evolve under conditions of natural infection and persistence, and their eventual regression and progression to cancer. We also studied the conceptual development of these relationships influenced by therapeutic vaccines. In relation to the simulated immune system (SIS) and in relation to the theory of complex systems, our model highlights adaptive, evolutionary, and emergent behaviour; and in relation to the theory of biological systems, we emphasise the integration of experiments, the use of computation, and the development of methods to analyse biological information.We present this work in two articles, due to its length. The first aims to describe the conceptualisation and creation process of the artificial life model that we have called HPV16-ALIFE. The second presents what we observed from tests conducted on the prototype, through which we have demonstrated the operability of the model.MethodThis work was developed in four stages. The preliminary stage was the compilation of updated biological background information based on the scientific literature.<a class="elsevierStyleCrossRef" href="#bib0015">3</a> The second stage involved the construction of a conceptual design reflecting three different domains, which interact within a single model, capable of reproducing the biological phenomena involved and feeding back on its own. The third stage was to construct the functional prototype to be used as a virtual laboratory. The fourth stage involved defining the experimental design and performing virtual tests. This last stage will make it possible to observe behaviour and obtain results for subsequent analysis and comparison with real-world clinical data.This model considers the virtual therapeutic vaccine a nanodevice that is implanted in the simulated patient, capable of modulating and controlling the release of each dose, depending on environmental conditions and the activity of certain biomarkers that warn of the presence of lesions caused by persistence of the infection. Since this is an artificial life model, the construction of the nanodevice as such is beyond the scope of the present work. However, the conceptual development of the vaccine is inspired by personalised cervical cancer therapy based on autologous E6/E7 antigen-loaded dendritic cells (DC) with a nanodevice delivery system. The DC vaccine has been previously tested on animal models.<a class="elsevierStyleCrossRefs" href="#bib0020">4–8</a> The nanodevice was conceived based on several ideas and concepts that other authors are exploring, through in-vitro experiments. These concepts include the application of: (i) nanodevices, also termed nanomachines,<a class="elsevierStyleCrossRefs" href="#bib0045">9,10</a> or nanorobots,<a class="elsevierStyleCrossRefs" href="#bib0050">10,11</a> which have controllable locomotion and release of cargo capabilities<a class="elsevierStyleCrossRefs" href="#bib0055">11,12</a>; and (ii) nanovaccines<a class="elsevierStyleCrossRefs" href="#bib0065">13,14</a> aimed at cancer immunotherapy.<a class="elsevierStyleCrossRefs" href="#bib0075">15,16</a> These nanoparticle- and nanomaterial-based cancer vaccines<a class="elsevierStyleCrossRefs" href="#bib0060">12,17</a> target both tumour-specific antigens and adjuvants for targeted delivery to antigen-presenting cells.<a class="elsevierStyleCrossRefs" href="#bib0065">13,18</a> It is important to clarify that the nanodevice as such only exists in the HPV16-ALIFE model, and its use in the physical world refers to a future scenario.Methodological contributionThis model simulates the responses of the innate and adaptive immune system of the host, and the following are its main components: (i) more than 24 different types of cell populations, of both myeloid and lymphoid origin, including their master regulators, markers and surface molecules; (ii) five different types of toll-like receptors (TLRs) with their corresponding ligands and signalling pathways; and (iii) five different cytokine families involving more than 42 of their members, including their particular functions and biological interaction profiles. With respect to the pathogen, this model simulates the HPV16 life cycle, covering the different phases involving the processes of replication, viral transcription, growth, capsid conformation, and oncogenic transformation, considering the activity of the associated viral proteins (early and late). With respect to host-pathogen interaction, this model simulates immune system responses during virus detection, as well as the changes that emerge as long as the infection persists. Similarly, HPV16-ALIFE simulates the application of some therapeutic vaccines that focus on mitigating or curing lesions caused by HPV16 viral infection, including low- and high-grade cervical intraepithelial neoplasia (CIN), as well as malignant, pre-cancer and cancer conditions. By including the application of a specific vaccine in the simulation, this model allows visualisation of the changes resulting from the interaction between multiple related agents, from the perspective of the host and the pathogen. On this basis, it is also possible to show the effects caused by the vaccine and predict optimal vaccination protocols.The HPV16-ALIFE model is original in its design because it was conceived in three different levels, which allow triggering of a downstream response process and an upstream feedback process, involving both host and virus. In addition, this feedback process enables the model to be autonomous in releasing the doses of vaccine necessary to control a lesion, consistent with the information provided by markers that vary according to the conditions of the simulated microenvironment.Current development focuses on HPV16, but this model has the potential to study other cancers originating from different double-stranded DNA viruses, such as EBV (Epstein-Barr Virus), HBV (Hepatitis-B Virus), and MCPyV (Merkel Cell Polyomavirus), among others. However, to simulate double-stranded DNA viruses other than HPV16, it is necessary to adapt the domain corresponding to the life cycle of the new virus and to review the type of response in cell populations, cytokines and TLRs.In the context of HPV16 and HPV16-associated cancers, our model can be extended to other studies to observe the behaviour of the human immune system in relation to the development of new drugs and different therapeutic vaccines with and without adjuvant.Conceptual description of the HPV16-ALIFE modelIn HPV16-ALIFE the interacting components between host, pathogen and vaccine are defined at three different levels. Each of these levels represents an independent microenvironment. However, all three microenvironments interact with each other, feeding back vertically and horizontally (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>).<elsevierMultimedia ident="fig0005"></elsevierMultimedia>The horizontal interaction processes simulate the dynamics between the various components that are part of the same level. For example, interactions between cell cycle phases and the processes of cell differentiation, proliferation, and apoptosis (CDPA) are simulated at Level-1. The vertical interaction processes simulate the dynamics emerging between the different proposed levels. Initially, actions are generated that induce activation of downstream signalling pathways, which, after updating each of the levels in this direction, trigger upstream mobilisation processes. For example, in the case of downstream actions and depending on the cell populations that are differentiated (Level-1), the activation of certain TLR signalling pathways is induced (Level-2). In the same example, in the case of upstream signalling, high levels of E6 and E7 protein expression (Level-2) affect the behaviour of tumour suppressors p53 and pRB (Level-1).The processes that take place within the host (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a> - left-side) are simulated in our model as follows. CDPA processes take place at Level-1, which induce the emergence of various cell populations (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>). Signalling processes linked to the five different toll-like receptors (TLR3, TLR4, TLR7, TLR8, and TLR9) incorporated into the model are developed at Level-2 (<a class="elsevierStyleCrossRef" href="#fig0020">Fig. 4</a>). Processes linked to the five currently recognised cytokine families, including their corresponding members and receptors, are developed at Level-3. These families include TNF (tumour necrosis factor), TGF (transforming growth factor), IFNs (interferons), MIF (macrophage migration inhibitory factor), ILs (interleukins).<elsevierMultimedia ident="fig0010"></elsevierMultimedia>Processes considered to have their origin in the pathogen (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a> - right-side) are simulated in our model as follows. The different phases of the cell cycle (G1, S, G2, M, G0) are included at Level 1, and the behaviour of tumour suppressors involved in viral-like responses (p53, pRB, Tert and p21) is followed. The infectious process caused by HPV16 is developed at Level-2, considering the different stages of the evolution of the virus. This process may start from a condition of natural infection, which may then progress to the different degrees of severity observed in CIN, and eventually progress to more severe conditions. Natural regression processes are also considered at this level, where from stages of persistent infection, neoplasia, or pre-cancer, and through the effects mediated by immune system responses, these infectious conditions may be reversed and then driven towards a state of natural infection and subsequent viral clearance. The different phases associated with the HPV16 life cycle are developed at Level-3, and the behaviour of several of its early (E1, E2, E4, E5, E6, E7) and late (L1 and L2) viral proteins continues in terms of their expression levels.The complexities of vertical and horizontal interactions, and the components and processes that arise between host and pathogen, all simulated in HPV16-ALIFE, are also shown in <a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a> and obey the following dynamics. The model initiates its activity between host and virus, establishing the interactions that arise by connecting cell cycle phases and CDPA processes (Level-1-horizontal interaction, downstream). Depending on the cell populations that differentiate and proliferate, their corresponding TLR signalling pathways are enabled (Level-1 and Level-2-vertical interaction, downstream). Depending on the TLRs that receive a stimulus, or because they encounter a ligand to which they are akin, receptor-ligand binding may occur. Successful binding induces the enabling of the linked cytokine populations (Level-2 and Level-3-vertical interaction, downstream), which take place at the third level. The different cytokines enabled at Level-3 are activated when they detect and bind to cognate receptors. The cytokine population in an active state induces the onset of bottom-up dynamics. In this context, depending on which cytokines are activated, the expression levels of some viral proteins can be affected (Level-3-horizontal interaction, upstream). At Level-2, the expression of viral oncoproteins (especially E6 and E7) is examined as they may affect the behaviour of some TLR signalling pathways (Level-2-horizontal interaction, upstream). Considering the stage of simulation of the HPV16 infectious process and the expression levels of the viral oncoproteins, the behaviour of the tumour suppressors is evaluated at Level-1, as E6 and E7 proteins may affect their activity (Level-1-vertical interaction, upstream).The dynamics described above guarantee the feedback of the model at any point in time. Further details for each proposed level are presented in the sections below.Cellular microenvironment (Nivel-1)The process of cell differentiation takes place at Level-1 of HPV16-ALIFE, according to lineages that are derived from myeloid and lymphoid progenitors from a haematopoietic cell. The cell differentiation process shown in <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a> corresponds to the scope defined in this model. All target cell populations generated by our model are related to: (i) group of master regulators that allow their differentiation, (ii) grouping of cytokines that they can secrete, and (iii) set of surface molecules that they can express. In addition to CDPA processes, this first level also simulates the interactions that arise between the different incorporated cell populations. We mention below some of these cellular interactions, considered essential for the model, and describe the scope that each cell population has within HPV16-ALIFE.<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005">(i)Macrophage population and T-cell lineage: the M1-type macrophage population interacts with the T-helper 1 (Th1) population (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a> - connector 1). The M2-type macrophage population interacts with the T-helper 2 (Th2) population (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a> - connector 2).</li><li class="elsevierStyleListItem" id="lsti0010">(ii)Dendritic cell population and T-cell lineage: in addition to the differentiation process of the DC population: conventional (cDC), plasmacytoid (pDC), myeloid (mDC), and Langerhans cells (LC), shown in <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>, the major histocompatibility complex (MHC) is incorporated, inducing antigen-specific T-cell activation. The latter population interacts with the CD4 effector T-cell set (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a> - connector 3). Within the antigen-specific T-cell population, this model considers a process of cell apoptosis capable of eliminating 90% to 95% of this population, implying that only 5%–10% of these cells are actually interacting with the CD4 effector T-cell population.</li><li class="elsevierStyleListItem" id="lsti0015">(iii)B-cell lineage: in addition to the CDPA processes of the B-cell lineage, this model also incorporates certain procedures that are linked to cell division stages that allow differentiation of the isotypes associated with the antibody populations (IgM, IgG, IgA, IgG1, IgG2, IgG3, IgG4).</li><li class="elsevierStyleListItem" id="lsti0020">(iv)Cytotoxic T-lymphocytes (CTLs): this population interacts with memory T-cell groups including central (Tcm), effector (Tem), tissue-resident (Trm), and stem cell (Tsm) T-cells (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a> - connector 4).</li></ul>Within the cell population-level interactions, this model also simulates essential processes, of which we highlight the following: antigen-mediated activation, cell proliferation, extrafollicular antibody production, germinal centre-independent humoral memory, germinal centre dynamics (including clonal expansion, class switch recombination and somatic hypermutation), cell apoptosis, germinal centre-dependent humoral memory, serological memory, cell population shrinkage, homeostatic proliferation, and trained memory.Three different layers are also incorporated as part of the cellular microenvironment (Level-1), labelled in HPV16-ALIFE as: Layer-1, Layer-2, and Layer-3 (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>). Layer-1 contains the set of master regulators (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a> - yellow rectangles), which allow differentiation of each of the cell populations incorporated into the model. Layer-2 contains the set of cytokines that each cell population is capable of secreting (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a> - green rectangles). Layer-3 enables the set of specific surface molecules to be established for each of the cell populations involved in the model (<a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a> - fuchsia-rectangles).The HPV16-ALIFE model uses two approaches to consider the cell division process: stochastic division and asymmetric division. Stochastic competition can explain how each cell fate can be reached by considering the distribution of a proportion of B-cells.<a class="elsevierStyleCrossRef" href="#bib0095">19</a> Asymmetric division represents an evolutionarily conserved mechanism that allows a single cell to produce two separate daughter cells, which are differentially predestined from their origin. Several classes of lymphocytes use asymmetric division as a strategy to generate cell diversity or regulate their functions.<a class="elsevierStyleCrossRefs" href="#bib0100">20,21</a>The T-cell population is more notably complex, particularly due to the plasticity associated with CD4 + T-cells.<a class="elsevierStyleCrossRefs" href="#bib0110">22–25</a> Subgroups of cell populations that are derived from CD4 +  T-cells are characterised by unique profiles that enable them to be differentiated in the cytokine and surface marker domains. In HPV16-ALIFE this concept of plasticity is applied to processes in which new CD4 + T-cell groups differentiate into different types of helper T-cells (Th1, Th2, Th9, Th17, Th22, Tfh) and regulatory T-cells (Treg).All these elements that interact simultaneously allow us to model the CDPA processes, which are simulated at Level-1 of the model. To incorporate the abovementioned cell populations into the functional prototype, each is associated with a symbol that represents them, as shown in <a class="elsevierStyleCrossRef" href="#fig0015">Fig. 3</a>. Each symbol, in turn, corresponds to an agent within the model, and each agent is linked to the characteristics of its population. Then, events specific to their microenvironments emerge because of the interactions arising between these agents over time.<elsevierMultimedia ident="fig0015"></elsevierMultimedia>Toll-like receptor microenvironment (Nivel-2)The signalling process shown in <a class="elsevierStyleCrossRef" href="#fig0020">Fig. 4</a> occur at Level-2 of HPV16-ALIFE,<a class="elsevierStyleCrossRefs" href="#bib0130">26–30</a> which corresponds to the TLR microenvironment. Considering that HPV is a DNA virus, this model considers the signalling pathways associated with toll-like receptors: TLR3, TLR4, TLR7, TLR8, and TLR9, given their link to viral-like responses.<elsevierMultimedia ident="fig0020"></elsevierMultimedia>TLRs bind to and are activated by different ligands, which localise in different types of organisms and structures. In turn, these bindings allow them to capture certain adaptors that allow them to respond to activation processes. Different adaptors couple to different receptors, and it is the adaptor used that determines which signalling pathway will be activated. Characterised TIR (Toll-interleukin-1 receptor) domain-containing adaptors, such as: MyD88 (Myeloid differentiation factor 88), TIRAP (MAL) (Toll/interleukin-1 receptor domain-containing adapter protein), TRIF (TICAM1) (TIR domain-containing adapter molecule 1), TRAM (TICAM2) (TIR domain-containing adapter molecule 2), SARM1 (Sterile alpha and TIR motif-containing protein 1), BCAP (B-cell adapter for phosphoinositide 3-kinase), have established essential roles over the TLR signalling pathways.<a class="elsevierStyleCrossRefs" href="#bib0135">27,31–33</a>The signalling pathways used to differentiate TLRs allow different cellular responses to be triggered, and their activation depends on various adapters that are activated downstream from the different pattern recognition receptors (PRRs)<a class="elsevierStyleCrossRefs" href="#bib0155">31,33</a> (<a class="elsevierStyleCrossRef" href="#fig0020">Fig. 4</a>). These pathways prove to be fundamental in the orchestration of innate and adaptive immune responses during the processes of inflammation and repair of host tissues.<a class="elsevierStyleCrossRef" href="#bib0130">26</a>Based on the above, the HPV16-ALIFE model incorporates the characterisation of toll-like receptors, specifically TLR3, TLR4, TLR7, TLR8, and TLR9, considering their adaptor families, signalling cascades, and their various components.Cytokine microenvironment (Level-3)Processes linked to cytokine populations are simulated in Level-3 of HPV16-ALIFE, where it is sought to represent their microenvironment. Cytokines induce their responses by binding to cell surface receptors, with specific high affinity on target cells, resulting in the initiation of a series of intracellular signal transduction pathways. The receptors of various cytokines and growth factors are homologous within their extracellular domains, and therefore they can exert their effects through common signal transduction pathways. Several cytokines exhibit a redundancy in function and share overlapping properties, and certain subunits of their cell surface receptors.<a class="elsevierStyleCrossRef" href="#bib0170">34</a> The effects cytokines produce depend on the release time, the environment in which they interact, the neighbours with whom they compete or synergise, the density of the receptor, and the tissue response to each cytokine. In addition, cytokines play a dual role, sometimes producing anti-inflammatory effects and sometimes pro-inflammatory actions, according to the conditions of their microenvironment. All these events allow cytokines to generate a complex network of interactions comprising sophisticated interdependent feedback mechanisms, thus helping to generate effective immune responses.<a class="elsevierStyleCrossRefs" href="#bib0170">34,35</a>HPV16-ALIFE includes most of the cytokines related to human immune system responses, on which there is scientific documentation published up to and including 2019. The developed prototype incorporates: TNF family that includes two members (TNF-α, TNF-β), TGF family including two members (TGF-α, TGF-β), TGF family that includes four members (IFN-α, IFN-β, IFN-γ, IFN-λ), MIF, and ILs family including 40 members (<a class="elsevierStyleCrossRef" href="#fig0025">Fig. 5</a>).<elsevierMultimedia ident="fig0025"></elsevierMultimedia>Depending on the cell populations that differentiate or proliferate in the cellular microenvironment (Level-1), certain types of cytokines are secreted that report an “available” state in their corresponding microenvironment (Level-3). When some of the related receptors are found in the neighbourhood and cytokine-receptor binding occurs, the cytokines switch to an “active” state. When cytokines are reported to be in an active state, other interactions occur simultaneously with the other components in their environment. This exchange of signals between components can induce modifications to the cytokines' own current state, either to increase, decrease, or block their secretion levels. Based on this, the behaviour of other cell populations can be affected, and thus generate dynamics that produce changes in their microenvironment.Therapeutic vaccine monitoringHPV16-ALIFE considers vaccine control an element of the external perspective of this model. The end-user defines the therapeutic vaccine type, load, dose, and frequency to be evaluated against the microenvironments that are integrated in the model. In the current version of the developed prototype, therapeutic vaccines can vary between autologous DC vaccines loaded with E6 and E7 antigens, and vaccines that target immune regulatory pathways, particularly PD-1 (anti-PD1) molecules (<a class="elsevierStyleCrossRef" href="#fig0030">Fig. 6</a> - option B). The user can also establish whether to test the vaccine with or without adjuvant. If an adjuvant is selected, this component can be chosen from a drop-down list that shows the cytokines that are incorporated into the model, and another list that allows components to be selected that are part of the TLR signalling pathways. In addition to selecting these data, the user establishes whether to activate or block the component. For this purpose, our prototype incorporates the appropriate components in its graphical user interface (GUI) to facilitate the execution of these activities (<a class="elsevierStyleCrossRef" href="#fig0030">Fig. 6</a> - option C).<elsevierMultimedia ident="fig0030"></elsevierMultimedia>In line with the evolution in the simulated microenvironments, this model can predict the most appropriate time to release a dose of the pre-programmed therapeutic vaccine. When the model demands a vaccine dose, it reports the number of the specific week in which it simulates its delivery to the virtual host. At the end of the simulation, the amount can be established of vaccine doses that were actually used by the model to control the cancer cell population.Description of the dynamics implemented in the HPV16-ALIFE modelAs in the real world, experiments simulated with this model can induce scenarios of spontaneous regression and/or progression to cancer. Under spontaneous regression scenarios, the virtual host detects key components in its environment that allow it to release the infection by activating defence mechanisms specific to the SIS. Under progression scenarios, the host initially detects a state of natural infection that then evolves, following the process of the productive viral cycle, through which different low-grade and high-grade CIN lesions (CIN1, CIN2, CIN3) may develop. Depending on the setting, such lesions may eventually progress to more complex conditions, such as pre-cancer and cancer. On detection of a lesion caused by HPV16, this model can activate key elements of the innate immune system, acting as a first line of defence; and with persistence of infection, it can also trigger components linked to the adaptive immune system. Then, through aids provided by simulated immunotherapies, some components of the SIS are also stimulated and may modify their responses to achieve the targets driven by each therapeutic vaccine, in an attempt to affect the population of HPV16-infected cells.The description of the dynamics implemented in the HPV16-ALIFE model includes the definition of checkpoints, rules, states, transitions, and interactions, which are detailed below and summarised in <a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>.General parametersWe can define the general criteria of the HPV16-ALIFE model through three types of tools incorporated onto the developed GUI prototype. These tool types include switches, sliders, and selectors. This section explains each of the general parameters that the user can vary, either before starting a simulation or in the runtime (<a class="elsevierStyleCrossRef" href="#fig0030">Fig. 6</a>). Modifying any general parameter can affect the micro-environments in which the different components of the model evolve. Such a situation can occur by varying the initial state of any of the general parameters related to infection, viral proteins, TLRs, starting population, vaccines, cytokines, and members of the TLR signalling pathway. Each of these parameters is described below in literals: (a1), (a2), (a3), (b1), (b2), (c1), and (c2), respectively. Initially, the parameters affect the processes at certain levels. For example, the initial population parameter impacts the microenvironment where the CDPA process takes place; i.e., Level-1 of the model is affected. However, downstream, and upstream feedback processes induce changes in the environment, and therefore each modification in a general parameter will affect the entire dynamics of the model.(a) Switches: some switches are incorporated onto the GUI of the developed prototype that allow an initial state of activity or inactivity to be specified, which are associated with certain criteria that are defined when the simulation process starts. However, the states of these switches may change because of actions generated between the model’s three levels, i.e., interactions between host, virus, and vaccine control (<a class="elsevierStyleCrossRef" href="#fig0030">Fig. 6</a> - option A).(a1) State of infection: This criterion is defined from two switches labelled “Infection?” and “Acute-infection?”. The first switch allows us to set whether the prototype should consider the first step of the simulation starting from a natural pre-infection (“on” state), or, alternatively, start under a non-infectious condition (“off” state). The second switch allows us to set whether the model starts the simulation from an acute infection condition (“on” state), or, alternatively, start from a persistent infection condition (“off” state).(a2) Viral proteins: This component comprises eight switches that represent the initial state (active or inactive) of the early (Es) and late (Ls) proteins involved in the HPV16 life cycle. These switches are labelled in the model as: “prtE1?”, “prtE2?”, “prtE4?”, “prtE5?” “prtE6?”, “prtE7?”, “prtL1?”, “prtL2?”.(a3) TLRs receptors: This component comprises four switches representing the initial state of the TLR3, TLR4, TLR7/TLR8, and TLR9 receivers, specifically.(b) Sliders: This tool allows us to set an initial quantity associated with a general parameter that can vary between two limits, one lower and one upper. The model normally takes the lower range as the initial default value, if the user does not modify the component. The model reads these values and uses them during the simulation process, even if the user modifies them at runtime (<a class="elsevierStyleCrossRef" href="#fig0030">Fig. 6</a> - option B).(b1) Initial Population: This criterion, labelled in the model as” Initial Population”, is defined through a slider that allows us to set the number of haematopoietic cells from which the model will generate initial progenitor cell populations, which will then lead to the CDPA processes.(b2) Vaccines: The criteria defined in the model for vaccine control, implements four sliders labelled “antigen-loaded DCs”, “Dosage”, “Interval-weeks” and “anti-PD1”. These criteria allow us to set the vaccine type, initial load, number of doses, and time intervals measured in weeks, respectively. The vaccine type can vary between DCs loaded with E6/E7 antigens and anti-PD-1 vaccines. The parameters can be defined in advance, at the start of the simulation (pre-programmed conditions), or modified at runtime. HPV16-ALIFE will make use of the pre-programmed vaccination parameters only when a cancer condition is detected that requires the release of a treatment dose, because it is simulating a therapeutic vaccine in a setting associated with cervical cancer. In a pre-programmed scenario, the system will report the week in which the model actually delivers each vaccine dose. This information can then be reviewed displaying the view associated with the command centre in the GUI. If during a simulation the immune system clears the viral infection by itself and no cancerous lesions occur, the model does not need to use the vaccination criteria.(c) Selectors: this tool allows us to complement the vaccination parameters, which is especially useful when we want to add an adjuvant and/or add blockers to a vaccine, or when evaluating a drug containing certain activators and/or blockers. This type of tool allows us to choose an item from a drop-down list. Once chosen, the model is told that the selected component will be intervened either activating or inactivating its essential function. As with the other components, this model reads the values defined by the user, thus altering the normal dynamics of the simulation based on the external change made to these parameters. The model allows the following elements to be blocked or activated, whether declared beforehand or modified at runtime. It is important to clarify that under this concept and in the current version in which the prototype is presented, for each run only one component can be selected for each available selector. Thus, at least four components can intervene simultaneously in the same simulation, which can operate as adjuvants or blockers, acting with each vaccine or drug that is being assessed in a specific patient. This does not mean that a vaccine cannot block or activate other components on the list. The current restriction is that a particular vaccine can work with up to four blockers and/or activators within a single dose to be delivered to the virtual host. For example, in a DC vaccine loaded with E6 and E7 antigens, blocking IL-6, IL-10, and activating IRF3 and IRF7, the count of four components in this test refers specifically to: IL-6, IL-10, IRF3, and IRF7. However, four other different components could have been selected for the test (<a class="elsevierStyleCrossRef" href="#fig0030">Fig. 6</a> - option C).(c1) Cytokine Blockers/Activators: This tool allows the user to block or activate, in advance or at simulation time, components that are part of the cytokine signalling pathways. To help locate them within the list displayed by the tool, we present their components in alphabetical order. This selector includes the following cytokines: IL1 (has two variants: IL1α, IL1β), IL2-IL13, IL15, IL-17 (has six variants: IL17A-IL17F), IL18-IL27, IL30-IL38, IFN-α, IFN-β, IFN-γ, IFN-λ (has three variants: IFNλ1-IFNλ3), MIF, TGF-α, TGF-β, TNF-α, TNF-β.(c2) TLR Blockers/Activators: This tool allows the user to block or activate, in advance or at simulation time, components that are part of TLR signalling pathways. The tool includes the following components: AP1, CREB, ERK, IKKα, IKKβ, IKKɛ, IRAK1, IRAK2, IRAK4, IRF3, IRF7, JNK, MEK1/2, MEK3/6, MEK4/7, mTOR, MyD88, NF-kB, p38, Pellino3, PI3K, Raptor, RIP1, TAB2, TAB3, TAK1, TBK1, TIRAP, TRADD, TRAF3, TRAF6, TRAM, TRIF.In both cases (c1 and c2) the “nobody” option is included. Selecting this option implies that the selector is not participating in the simulation. Once the selectors (cytokines and/or TLRs) are activated, the model will simulate the behaviour of a drug that will be administered to the patient permanently (once a week), with the specific purpose of intervening the signalling pathways associated with cytokines and/or TLR.CheckpointsThe main checkpoints in this model are provided by the activation of TLRs that recognise pathogen-associated molecular patterns (PAMPs), including those of viral type, such as TLR3, TLR7, TLR8 and TLR9; and TLR4, which recognises lipopolysaccharide (LPS) through an accessory molecule (MD2 or CD14). When these viral receptors are activated, they stimulate the transcription of inflammatory genes and trigger certain signalling pathways. These receptors lead to the stimulation of transcription factors that enable the secretion of five possible cytokine families, which contribute to the manifestation or non-manifestation of an innate and adaptive immune response. These receptors are also influenced by the action of certain oncoproteins expressed by HPV16.The checkpoints associated with TLRs are represented in this model on the second level. At Level-2 the interactions are stimulated that arise between TLRs and HPV16, primarily through changes in the behaviour of oncoproteins expressed by the virus in response to stimuli triggered by viral TLRs. In addition, this activity induces the dynamics of exchange with Level-1 of the model, through the infection processes that occur in keratinocytes (KCs) and the modulation generated in other cell populations under infectious conditions. The dynamics of exchange with Level-3 are produced by the secretion and blocking of cytokines that modify the microenvironment and allow the activation of positive and negative feedback cycles between the cell populations involved.RulesThe components that make up the HPV16-ALIFE model are predominantly governed by the following rules: co-selection, network connectivity, rotation, suppression, and state switching.Co-selection, a term introduced by G. W. Hoffmann in his immune network theory,<a class="elsevierStyleCrossRef" href="#bib0180">36</a> denotes the positive mutual selection of individual members within dispersed cell populations, such that the selection of members within each defined population is dependent on interactions, with recognition of one or more members within other populations. For example, a population of positive T cells recognises the antigen and interacts with a diverse group of negative T cells.Network connectivity, proposed by G. W. Hoffmann in his theory,<a class="elsevierStyleCrossRef" href="#bib0180">36</a> indicates that suppressed state is a state of high network connectivity. Immune state is a low-connected state because antigen-specific cells are labelled. Virgin state has the potential to change to a suppressed state or an immune state, whereas the transformation from an immune state to a suppressed state, or vice versa, is more complex. In virgin state, T-cells with low connectivity act as helper cells, and with high connectivity they behave as suppressor cells. HPV16-ALIFE works under these concepts, assuming them as rules by which to evaluate the various states arising from the interactions between the different components and the various networks that comprise it. At the network level, this model implements three types of networks: a network built from cell populations, a network composed of different cytokines and a network of TLRs. At state level, network connectivity allows encounters to be conducted between populations, which mark the conditions of change between circumstances regularly identified by the activation or inactivation of surface molecules. This is the case for encounters between DC and antigen populations, DCs and antigen-specific T-cells, antigen-specific T-cells and B-cells, and other interactions implemented in this model, which are illustrated in <a class="elsevierStyleCrossRef" href="#fig0010">Fig. 2</a>.Rotation of cell populations results from DPAC processes. Cell differentiation is directly affected by the expression levels of various cytokines, leading to the definition of specific cell population types, as presented by the process of T-helper and T-regulatory cell flexibility and plasticity. Cell proliferation is linked to a rate of division, depending on the type of population and the conditions of its microenvironment. Cell elimination occurs in response to the activation of programmed cell death processes or apoptosis induced by interactions arising between elements of the microenvironment, as may occur between cytotoxic T-cells (CTLs) and infected KCs.The specific rules that affect all the procedures implemented in HPV16-ALIFE obey biological conditions that have been previously documented by the scientific community, which are explicitly referenced in each of the processes involved.<a class="elsevierStyleCrossRef" href="#bib0015">3</a>StatesFrom the host’s perspective, the HPV16-ALIFE model considers five possible stable states of cell populations, including virgin, suppressed, immune, autoimmune, and tolerance states, the scope of which corresponds to that proposed by G. W. Hoffmann in his immune network theory.<a class="elsevierStyleCrossRef" href="#bib0180">36</a>The host being in a virgin state implies that they have never been exposed to the pathogen. In this context, the model preserves a balancing environment, induced by mutual elimination between clones of complementary specificities. The behaviour of IgM and IgG antibodies is also incorporated into the model, because of their importance for the microenvironment when this state is reported. The lifespan of B-cells depends on their environment and interaction with other microenvironments, which are mainly modulated by the expression of cytokine populations. For example, TGF plays a protective role and apoptosis mediated by BCR (B-cell receptor) and CD40 (cluster of differentiation 40) is induced after class switch recombination. In this state, cell apoptosis has been observed in the presence of BCR and CD40 after three to four days.<a class="elsevierStyleCrossRef" href="#bib0185">37</a> In addition, IL-21 also produces pro-apoptotic effects on B-cells stimulated with CD40-specific antibodies.<a class="elsevierStyleCrossRef" href="#bib0190">38</a> The virgin state is perturbed by the detection of an infectious condition, which is activated by interactions between PAMPs and TLRs.The suppressed state has high levels of positive and negative T-cells, and their mutual stimulation leads to a significant level of expression of their receptors, molecules, and cytokines. These components in their negative condition block positive receptors, and the positive components block negative receptors. In the suppressed state, specific B-cell receptors are blocked, and in turn, these blocks are generated by the expression of certain cytokines.The immune state, or antigen-exposed state, is distinguished by high levels of positive cell populations and low levels of negative cell populations. In our model, the type of cells initially infected with HPV16 are KCs. If persistent infection develops, high levels of proliferation of KCs are reported, while other cell populations may decrease, such as CTLs and NK (Natural Killer) cells.Autoimmune state is the opposite of immune state, and is characterised by high levels of negative clones and decreased levels of positive cells.Tolerant state is defined as a lack of response that occurs without suppression being evident. Cells in virgin and immune states combine to produce a condition in which there is no response (anergy).State change is generated by interactions that occur between the various components of this model, including receptors, growth factors, TLRs, and cytokines. These recent states can stimulate or override certain conditions associated with CDPA processes, thus modifying the degrees of interaction between components and cell populations.From a cell perspective and as a member of a population, the HPV16-ALIFE model considers ten possible cell states: immature, mature, healthy, infected, cytokine secretion, transcription (infecting from one cell to another, from one generation to another), replication (copying itself), differentiation (acquiring a new phenotype and function), proliferation (multiplication of a cell population), death (natural or by apoptosis).TransitionsGiven the different cell types involved in the HPV16-ALIFE model, the transitions of each population are affected by several events, including the following: (i) CDPA processes; (ii) the influence exerted by the expression of particular molecules on each cell population; (iii) cytokine networks; and (iv) signalling pathways triggered by TLRs. The biological background associated with CDPA processes, as well as the molecules and cytokines that each population expresses, along with the profile and functions of each of the five cytokine families considered in this model, are extensively documented in the works of Goutagny,<a class="elsevierStyleCrossRef" href="#bib0165">33</a> Akdis,<a class="elsevierStyleCrossRef" href="#bib0195">39</a> De Silva,<a class="elsevierStyleCrossRef" href="#bib0200">40</a> Turner,<a class="elsevierStyleCrossRef" href="#bib0205">41</a> and Vazquez.<a class="elsevierStyleCrossRef" href="#bib0210">42</a>Regarding TLRs, and considering that these receptors are implemented in the model under the ABM (agent-based modelling) concept, it is appropriate to specify that transitions will be marked by the perceptions, sensors, agents, actuators, and actions inherent to this technique. Perceptions emerge from the microenvironment in which interactions between cell populations, HPV16 actions, and cytokine networks occur. Host-derived damage-associated molecular patterns (DAMPs) are endogenous molecules normally found in cells that are released during necrosis, including some cytokines. In contrast, pathogen-derived PAMPs regularly become essential components for their survival. DAMPs are recognised by several PRRs, including toll-like receptors. TLRs are receptors that identify PAMPs,<a class="elsevierStyleCrossRefs" href="#bib0215">43,44</a> including those that recognise viruses. Thus, the viral PAMPs signals produced in the model are detected by TLRs (agents) in the extracellular and endosomal environments. In combination with different molecular adaptors (such as MyD88, TIRAP, TRIF and TRAM) - which assume the role of actuators in this model - different TLRs trigger their own signalling pathways. These pathways induce the activation of different components, including transcription factors (e.g., IRFs, CRB, AP-1, NF-kB), effector pathways (e.g., NF-kB), and cytokines (e.g., IFNs and ILs).InteractionsThe most general interaction produced in this model is between HPV16 and the host immune system. Regarding HPV16, interactions develop between cell populations, TLRs and cytokines. In relation to TLRs, interactions are generated between effector pathways, molecules, transcription factors and cytokines. However, this crosstalk is part of four fundamental interactions that are defined in the model as stimulation, inhibition, suppression, and death.Under stimulation scenarios, one component of this model causes the other component, with which it interacts, to be activated and produce an action akin to its natural function. Under inhibition scenarios, the opposite occurs, one component of this model causes the other, with which it interacts, to be temporarily blocked and not allowed to perform its natural function. This scenario continues to operate as long as the established interaction remains active. Under suppression scenarios, one component of this model prevents the action of its counterpart (e.g., pro-apoptotic vs. anti-apoptotic functions, pro-inflammatory vs. anti-inflammatory functions). Under death scenarios, one component of this model overrides the other, with which it interacts, and the overridden element then disappears permanently from the microenvironment (e.g., cellular apoptosis).In HPV16-ALIFE, the interactions between the various cell populations are designed under the ABM technique, as well as those related to TLRs and cytokines. Considering that at the cellular level HPV16 exclusively affects KCs, both the proteins expressed by the virus and this cell population are designed in our model as members of CA (cellular automata) controlled by specific lists. Under this approach both the KC and protein ranges can vary between one and six members of the neighbourhood. In each case their members are placed on a hexagonal grid, so that each KC and each protein can have up to six neighbours simultaneously. The KCs are stored in a list (eligibles) containing the identification numbers of the members of this population, and these cells are affected by the expression of proteins whose labels are included in the second list (switches). Both lists are used to recognise the corresponding neighbours in each case and to support the options that may emerge from their potential interactions.Specific interactionsReceptor crosstalk establishes an activation mechanism between B-cell and T-cell populations, representing a stimulatory interaction. Lymphocytes themselves act as efficient antigens since they contain a large number of specific receptors on their surface, allowing them to crosstalk with other lymphocytes. In relation to lymphocytes with mutually complementary receptors, they can be expected to stimulate each other to proliferate or to secrete specific molecules.<a class="elsevierStyleCrossRef" href="#bib0180">36</a>HPV16-ALIFE produces cell activation of specific populations based on this concept. A cell that complements another cell induces a modification to the state of its cognate receptors. When the cell results from a process of differentiation or proliferation, its receptors are in a zero (0) state, which denotes inactivity. Once the cell is activated through various processes, its state changes to state one (1); when a successful interaction is generated, the respective receptors and molecules change to state two (2). In addition, the variables defined in the model's cell populations preserve traceability, which enables the types of cells and receptors to be evidenced that are coupled because of their interactions. This is what results from the interaction processes between DCs and antigens, DCs and antigen-specific B-cells, T-cells, and antigen-specific B-cells, among other signal exchange actions implemented in this model as detailed below.With respect to inhibitory or blocking activity, positive components block negative receptors and vice versa. This behaviour can be seen during the expression of pro-inflammatory and/or anti-inflammatory cytokines. In HPV16-ALIFE, when the expression of a cytokine is active, its state will be identified with a value equal to one (1) and, conversely, when its expression is inactivated or blocked by some element of the environment, its state will change to zero (0). Regarding death or elimination activities, cell populations will respond to programmed cell death processes. These processes are triggered as a condition of natural death or death by apoptosis, which will depend on the microenvironment events resulting from interactions between cell populations, types of cell populations affected, and state of some specific markers.Regarding the HPV16 life cycle within the model, the rules are dictated by the functions associated with the early and late proteins expressed by the virus (E1, E2, E4, E5, E6, E7, L1 and L2). This process, and those associated with their states, transitions, and interactions, are extensively documented in our previous publication.<a class="elsevierStyleCrossRef" href="#bib0225">45</a>ConclusionsThis work enabled us to establish a biological knowledge base, sufficient to define a conceptual design, determine the general logic, and produce the implementation dynamics of the HPV16-ALIFE artificial life model. All these components are fundamental inputs for the construction of a functional prototype. This prototype operates as a virtual laboratory through which we can observe the functionality of the model. Due to its length, the HPV16-ALIFE prototype is presented and evaluated in a separate article.Compliance with ethical standardsThis article meets the applicable ethical standards.Conflict of interestsThe authors have no conflict of interests to declare." "textoCompletoSecciones" => array:1 [ "secciones" => array:10 [ 0 => array:3 [ "identificador" => "xres1716223" "titulo" => "Abstract" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Objective" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Materials and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Conclusions" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1516881" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres1716224" "titulo" => "Resumen" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "abst0020" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0025" "titulo" => "Materiales y métodos" ] 2 => array:2 [ "identificador" => "abst0030" "titulo" => "Conclusiones" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec1516880" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:3 [ "identificador" => "sec0010" "titulo" => "Materials and methods" "secciones" => array:16 [ 0 => array:2 [ "identificador" => "sec0015" "titulo" => "Background supporting the HPV16-ALIFE model" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Method" ] 2 => array:2 [ "identificador" => "sec0025" "titulo" => "Methodological contribution" ] 3 => array:2 [ "identificador" => "sec0030" "titulo" => "Conceptual description of the HPV16-ALIFE model" ] 4 => array:2 [ "identificador" => "sec0035" "titulo" => "Cellular microenvironment (Nivel-1)" ] 5 => array:2 [ "identificador" => "sec0040" "titulo" => "Toll-like receptor microenvironment (Nivel-2)" ] 6 => array:2 [ "identificador" => "sec0045" "titulo" => "Cytokine microenvironment (Level-3)" ] 7 => array:2 [ "identificador" => "sec0050" "titulo" => "Therapeutic vaccine monitoring" ] 8 => array:2 [ "identificador" => "sec0055" "titulo" => "Description of the dynamics implemented in the HPV16-ALIFE model" ] 9 => array:2 [ "identificador" => "sec0060" "titulo" => "General parameters" ] 10 => array:2 [ "identificador" => "sec0065" "titulo" => "Checkpoints" ] 11 => array:2 [ "identificador" => "sec0070" "titulo" => "Rules" ] 12 => array:2 [ "identificador" => "sec0075" "titulo" => "States" ] 13 => array:2 [ "identificador" => "sec0080" "titulo" => "Transitions" ] 14 => array:2 [ "identificador" => "sec0085" "titulo" => "Interactions" ] 15 => array:2 [ "identificador" => "sec0090" "titulo" => "Specific interactions" ] ] ] 6 => array:2 [ "identificador" => "sec0095" "titulo" => "Conclusions" ] 7 => array:2 [ "identificador" => "sec0100" "titulo" => "Compliance with ethical standards" ] 8 => array:2 [ "identificador" => "sec0105" "titulo" => "Conflict of interests" ] 9 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2020-11-17" "fechaAceptado" => "2021-08-06" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1516881" "palabras" => array:6 [ 0 => "Artificial life" 1 => "Artificial immune system" 2 => "Cervical cancer" 3 => "Hpv16" 4 => "Simulation model" 5 => "Therapeutic vaccine" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec1516880" "palabras" => array:6 [ 0 => "Vida artificial" 1 => "Sistema inmune artificial" 2 => "Cáncer cérvix" 3 => "Hpv16" 4 => "Modelo simulación" 5 => "Vacuna terapéutica" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:3 [ "titulo" => "Abstract" "resumen" => "ObjectiveTo present an artificial life model that simulates behaviours based on interactions that emerge between the human immune system, the life cycle of human papillomavirus type 16, some types of therapeutic virus type 16, and some types of therapeutic vaccines. Materials and methodsUsing the approach of complex systems theories and biological systems, we created an artificial life model that allowed us to simulate virtual patient immune systems that develop infectious viral processes. These patients receive a therapeutic vaccine capable of releasing the necessary doses autonomously, once the model establishes that the immune system has outgrown its ability to counteract lesions caused by persistent viral infection. ConclusionsThe methodology developed provides independence to the modelling of each of the components that make up each of the components that constitute the three proposed domains and reflects the dynamics that emerge from their interactions, thus ensuring the corresponding feedback processes. With this work, a knowledge base is established that allows us to define the conceptual design, determine the general logic, and produce the dynamics of implementation of the HPV16-ALIFE model." "secciones" => array:3 [ 0 => array:2 [ "identificador" => "abst0005" "titulo" => "Objective" ] 1 => array:2 [ "identificador" => "abst0010" "titulo" => "Materials and methods" ] 2 => array:2 [ "identificador" => "abst0015" "titulo" => "Conclusions" ] ] ] "es" => array:3 [ "titulo" => "Resumen" "resumen" => "ObjetivoPresentar un modelo de vida artificial que simula comportamientos basados en interacciones que emergen entre el sistema inmune humano, ciclo de vida del virus de papiloma humano tipo 16, y algunos tipos de vacunas terapéuticas. Materiales y métodosBajo el enfoque de teorías de sistemas complejos y sistemas biológicos, creamos un modelo de vida artificial que nos permite simular sistemas inmunes de pacientes virtuales que desarrollan procesos virales infecciosos. Estos pacientes reciben una vacuna terapéutica capaz de liberar las dosis necesarias de forma autónoma, una vez el modelo establece que el sistema inmune ha superado su capacidad de contrarrestar lesiones causadas por la infección viral persistente. ConclusionesLa metodología desarrollada proporciona independencia al modelamiento de cada uno de los componentes que integran los tres dominios propuestos y refleja la dinámica que surge a partir de sus interacciones, garantizando de esta forma los procesos de retroalimentación correspondientes. Con este trabajo se establece una base de conocimiento que nos permite definir el diseño conceptual, determinar la lógica general, y producir la dinámica de implementación del modelo HPV16-ALIFE." "secciones" => array:3 [ 0 => array:2 [ "identificador" => "abst0020" "titulo" => "Objetivo" ] 1 => array:2 [ "identificador" => "abst0025" "titulo" => "Materiales y métodos" ] 2 => array:2 [ "identificador" => "abst0030" "titulo" => "Conclusiones" ] ] ] ] "NotaPie" => array:1 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "Please cite this article as: Escobar Ospina ME. Simulador de interacciones del sistema inmune humano: descripción del modelo para el ciclo de vida del virus de papiloma humano tipo 16 y vacunas terapéuticas. Vacunas. 2022;23:1–16." ] ] "multimedia" => array:6 [ 0 => array:8 [ "identificador" => "fig0005" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 2132 "Ancho" => 2833 "Tamanyo" => 493262 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0005" "detalle" => "Fig. " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "General design of the HPV16-ALIFE model. This figure depicts some of the actions that arise between the host (left-side) and virus (right-side) domains. The vertical interactions that occur between the two domains are organised into three different levels: Level-1, Level-2, and Level-3, represented as green rectangles, which segment the Figure into three areas. Within each level a specific microenvironment is simulated, represented by hexagons, which correspond to each of the microenvironments: cellular, TLRs and cytokines, respectively, when viewed from top to bottom. The horizontal interactions that emerge between host and pathogen, within each level are also depicted. The downward-pointing arrows (red) represent downstream signalling processes. The upward pointing arrows (blue) represent upstream signalling processes. The syringes shown at each level represent the nanodevice that simulates therapeutic vaccines, with or without adjuvant (vaccine domain), which enter to modify the immune system responses to each microenvironment once delivered to the virtual patient. Additional details to each level in the text." ] ] 1 => array:8 [ "identificador" => "fig0010" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 2111 "Ancho" => 2833 "Tamanyo" => 592268 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0010" "detalle" => "Fig. " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "Cell differentiation process in the HPV16-ALIFE model. Activity starts from a haematopoietic stem cell (HSC) (follow the Figure from left to right), which can diverge into a common progenitor, either myeloid or lymphoid. From a myeloid progenitor, the cell can differentiate into populations of macrophages or DCs. From a lymphoid progenitor, the cell can differentiate into a B-cell lineage or T/NK cell progenitor. For the macrophage population, the cell can differentiate between M1-type macrophages and M2-type macrophages. With respect to the DC population, the cell can differentiate into cDC, pDC, mDC, and LC. In relation to the B-cell lineage, the cell can differentiate into memory B-cells and plasma cells, both populations capable of producing antibodies. From a T/NK cell progenitor, the cell can diverge into a T-cell lineage or an NK cell lineage per se. From the T-cell lineage, a cell can differentiate into effector T-cells or memory T-cells. Effector T-cells can differentiate into CD8 T-cells and CD4 T-cells. In turn, CD8 T-cells can differentiate into CTLs. In relation to CD4 T-cells, a cell can differentiate into Th1 helper T-cells, Th2 helper T-cells, and type 3 helper T-cells (Th3). In reality, the Th3 population comprises several types of T-helper cells that, due to a characteristic of plasticity, one group of cells can become another. Within the Th3 population, T-cells can differentiate into type-9 (Th9), type-17 (Th17), type-22 (Th22), regulatory T-cells (Treg), and follicular helper T-cells (Tfh). In turn, memory T-cells can diverge into CD4 memory T-cells or CD8 memory T-cells. As for CD4 memory T-cells, a cell can differentiate into Tcm. With respect to CD8 memory T-cells, a cell can differentiate into Tcm, Tem, Trm, and Tsm. The shaded areas in the Figure correspond to the range established for each of the cell populations incorporated in this model. In the figure, the yellow rectangles represent master regulators of cell differentiation; the green rectangles represent cytokines secreted by each cell group, and the fuchsia rectangles represent surface molecules expressed by each cell population." ] ] 2 => array:8 [ "identificador" => "fig0015" "etiqueta" => "Fig. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1667 "Ancho" => 2833 "Tamanyo" => 522362 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0015" "detalle" => "Fig. " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "Symbols that represent cell populations in the HPV16-ALIFE model. Each symbol represents a cell population within HPV16-ALIFE. Bcell: B-cell lineage; gcB cell: germinal centre B-cell; slPC: short-lived plasma cell; slmBC: short-lived memory B-cell; llPC: long-lived plasma cell; llmBC: long-lived memory B-cell; Antibody; pDC: dendritic cell; Tcell: T-cell lineage; NK: Natural Killer cell lineage; TCD4: new CD4 T-cell; TCD8: new CD8 T-cell; CTL: cytotoxic T-lymphocyte; mTCD8: memory CD8 T-cell; KC: keratinocyte; FDC: follicular dendritic cell. Th1: T-helper cell type-1; Th2: T-helper cell type-2; Tfh: follicular T-helper cell; Th9: T-helper cell type-9; Th17: T-helper cell type-17; Th22: T-helper cell type-22; Treg: regulatory T-cell; mfm1: M1-type macrophage." ] ] 3 => array:8 [ "identificador" => "fig0020" "etiqueta" => "Fig. 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 1868 "Ancho" => 2833 "Tamanyo" => 591543 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0020" "detalle" => "Fig. " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "TLR signalling process in the HPV16-ALIFE model. In an abstraction from reality, this figure represents cytoplasm, endocytosis area, endosomal membrane, and cell nucleus. Some TLRs are expressed on the cell surface and others intracellularly. This Figure shows each of the currently reported TLRs. Next to each TLR is the year it was identified, the molecular patterns associated with the pathogens it recognises, and the signalling that mediates its activation. TLRs have a common intracellular domain called TIR (Toll-IL-1R). TLRs are activated by different ligands, which localise to different types of organisms and structures and have adapters that allow them to respond to activation processes. Different adaptors couple to different receptors, and once this coupling is activated it determines the signalling pathway that will be triggered. The characterisation of TIR domain-containing adaptors has established essential roles in TLR signalling pathways. The antiviral TLRs are TLR3, TLR7, TLR8, and TLR9.<a class="elsevierStyleCrossRefs" href="#bib0130">26–29</a> The most complex signalling scenario is TLR4, which can activate two pathways, one reported on the cell surface and the other in the area of endocytosis. Once TLRs are activated through binding to a ligand, a cascade of intracellular kinases is triggered by intermediate adaptor molecules. Depending on their nature, these adaptors recruit and activate certain complexes such as IRAKs, TBKs, IKKs, some ubiquitin ligases (TRAF6, TRAF3, and Pellino-1), and then conclude in coupling the NF-kB, IFN type I, MAPK-p38, and MAPK-JNK JNK<a class="elsevierStyleCrossRefs" href="#bib0135">27,28,30</a> pathways." ] ] 4 => array:8 [ "identificador" => "fig0025" "etiqueta" => "Fig. 5" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr5.jpeg" "Alto" => 2229 "Ancho" => 2833 "Tamanyo" => 762388 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0025" "detalle" => "Fig. " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "Interleukin families and cancer. Interleukins are classified into superfamilies. Each superfamily brings together several interleukins that have one or more common characteristics. Currently, more than 38 cytokines have been identified, although only a few can be considered anti-cancer agents, some others are referred to as pro-cancer agents, and there are others that show both pro- and anti-carcinogenic activities. Therefore, the impact of cytokines on cancer is ambiguous. There is also a group of ILs that have been little investigated in cancer. In the context of cancer, this Figure classifies ILs according to the colour represented by their members, as follows: green, corresponding to ILs with an anti-cancer profile; yellow, corresponding to ILs with a pro-cancer profile; purple, corresponding to ILs with both pro- and anti-cancer activities; and red, corresponding to ILs that so far have not been associated with cancer." ] ] 5 => array:8 [ "identificador" => "fig0030" "etiqueta" => "Fig. 6" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr6.jpeg" "Alto" => 1604 "Ancho" => 2833 "Tamanyo" => 349312 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0030" "detalle" => "Fig. " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "Global parameters of the HPV16-ALIFE prototype. This Figure shows the components that allow the initial values of the model to be modified before activating the simulation. These components include: (A) Initial state of viral proteins and activity state of TLR ligands; (B) Initial parameters of therapeutic vaccine under evaluation; (C) Components associated with TLRs (left-side) and cytokines (right-side) signalling pathways. These components represent part of the GUI incorporated into the developed prototype. These parameters can also be modified at runtime." ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:45 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:1 [ "referenciaCompleta" => "Bruni L, Barrionuevo-Rosas L, Albero G, Aldea M, Serrano B, Valencia S, et al. ICO Information Centre on HPV and Cancer (HPV Information Centre). Human papillomavirus and related diseases in the world. Summary report. 2015 [accessed 23 Dec 2015]. 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Simulator of interactions of the human immune system: Description of the model for the life cycle of human papillomavirus type 16 and therapeutic vaccines

Simulador de interacciones del sistema inmune humano: descripción del modelo para el ciclo de vida del virus de papiloma humano tipo 16 y vacunas terapéuticas

M.E. Escobar Ospina

Departamento de Ingeniería – Sistemas y Computación, Universidad Nacional, Bogotá, Colombia

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      "titulo" => "Cost-minimization and opportunity cost analysis of fully-liquid hexavalent and meningococcal vaccines in Spain"
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            "en" => "<p id="sp0020" class="elsevierStyleSimplePara elsevierViewall">Total direct cost &#40;euros&#41; per 100&#46;000 inhabitants associated to administration of fully and non-fully liquid vaccines&#46;</p>"
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          "autoresLista" => "Inmaculada Cuesta, David Carcedo, Mar&#237;a Jos&#233; Menor, Georgina Drago, Escolano Manuel, Juan Luis L&#243;pez-Belmonte, Sonia L&#243;pez, Hosanna Parra, Agust&#237;n Rivero, Sonia Tamames"
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    "titulo" => "Simulator of interactions of the human immune system&#58; Description of the model for the life cycle of human papillomavirus type 16 and therapeutic vaccines"
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          "en" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Global parameters of the HPV16-ALIFE prototype&#46; This Figure shows the components that allow the initial values of the model to be modified before activating the simulation&#46; These components include&#58; &#40;A&#41; Initial state of viral proteins and activity state of TLR ligands&#59; &#40;B&#41; Initial parameters of therapeutic vaccine under evaluation&#59; &#40;C&#41; Components associated with TLRs &#40;left-side&#41; and cytokines &#40;right-side&#41; signalling pathways&#46; These components represent part of the GUI incorporated into the developed prototype&#46; These parameters can also be modified at runtime&#46;</p>"
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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Diseases caused by persistent infectious processes originating from the human papillomavirus type 16 &#40;HPV16&#41; still pose a great challenge to the global population&#44; despite major advances in prevention&#44; monitoring and control&#46; This virus&#44; considered an aetiological agent of several types of cancer &#40;cervical&#44; anal&#44; oropharyngeal&#44; vaginal&#44; penile&#41;&#44;<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> together with the complexity of the human immune system&#44; and certain control strategies designed through the development of immunisation therapies&#44; are the source of information for the model we present here&#46;</p><p id="par0010" class="elsevierStylePara elsevierViewall">The purpose of our work is to contribute to research and development of therapeutic vaccines aimed at controlling the diseases caused by this virus through the construction of an artificial life model&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">Because immune system responses may differ among various HPV16-infected individuals&#44; broad therapeutic vaccination could induce different effects in each treated patient&#46; In this context&#44; we believe that our work significantly contributes to healthcare informatics&#44; as with our model we propose a futuristic vision of a specific&#44; personalised&#44; and autonomous therapeutic vaccination&#44; which is currently within a virtual environment&#46; This new approach implies that therapeutic vaccines implanted into the host can regulate the cancerous environment generated by the viral infection by means of an autonomous immunisation process&#46; This autonomy within the model allows it to regulate and release&#44; by itself&#44; the number of doses needed by the host&#44; starting at the appropriate time and according to the degree of progression that existing lesions show through the established biomarkers&#44; which update their state depending on the evolution and conditions of their environment&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">Materials and methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Background supporting the HPV16-ALIFE model</span><p id="par0020" class="elsevierStylePara elsevierViewall">This model is conceptually based on complex systems theory and biological systems theory&#46; Its conception is supported by both theories&#44; primarily because the human immune system is considered a complex system&#44;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a> and its essential function is realised when it activates its defence mechanisms in the host to counteract the effects of various infectious agents&#44; including viruses&#46;</p><p id="par0025" class="elsevierStylePara elsevierViewall">The two underlying theories&#44; the reasons why the characterisation of the immune system recognises commonalities with both theories&#44; and the biological background supporting the development of this model&#44; are summarised in our previous publication&#46;<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> This background includes characterisations of the virus and the human host immune system&#44; the elements that comprise them and the interactions between them&#44; considering how the processes evolve under conditions of natural infection and persistence&#44; and their eventual regression and progression to cancer&#46; We also studied the conceptual development of these relationships influenced by therapeutic vaccines&#46; In relation to the simulated immune system &#40;SIS&#41; and in relation to the theory of complex systems&#44; our model highlights adaptive&#44; evolutionary&#44; and emergent behaviour&#59; and in relation to the theory of biological systems&#44; we emphasise the integration of experiments&#44; the use of computation&#44; and the development of methods to analyse biological information&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">We present this work in two articles&#44; due to its length&#46; The first aims to describe the conceptualisation and creation process of the artificial life model that we have called HPV16-ALIFE&#46; The second presents what we observed from tests conducted on the prototype&#44; through which we have demonstrated the operability of the model&#46;</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Method</span><p id="par0035" class="elsevierStylePara elsevierViewall">This work was developed in four stages&#46; The preliminary stage was the compilation of updated biological background information based on the scientific literature&#46;<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> The second stage involved the construction of a conceptual design reflecting three different domains&#44; which interact within a single model&#44; capable of reproducing the biological phenomena involved and feeding back on its own&#46; The third stage was to construct the functional prototype to be used as a virtual laboratory&#46; The fourth stage involved defining the experimental design and performing virtual tests&#46; This last stage will make it possible to observe behaviour and obtain results for subsequent analysis and comparison with real-world clinical data&#46;</p><p id="par0040" class="elsevierStylePara elsevierViewall">This model considers the virtual therapeutic vaccine a nanodevice that is implanted in the simulated patient&#44; capable of modulating and controlling the release of each dose&#44; depending on environmental conditions and the activity of certain biomarkers that warn of the presence of lesions caused by persistence of the infection&#46; Since this is an artificial life model&#44; the construction of the nanodevice as such is beyond the scope of the present work&#46; However&#44; the conceptual development of the vaccine is inspired by personalised cervical cancer therapy based on autologous E6&#47;E7 antigen-loaded dendritic cells &#40;DC&#41; with a nanodevice delivery system&#46; The DC vaccine has been previously tested on animal models&#46;<a class="elsevierStyleCrossRefs" href="#bib0020"><span class="elsevierStyleSup">4&#8211;8</span></a> The nanodevice was conceived based on several ideas and concepts that other authors are exploring&#44; through in-vitro experiments&#46; These concepts include the application of&#58; &#40;i&#41; nanodevices&#44; also termed nanomachines&#44;<a class="elsevierStyleCrossRefs" href="#bib0045"><span class="elsevierStyleSup">9&#44;10</span></a> or nanorobots&#44;<a class="elsevierStyleCrossRefs" href="#bib0050"><span class="elsevierStyleSup">10&#44;11</span></a> which have controllable locomotion and release of cargo capabilities<a class="elsevierStyleCrossRefs" href="#bib0055"><span class="elsevierStyleSup">11&#44;12</span></a>&#59; and &#40;ii&#41; nanovaccines<a class="elsevierStyleCrossRefs" href="#bib0065"><span class="elsevierStyleSup">13&#44;14</span></a> aimed at cancer immunotherapy&#46;<a class="elsevierStyleCrossRefs" href="#bib0075"><span class="elsevierStyleSup">15&#44;16</span></a> These nanoparticle- and nanomaterial-based cancer vaccines<a class="elsevierStyleCrossRefs" href="#bib0060"><span class="elsevierStyleSup">12&#44;17</span></a> target both tumour-specific antigens and adjuvants for targeted delivery to antigen-presenting cells&#46;<a class="elsevierStyleCrossRefs" href="#bib0065"><span class="elsevierStyleSup">13&#44;18</span></a> It is important to clarify that the nanodevice as such only exists in the HPV16-ALIFE model&#44; and its use in the physical world refers to a future scenario&#46;</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Methodological contribution</span><p id="par0045" class="elsevierStylePara elsevierViewall">This model simulates the responses of the innate and adaptive immune system of the host&#44; and the following are its main components&#58; &#40;i&#41; more than 24 different types of cell populations&#44; of both myeloid and lymphoid origin&#44; including their master regulators&#44; markers and surface molecules&#59; &#40;ii&#41; five different types of toll-like receptors &#40;TLRs&#41; with their corresponding ligands and signalling pathways&#59; and &#40;iii&#41; five different cytokine families involving more than 42 of their members&#44; including their particular functions and biological interaction profiles&#46; With respect to the pathogen&#44; this model simulates the HPV16 life cycle&#44; covering the different phases involving the processes of replication&#44; viral transcription&#44; growth&#44; capsid conformation&#44; and oncogenic transformation&#44; considering the activity of the associated viral proteins &#40;early and late&#41;&#46; With respect to host-pathogen interaction&#44; this model simulates immune system responses during virus detection&#44; as well as the changes that emerge as long as the infection persists&#46; Similarly&#44; HPV16-ALIFE simulates the application of some therapeutic vaccines that focus on mitigating or curing lesions caused by HPV16 viral infection&#44; including low- and high-grade cervical intraepithelial neoplasia &#40;CIN&#41;&#44; as well as malignant&#44; pre-cancer and cancer conditions&#46; By including the application of a specific vaccine in the simulation&#44; this model allows visualisation of the changes resulting from the interaction between multiple related agents&#44; from the perspective of the host and the pathogen&#46; On this basis&#44; it is also possible to show the effects caused by the vaccine and predict optimal vaccination protocols&#46;</p><p id="par0050" class="elsevierStylePara elsevierViewall">The HPV16-ALIFE model is original in its design because it was conceived in three different levels&#44; which allow triggering of a downstream response process and an upstream feedback process&#44; involving both host and virus&#46; In addition&#44; this feedback process enables the model to be autonomous in releasing the doses of vaccine necessary to control a lesion&#44; consistent with the information provided by markers that vary according to the conditions of the simulated microenvironment&#46;</p><p id="par0055" class="elsevierStylePara elsevierViewall">Current development focuses on HPV16&#44; but this model has the potential to study other cancers originating from different double-stranded DNA viruses&#44; such as EBV &#40;Epstein-Barr Virus&#41;&#44; HBV &#40;Hepatitis-B Virus&#41;&#44; and MCPyV &#40;Merkel Cell Polyomavirus&#41;&#44; among others&#46; However&#44; to simulate double-stranded DNA viruses other than HPV16&#44; it is necessary to adapt the domain corresponding to the life cycle of the new virus and to review the type of response in cell populations&#44; cytokines and TLRs&#46;</p><p id="par0060" class="elsevierStylePara elsevierViewall">In the context of HPV16 and HPV16-associated cancers&#44; our model can be extended to other studies to observe the behaviour of the human immune system in relation to the development of new drugs and different therapeutic vaccines with and without adjuvant&#46;</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Conceptual description of the HPV16-ALIFE model</span><p id="par0065" class="elsevierStylePara elsevierViewall">In HPV16-ALIFE the interacting components between host&#44; pathogen and vaccine are defined at three different levels&#46; Each of these levels represents an independent microenvironment&#46; However&#44; all three microenvironments interact with each other&#44; feeding back vertically and horizontally &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#41;&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0070" class="elsevierStylePara elsevierViewall">The horizontal interaction processes simulate the dynamics between the various components that are part of the same level&#46; For example&#44; interactions between cell cycle phases and the processes of cell differentiation&#44; proliferation&#44; and apoptosis &#40;CDPA&#41; are simulated at Level-1&#46; The vertical interaction processes simulate the dynamics emerging between the different proposed levels&#46; Initially&#44; actions are generated that induce activation of downstream signalling pathways&#44; which&#44; after updating each of the levels in this direction&#44; trigger upstream mobilisation processes&#46; For example&#44; in the case of downstream actions and depending on the cell populations that are differentiated &#40;Level-1&#41;&#44; the activation of certain TLR signalling pathways is induced &#40;Level-2&#41;&#46; In the same example&#44; in the case of upstream signalling&#44; high levels of E6 and E7 protein expression &#40;Level-2&#41; affect the behaviour of tumour suppressors p53 and pRB &#40;Level-1&#41;&#46;</p><p id="par0075" class="elsevierStylePara elsevierViewall">The processes that take place within the host &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a> - left-side&#41; are simulated in our model as follows&#46; CDPA processes take place at Level-1&#44; which induce the emergence of various cell populations &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>&#41;&#46; Signalling processes linked to the five different toll-like receptors &#40;TLR3&#44; TLR4&#44; TLR7&#44; TLR8&#44; and TLR9&#41; incorporated into the model are developed at Level-2 &#40;<a class="elsevierStyleCrossRef" href="#fig0020">Fig&#46; 4</a>&#41;&#46; Processes linked to the five currently recognised cytokine families&#44; including their corresponding members and receptors&#44; are developed at Level-3&#46; These families include TNF &#40;tumour necrosis factor&#41;&#44; TGF &#40;transforming growth factor&#41;&#44; IFNs &#40;interferons&#41;&#44; MIF &#40;macrophage migration inhibitory factor&#41;&#44; ILs &#40;interleukins&#41;&#46;</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0080" class="elsevierStylePara elsevierViewall">Processes considered to have their origin in the pathogen &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a> - right-side&#41; are simulated in our model as follows&#46; The different phases of the cell cycle &#40;G1&#44; S&#44; G2&#44; M&#44; G0&#41; are included at Level 1&#44; and the behaviour of tumour suppressors involved in viral-like responses &#40;p53&#44; pRB&#44; Tert and p21&#41; is followed&#46; The infectious process caused by HPV16 is developed at Level-2&#44; considering the different stages of the evolution of the virus&#46; This process may start from a condition of natural infection&#44; which may then progress to the different degrees of severity observed in CIN&#44; and eventually progress to more severe conditions&#46; Natural regression processes are also considered at this level&#44; where from stages of persistent infection&#44; neoplasia&#44; or pre-cancer&#44; and through the effects mediated by immune system responses&#44; these infectious conditions may be reversed and then driven towards a state of natural infection and subsequent viral clearance&#46; The different phases associated with the HPV16 life cycle are developed at Level-3&#44; and the behaviour of several of its early &#40;E1&#44; E2&#44; E4&#44; E5&#44; E6&#44; E7&#41; and late &#40;L1 and L2&#41; viral proteins continues in terms of their expression levels&#46;</p><p id="par0085" class="elsevierStylePara elsevierViewall">The complexities of vertical and horizontal interactions&#44; and the components and processes that arise between host and pathogen&#44; all simulated in HPV16-ALIFE&#44; are also shown in <a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a> and obey the following dynamics&#46; The model initiates its activity between host and virus&#44; establishing the interactions that arise by connecting cell cycle phases and CDPA processes &#40;Level-1-horizontal interaction&#44; downstream&#41;&#46; Depending on the cell populations that differentiate and proliferate&#44; their corresponding TLR signalling pathways are enabled &#40;Level-1 and Level-2-vertical interaction&#44; downstream&#41;&#46; Depending on the TLRs that receive a stimulus&#44; or because they encounter a ligand to which they are akin&#44; receptor-ligand binding may occur&#46; Successful binding induces the enabling of the linked cytokine populations &#40;Level-2 and Level-3-vertical interaction&#44; downstream&#41;&#44; which take place at the third level&#46; The different cytokines enabled at Level-3 are activated when they detect and bind to cognate receptors&#46; The cytokine population in an active state induces the onset of bottom-up dynamics&#46; In this context&#44; depending on which cytokines are activated&#44; the expression levels of some viral proteins can be affected &#40;Level-3-horizontal interaction&#44; upstream&#41;&#46; At Level-2&#44; the expression of viral oncoproteins &#40;especially E6 and E7&#41; is examined as they may affect the behaviour of some TLR signalling pathways &#40;Level-2-horizontal interaction&#44; upstream&#41;&#46; Considering the stage of simulation of the HPV16 infectious process and the expression levels of the viral oncoproteins&#44; the behaviour of the tumour suppressors is evaluated at Level-1&#44; as E6 and E7 proteins may affect their activity &#40;Level-1-vertical interaction&#44; upstream&#41;&#46;</p><p id="par0090" class="elsevierStylePara elsevierViewall">The dynamics described above guarantee the feedback of the model at any point in time&#46; Further details for each proposed level are presented in the sections below&#46;</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Cellular microenvironment &#40;Nivel-1&#41;</span><p id="par0095" class="elsevierStylePara elsevierViewall">The process of cell differentiation takes place at Level-1 of HPV16-ALIFE&#44; according to lineages that are derived from myeloid and lymphoid progenitors from a haematopoietic cell&#46; The cell differentiation process shown in <a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a> corresponds to the scope defined in this model&#46; All target cell populations generated by our model are related to&#58; &#40;i&#41; group of master regulators that allow their differentiation&#44; &#40;ii&#41; grouping of cytokines that they can secrete&#44; and &#40;iii&#41; set of surface molecules that they can express&#46; In addition to CDPA processes&#44; this first level also simulates the interactions that arise between the different incorporated cell populations&#46; We mention below some of these cellular interactions&#44; considered essential for the model&#44; and describe the scope that each cell population has within HPV16-ALIFE&#46;<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">&#40;i&#41;</span><p id="par0100" class="elsevierStylePara elsevierViewall">Macrophage population and T-cell lineage&#58; the M1-type macrophage population interacts with the T-helper 1 &#40;Th1&#41; population &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a> - connector 1&#41;&#46; The M2-type macrophage population interacts with the T-helper 2 &#40;Th2&#41; population &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a> - connector 2&#41;&#46;</p></li><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">&#40;ii&#41;</span><p id="par0105" class="elsevierStylePara elsevierViewall">Dendritic cell population and T-cell lineage&#58; in addition to the differentiation process of the DC population&#58; conventional &#40;cDC&#41;&#44; plasmacytoid &#40;pDC&#41;&#44; myeloid &#40;<span class="elsevierStyleBold">mDC</span>&#41;&#44; and Langerhans cells &#40;LC&#41;&#44; shown in <a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>&#44; the major histocompatibility complex &#40;MHC&#41; is incorporated&#44; inducing antigen-specific T-cell activation&#46; The latter population interacts with the CD4 effector T-cell set &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a> - connector 3&#41;&#46; Within the antigen-specific T-cell population&#44; this model considers a process of cell apoptosis capable of eliminating 90&#37; to 95&#37; of this population&#44; implying that only 5&#37;&#8211;10&#37; of these cells are actually interacting with the CD4 effector T-cell population&#46;</p></li><li class="elsevierStyleListItem" id="lsti0015"><span class="elsevierStyleLabel">&#40;iii&#41;</span><p id="par0110" class="elsevierStylePara elsevierViewall">B-cell lineage&#58; in addition to the CDPA processes of the B-cell lineage&#44; this model also incorporates certain procedures that are linked to cell division stages that allow differentiation of the isotypes associated with the antibody populations &#40;IgM&#44; IgG&#44; IgA&#44; IgG1&#44; IgG2&#44; IgG3&#44; IgG4&#41;&#46;</p></li><li class="elsevierStyleListItem" id="lsti0020"><span class="elsevierStyleLabel">&#40;iv&#41;</span><p id="par0115" class="elsevierStylePara elsevierViewall">Cytotoxic T-lymphocytes &#40;CTLs&#41;&#58; this population interacts with memory T-cell groups including central &#40;Tcm&#41;&#44; effector &#40;Tem&#41;&#44; tissue-resident &#40;Trm&#41;&#44; and stem cell &#40;Tsm&#41; T-cells &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a> - connector 4&#41;&#46;</p></li></ul></p><p id="par0120" class="elsevierStylePara elsevierViewall">Within the cell population-level interactions&#44; this model also simulates essential processes&#44; of which we highlight the following&#58; antigen-mediated activation&#44; cell proliferation&#44; extrafollicular antibody production&#44; germinal centre-independent humoral memory&#44; germinal centre dynamics &#40;including clonal expansion&#44; class switch recombination and somatic hypermutation&#41;&#44; cell apoptosis&#44; germinal centre-dependent humoral memory&#44; serological memory&#44; cell population shrinkage&#44; homeostatic proliferation&#44; and trained memory&#46;</p><p id="par0125" class="elsevierStylePara elsevierViewall">Three different layers are also incorporated as part of the cellular microenvironment &#40;Level-1&#41;&#44; labelled in HPV16-ALIFE as&#58; Layer-1&#44; Layer-2&#44; and Layer-3 &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#41;&#46; Layer-1 contains the set of master regulators &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a> - yellow rectangles&#41;&#44; which allow differentiation of each of the cell populations incorporated into the model&#46; Layer-2 contains the set of cytokines that each cell population is capable of secreting &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a> - green rectangles&#41;&#46; Layer-3 enables the set of specific surface molecules to be established for each of the cell populations involved in the model &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a> - fuchsia-rectangles&#41;&#46;</p><p id="par0130" class="elsevierStylePara elsevierViewall">The HPV16-ALIFE model uses two approaches to consider the cell division process&#58; stochastic division and asymmetric division&#46; Stochastic competition can explain how each cell fate can be reached by considering the distribution of a proportion of B-cells&#46;<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a> Asymmetric division represents an evolutionarily conserved mechanism that allows a single cell to produce two separate daughter cells&#44; which are differentially predestined from their origin&#46; Several classes of lymphocytes use asymmetric division as a strategy to generate cell diversity or regulate their functions&#46;<a class="elsevierStyleCrossRefs" href="#bib0100"><span class="elsevierStyleSup">20&#44;21</span></a></p><p id="par0135" class="elsevierStylePara elsevierViewall">The T-cell population is more notably complex&#44; particularly due to the plasticity associated with CD4&#8239;&#43;&#8239;T-cells&#46;<a class="elsevierStyleCrossRefs" href="#bib0110"><span class="elsevierStyleSup">22&#8211;25</span></a> Subgroups of cell populations that are derived from CD4&#8239;&#43;&#8239; T-cells are characterised by unique profiles that enable them to be differentiated in the cytokine and surface marker domains&#46; In HPV16-ALIFE this concept of plasticity is applied to processes in which new CD4&#8239;&#43; T-cell groups differentiate into different types of helper T-cells &#40;Th1&#44; Th2&#44; Th9&#44; Th17&#44; Th22&#44; Tfh&#41; and regulatory T-cells &#40;Treg&#41;&#46;</p><p id="par0140" class="elsevierStylePara elsevierViewall">All these elements that interact simultaneously allow us to model the CDPA processes&#44; which are simulated at Level-1 of the model&#46; To incorporate the abovementioned cell populations into the functional prototype&#44; each is associated with a symbol that represents them&#44; as shown in <a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a>&#46; Each symbol&#44; in turn&#44; corresponds to an agent within the model&#44; and each agent is linked to the characteristics of its population&#46; Then&#44; events specific to their microenvironments emerge because of the interactions arising between these agents over time&#46;</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Toll-like receptor microenvironment &#40;Nivel-2&#41;</span><p id="par0145" class="elsevierStylePara elsevierViewall">The signalling process shown in <a class="elsevierStyleCrossRef" href="#fig0020">Fig&#46; 4</a> occur at Level-2 of HPV16-ALIFE&#44;<a class="elsevierStyleCrossRefs" href="#bib0130"><span class="elsevierStyleSup">26&#8211;30</span></a> which corresponds to the TLR microenvironment&#46; Considering that HPV is a DNA virus&#44; this model considers the signalling pathways associated with toll-like receptors&#58; TLR3&#44; TLR4&#44; TLR7&#44; TLR8&#44; and TLR9&#44; given their link to viral-like responses&#46;</p><elsevierMultimedia ident="fig0020"></elsevierMultimedia><p id="par0150" class="elsevierStylePara elsevierViewall">TLRs bind to and are activated by different ligands&#44; which localise in different types of organisms and structures&#46; In turn&#44; these bindings allow them to capture certain adaptors that allow them to respond to activation processes&#46; Different adaptors couple to different receptors&#44; and it is the adaptor used that determines which signalling pathway will be activated&#46; Characterised TIR &#40;Toll-interleukin-1 receptor&#41; domain-containing adaptors&#44; such as&#58; MyD88 &#40;Myeloid differentiation factor 88&#41;&#44; TIRAP &#40;MAL&#41; &#40;Toll&#47;interleukin-1 receptor domain-containing adapter protein&#41;&#44; TRIF &#40;TICAM1&#41; &#40;TIR domain-containing adapter molecule 1&#41;&#44; TRAM &#40;TICAM2&#41; &#40;TIR domain-containing adapter molecule 2&#41;&#44; SARM1 &#40;Sterile alpha and TIR motif-containing protein 1&#41;&#44; BCAP &#40;B-cell adapter for phosphoinositide 3-kinase&#41;&#44; have established essential roles over the TLR signalling pathways&#46;<a class="elsevierStyleCrossRefs" href="#bib0135"><span class="elsevierStyleSup">27&#44;31&#8211;33</span></a></p><p id="par0155" class="elsevierStylePara elsevierViewall">The signalling pathways used to differentiate TLRs allow different cellular responses to be triggered&#44; and their activation depends on various adapters that are activated downstream from the different pattern recognition receptors &#40;PRRs&#41;<a class="elsevierStyleCrossRefs" href="#bib0155"><span class="elsevierStyleSup">31&#44;33</span></a> &#40;<a class="elsevierStyleCrossRef" href="#fig0020">Fig&#46; 4</a>&#41;&#46; These pathways prove to be fundamental in the orchestration of innate and adaptive immune responses during the processes of inflammation and repair of host tissues&#46;<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">26</span></a></p><p id="par0160" class="elsevierStylePara elsevierViewall">Based on the above&#44; the HPV16-ALIFE model incorporates the characterisation of toll-like receptors&#44; specifically TLR3&#44; TLR4&#44; TLR7&#44; TLR8&#44; and TLR9&#44; considering their adaptor families&#44; signalling cascades&#44; and their various components&#46;</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Cytokine microenvironment &#40;Level-3&#41;</span><p id="par0165" class="elsevierStylePara elsevierViewall">Processes linked to cytokine populations are simulated in Level-3 of HPV16-ALIFE&#44; where it is sought to represent their microenvironment&#46; Cytokines induce their responses by binding to cell surface receptors&#44; with specific high affinity on target cells&#44; resulting in the initiation of a series of intracellular signal transduction pathways&#46; The receptors of various cytokines and growth factors are homologous within their extracellular domains&#44; and therefore they can exert their effects through common signal transduction pathways&#46; Several cytokines exhibit a redundancy in function and share overlapping properties&#44; and certain subunits of their cell surface receptors&#46;<a class="elsevierStyleCrossRef" href="#bib0170"><span class="elsevierStyleSup">34</span></a> The effects cytokines produce depend on the release time&#44; the environment in which they interact&#44; the neighbours with whom they compete or synergise&#44; the density of the receptor&#44; and the tissue response to each cytokine&#46; In addition&#44; cytokines play a dual role&#44; sometimes producing anti-inflammatory effects and sometimes pro-inflammatory actions&#44; according to the conditions of their microenvironment&#46; All these events allow cytokines to generate a complex network of interactions comprising sophisticated interdependent feedback mechanisms&#44; thus helping to generate effective immune responses&#46;<a class="elsevierStyleCrossRefs" href="#bib0170"><span class="elsevierStyleSup">34&#44;35</span></a></p><p id="par0170" class="elsevierStylePara elsevierViewall">HPV16-ALIFE includes most of the cytokines related to human immune system responses&#44; on which there is scientific documentation published up to and including 2019&#46; The developed prototype incorporates&#58; TNF family that includes two members &#40;TNF-&#945;&#44; TNF-&#946;&#41;&#44; TGF family including two members &#40;TGF-&#945;&#44; TGF-&#946;&#41;&#44; TGF family that includes four members &#40;IFN-&#945;&#44; IFN-&#946;&#44; IFN-&#947;&#44; IFN-&#955;&#41;&#44; MIF&#44; and ILs family including 40 members &#40;<a class="elsevierStyleCrossRef" href="#fig0025">Fig&#46; 5</a>&#41;&#46;</p><elsevierMultimedia ident="fig0025"></elsevierMultimedia><p id="par0175" class="elsevierStylePara elsevierViewall">Depending on the cell populations that differentiate or proliferate in the cellular microenvironment &#40;Level-1&#41;&#44; certain types of cytokines are secreted that report an &#8220;available&#8221; state in their corresponding microenvironment &#40;Level-3&#41;&#46; When some of the related receptors are found in the neighbourhood and cytokine-receptor binding occurs&#44; the cytokines switch to an &#8220;active&#8221; state&#46; When cytokines are reported to be in an active state&#44; other interactions occur simultaneously with the other components in their environment&#46; This exchange of signals between components can induce modifications to the cytokines&#39; own current state&#44; either to increase&#44; decrease&#44; or block their secretion levels&#46; Based on this&#44; the behaviour of other cell populations can be affected&#44; and thus generate dynamics that produce changes in their microenvironment&#46;</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Therapeutic vaccine monitoring</span><p id="par0180" class="elsevierStylePara elsevierViewall">HPV16-ALIFE considers vaccine control an element of the external perspective of this model&#46; The end-user defines the therapeutic vaccine type&#44; load&#44; dose&#44; and frequency to be evaluated against the microenvironments that are integrated in the model&#46; In the current version of the developed prototype&#44; therapeutic vaccines can vary between autologous DC vaccines loaded with E6 and E7 antigens&#44; and vaccines that target immune regulatory pathways&#44; particularly PD-1 &#40;anti-PD1&#41; molecules &#40;<a class="elsevierStyleCrossRef" href="#fig0030">Fig&#46; 6</a> - option B&#41;&#46; The user can also establish whether to test the vaccine with or without adjuvant&#46; If an adjuvant is selected&#44; this component can be chosen from a drop-down list that shows the cytokines that are incorporated into the model&#44; and another list that allows components to be selected that are part of the TLR signalling pathways&#46; In addition to selecting these data&#44; the user establishes whether to activate or block the component&#46; For this purpose&#44; our prototype incorporates the appropriate components in its graphical user interface &#40;GUI&#41; to facilitate the execution of these activities &#40;<a class="elsevierStyleCrossRef" href="#fig0030">Fig&#46; 6</a> - option C&#41;&#46;</p><elsevierMultimedia ident="fig0030"></elsevierMultimedia><p id="par0185" class="elsevierStylePara elsevierViewall">In line with the evolution in the simulated microenvironments&#44; this model can predict the most appropriate time to release a dose of the pre-programmed therapeutic vaccine&#46; When the model demands a vaccine dose&#44; it reports the number of the specific week in which it simulates its delivery to the virtual host&#46; At the end of the simulation&#44; the amount can be established of vaccine doses that were actually used by the model to control the cancer cell population&#46;</p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Description of the dynamics implemented in the HPV16-ALIFE model</span><p id="par0190" class="elsevierStylePara elsevierViewall">As in the real world&#44; experiments simulated with this model can induce scenarios of spontaneous regression and&#47;or progression to cancer&#46; Under spontaneous regression scenarios&#44; the virtual host detects key components in its environment that allow it to release the infection by activating defence mechanisms specific to the SIS&#46; Under progression scenarios&#44; the host initially detects a state of natural infection that then evolves&#44; following the process of the productive viral cycle&#44; through which different low-grade and high-grade CIN lesions &#40;CIN1&#44; CIN2&#44; CIN3&#41; may develop&#46; Depending on the setting&#44; such lesions may eventually progress to more complex conditions&#44; such as pre-cancer and cancer&#46; On detection of a lesion caused by HPV16&#44; this model can activate key elements of the innate immune system&#44; acting as a first line of defence&#59; and with persistence of infection&#44; it can also trigger components linked to the adaptive immune system&#46; Then&#44; through aids provided by simulated immunotherapies&#44; some components of the SIS are also stimulated and may modify their responses to achieve the targets driven by each therapeutic vaccine&#44; in an attempt to affect the population of HPV16-infected cells&#46;</p><p id="par0195" class="elsevierStylePara elsevierViewall">The description of the dynamics implemented in the HPV16-ALIFE model includes the definition of checkpoints&#44; rules&#44; states&#44; transitions&#44; and interactions&#44; which are detailed below and summarised in <a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#46;</p></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">General parameters</span><p id="par0200" class="elsevierStylePara elsevierViewall">We can define the general criteria of the HPV16-ALIFE model through three types of tools incorporated onto the developed GUI prototype&#46; These tool types include switches&#44; sliders&#44; and selectors&#46; This section explains each of the general parameters that the user can vary&#44; either before starting a simulation or in the runtime &#40;<a class="elsevierStyleCrossRef" href="#fig0030">Fig&#46; 6</a>&#41;&#46; Modifying any general parameter can affect the micro-environments in which the different components of the model evolve&#46; Such a situation can occur by varying the initial state of any of the general parameters related to infection&#44; viral proteins&#44; TLRs&#44; starting population&#44; vaccines&#44; cytokines&#44; and members of the TLR signalling pathway&#46; Each of these parameters is described below in literals&#58; &#40;a1&#41;&#44; &#40;a2&#41;&#44; &#40;a3&#41;&#44; &#40;b1&#41;&#44; &#40;b2&#41;&#44; &#40;c1&#41;&#44; and &#40;c2&#41;&#44; respectively&#46; Initially&#44; the parameters affect the processes at certain levels&#46; For example&#44; the initial population parameter impacts the microenvironment where the CDPA process takes place&#59; i&#46;e&#46;&#44; Level-1 of the model is affected&#46; However&#44; downstream&#44; and upstream feedback processes induce changes in the environment&#44; and therefore each modification in a general parameter will affect the entire dynamics of the model&#46;</p><p id="par0205" class="elsevierStylePara elsevierViewall">&#40;a&#41; Switches&#58; some switches are incorporated onto the GUI of the developed prototype that allow an initial state of activity or inactivity to be specified&#44; which are associated with certain criteria that are defined when the simulation process starts&#46; However&#44; the states of these switches may change because of actions generated between the model&#8217;s three levels&#44; i&#46;e&#46;&#44; interactions between host&#44; virus&#44; and vaccine control &#40;<a class="elsevierStyleCrossRef" href="#fig0030">Fig&#46; 6</a> - option A&#41;&#46;</p><p id="par0210" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">&#40;a1&#41; State of infection</span>&#58; This criterion is defined from two switches labelled &#8220;<span class="elsevierStyleItalic">Infection&#63;</span>&#8221; and &#8220;<span class="elsevierStyleItalic">Acute-infection&#63;</span>&#8221;&#46; The first switch allows us to set whether the prototype should consider the first step of the simulation starting from a natural pre-infection &#40;&#8220;<span class="elsevierStyleItalic">on</span>&#8221; state&#41;&#44; or&#44; alternatively&#44; start under a non-infectious condition &#40;&#8220;<span class="elsevierStyleItalic">off</span>&#8221; state&#41;&#46; The second switch allows us to set whether the model starts the simulation from an acute infection condition &#40;&#8220;<span class="elsevierStyleItalic">on</span>&#8221; state&#41;&#44; or&#44; alternatively&#44; start from a persistent infection condition &#40;&#8220;off&#8221; state&#41;&#46;</p><p id="par0215" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">&#40;a2&#41; Viral proteins</span>&#58; This component comprises eight switches that represent the initial state &#40;active or inactive&#41; of the early &#40;<span class="elsevierStyleItalic">Es</span>&#41; and late &#40;<span class="elsevierStyleItalic">Ls</span>&#41; proteins involved in the HPV16 life cycle&#46; These switches are labelled in the model as&#58; &#8220;<span class="elsevierStyleItalic">prtE1&#63;</span>&#8221;&#44; &#8220;<span class="elsevierStyleItalic">prtE2&#63;</span>&#8221;&#44; &#8220;<span class="elsevierStyleItalic">prtE4&#63;</span>&#8221;&#44; &#8220;<span class="elsevierStyleItalic">prtE5&#63;</span>&#8221; &#8220;<span class="elsevierStyleItalic">prtE6&#63;</span>&#8221;&#44; &#8220;<span class="elsevierStyleItalic">prtE7&#63;</span>&#8221;&#44; &#8220;<span class="elsevierStyleItalic">prtL1&#63;</span>&#8221;&#44; &#8220;<span class="elsevierStyleItalic">prtL2&#63;</span>&#8221;&#46;</p><p id="par0220" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">&#40;a3&#41; TLRs receptors</span>&#58; This component comprises four switches representing the initial state of the TLR3&#44; TLR4&#44; TLR7&#47;TLR8&#44; and TLR9 receivers&#44; specifically&#46;</p><p id="par0225" class="elsevierStylePara elsevierViewall">&#40;b&#41; Sliders&#58; This tool allows us to set an initial quantity associated with a general parameter that can vary between two limits&#44; one lower and one upper&#46; The model normally takes the lower range as the initial default value&#44; if the user does not modify the component&#46; The model reads these values and uses them during the simulation process&#44; even if the user modifies them at runtime &#40;<a class="elsevierStyleCrossRef" href="#fig0030">Fig&#46; 6</a> - option B&#41;&#46;</p><p id="par0230" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">&#40;b1&#41; Initial Population</span>&#58; This criterion&#44; labelled in the model as&#8221; <span class="elsevierStyleItalic">Initial Population</span>&#8221;&#44; is defined through a slider that allows us to set the number of haematopoietic cells from which the model will generate initial progenitor cell populations&#44; which will then lead to the CDPA processes&#46;</p><p id="par0235" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">&#40;b2&#41; Vaccines</span>&#58; The criteria defined in the model for vaccine control&#44; implements four sliders labelled &#8220;<span class="elsevierStyleItalic">antigen-loaded DCs</span>&#8221;&#44; &#8220;<span class="elsevierStyleItalic">Dosage</span>&#8221;&#44; &#8220;<span class="elsevierStyleItalic">Interval-weeks</span>&#8221; and &#8220;<span class="elsevierStyleItalic">anti-PD1</span>&#8221;&#46; These criteria allow us to set the vaccine type&#44; initial load&#44; number of doses&#44; and time intervals measured in weeks&#44; respectively&#46; The vaccine type can vary between DCs loaded with E6&#47;E7 antigens and anti-PD-1 vaccines&#46; The parameters can be defined in advance&#44; at the start of the simulation &#40;pre-programmed conditions&#41;&#44; or modified at runtime&#46; HPV16-ALIFE will make use of the pre-programmed vaccination parameters only when a cancer condition is detected that requires the release of a treatment dose&#44; because it is simulating a therapeutic vaccine in a setting associated with cervical cancer&#46; In a pre-programmed scenario&#44; the system will report the week in which the model actually delivers each vaccine dose&#46; This information can then be reviewed displaying the view associated with the command centre in the GUI&#46; If during a simulation the immune system clears the viral infection by itself and no cancerous lesions occur&#44; the model does not need to use the vaccination criteria&#46;</p><p id="par0240" class="elsevierStylePara elsevierViewall">&#40;c&#41; Selectors&#58; this tool allows us to complement the vaccination parameters&#44; which is especially useful when we want to add an adjuvant and&#47;or add blockers to a vaccine&#44; or when evaluating a drug containing certain activators and&#47;or blockers&#46; This type of tool allows us to choose an item from a drop-down list&#46; Once chosen&#44; the model is told that the selected component will be intervened either activating or inactivating its essential function&#46; As with the other components&#44; this model reads the values defined by the user&#44; thus altering the normal dynamics of the simulation based on the external change made to these parameters&#46; The model allows the following elements to be blocked or activated&#44; whether declared beforehand or modified at runtime&#46; It is important to clarify that under this concept and in the current version in which the prototype is presented&#44; for each run only one component can be selected for each available selector&#46; Thus&#44; at least four components can intervene simultaneously in the same simulation&#44; which can operate as adjuvants or blockers&#44; acting with each vaccine or drug that is being assessed in a specific patient&#46; This does not mean that a vaccine cannot block or activate other components on the list&#46; The current restriction is that a particular vaccine can work with up to four blockers and&#47;or activators within a single dose to be delivered to the virtual host&#46; For example&#44; in a DC vaccine loaded with E6 and E7 antigens&#44; blocking IL-6&#44; IL-10&#44; and activating IRF3 and IRF7&#44; the count of four components in this test refers specifically to&#58; IL-6&#44; IL-10&#44; IRF3&#44; and IRF7&#46; However&#44; four other different components could have been selected for the test &#40;<a class="elsevierStyleCrossRef" href="#fig0030">Fig&#46; 6</a> - option C&#41;&#46;</p><p id="par0245" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">&#40;c1&#41; Cytokine Blockers&#47;Activators</span>&#58; This tool allows the user to block or activate&#44; in advance or at simulation time&#44; components that are part of the cytokine signalling pathways&#46; To help locate them within the list displayed by the tool&#44; we present their components in alphabetical order&#46; This selector includes the following cytokines&#58; IL1 &#40;has two variants&#58; IL1&#945;&#44; IL1&#946;&#41;&#44; IL2-IL13&#44; IL15&#44; IL-17 &#40;has six variants&#58; IL17A-IL17F&#41;&#44; IL18-IL27&#44; IL30-IL38&#44; IFN-&#945;&#44; IFN-&#946;&#44; IFN-&#947;&#44; IFN-&#955; &#40;has three variants&#58; IFN&#955;1-IFN&#955;3&#41;&#44; MIF&#44; TGF-&#945;&#44; TGF-&#946;&#44; TNF-&#945;&#44; TNF-&#946;&#46;</p><p id="par0250" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">&#40;c2&#41; TLR Blockers&#47;Activators</span>&#58; This tool allows the user to block or activate&#44; in advance or at simulation time&#44; components that are part of TLR signalling pathways&#46; The tool includes the following components&#58; AP1&#44; CREB&#44; ERK&#44; IKK&#945;&#44; IKK&#946;&#44; IKK&#603;&#44; IRAK1&#44; IRAK2&#44; IRAK4&#44; IRF3&#44; IRF7&#44; JNK&#44; MEK1&#47;2&#44; MEK3&#47;6&#44; MEK4&#47;7&#44; mTOR&#44; MyD88&#44; NF-kB&#44; p38&#44; Pellino3&#44; PI3K&#44; Raptor&#44; RIP1&#44; TAB2&#44; TAB3&#44; TAK1&#44; TBK1&#44; TIRAP&#44; TRADD&#44; TRAF3&#44; TRAF6&#44; TRAM&#44; TRIF&#46;</p><p id="par0255" class="elsevierStylePara elsevierViewall">In both cases &#40;c1 and c2&#41; the &#8220;<span class="elsevierStyleItalic">nobody</span>&#8221; option is included&#46; Selecting this option implies that the selector is not participating in the simulation&#46; Once the selectors &#40;cytokines and&#47;or TLRs&#41; are activated&#44; the model will simulate the behaviour of a drug that will be administered to the patient permanently &#40;once a week&#41;&#44; with the specific purpose of intervening the signalling pathways associated with cytokines and&#47;or TLR&#46;</p></span><span id="sec0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Checkpoints</span><p id="par0260" class="elsevierStylePara elsevierViewall">The main checkpoints in this model are provided by the activation of TLRs that recognise pathogen-associated molecular patterns &#40;PAMPs&#41;&#44; including those of viral type&#44; such as TLR3&#44; TLR7&#44; TLR8 and TLR9&#59; and TLR4&#44; which recognises lipopolysaccharide &#40;LPS&#41; through an accessory molecule &#40;MD2 or CD14&#41;&#46; When these viral receptors are activated&#44; they stimulate the transcription of inflammatory genes and trigger certain signalling pathways&#46; These receptors lead to the stimulation of transcription factors that enable the secretion of five possible cytokine families&#44; which contribute to the manifestation or non-manifestation of an innate and adaptive immune response&#46; These receptors are also influenced by the action of certain oncoproteins expressed by HPV16&#46;</p><p id="par0265" class="elsevierStylePara elsevierViewall">The checkpoints associated with TLRs are represented in this model on the second level&#46; At Level-2 the interactions are stimulated that arise between TLRs and HPV16&#44; primarily through changes in the behaviour of oncoproteins expressed by the virus in response to stimuli triggered by viral TLRs&#46; In addition&#44; this activity induces the dynamics of exchange with Level-1 of the model&#44; through the infection processes that occur in keratinocytes &#40;KCs&#41; and the modulation generated in other cell populations under infectious conditions&#46; The dynamics of exchange with Level-3 are produced by the secretion and blocking of cytokines that modify the microenvironment and allow the activation of positive and negative feedback cycles between the cell populations involved&#46;</p></span><span id="sec0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Rules</span><p id="par0270" class="elsevierStylePara elsevierViewall">The components that make up the HPV16-ALIFE model are predominantly governed by the following rules&#58; co-selection&#44; network connectivity&#44; rotation&#44; suppression&#44; and state switching&#46;</p><p id="par0275" class="elsevierStylePara elsevierViewall">Co-selection&#44; a term introduced by G&#46; W&#46; Hoffmann in his immune network theory&#44;<a class="elsevierStyleCrossRef" href="#bib0180"><span class="elsevierStyleSup">36</span></a> denotes the positive mutual selection of individual members within dispersed cell populations&#44; such that the selection of members within each defined population is dependent on interactions&#44; with recognition of one or more members within other populations&#46; For example&#44; a population of positive T cells recognises the antigen and interacts with a diverse group of negative T cells&#46;</p><p id="par0280" class="elsevierStylePara elsevierViewall">Network connectivity&#44; proposed by G&#46; W&#46; Hoffmann in his theory&#44;<a class="elsevierStyleCrossRef" href="#bib0180"><span class="elsevierStyleSup">36</span></a> indicates that suppressed state is a state of high network connectivity&#46; Immune state is a low-connected state because antigen-specific cells are labelled&#46; Virgin state has the potential to change to a suppressed state or an immune state&#44; whereas the transformation from an immune state to a suppressed state&#44; or vice versa&#44; is more complex&#46; In virgin state&#44; T-cells with low connectivity act as helper cells&#44; and with high connectivity they behave as suppressor cells&#46; HPV16-ALIFE works under these concepts&#44; assuming them as rules by which to evaluate the various states arising from the interactions between the different components and the various networks that comprise it&#46; At the network level&#44; this model implements three types of networks&#58; a network built from cell populations&#44; a network composed of different cytokines and a network of TLRs&#46; At state level&#44; network connectivity allows encounters to be conducted between populations&#44; which mark the conditions of change between circumstances regularly identified by the activation or inactivation of surface molecules&#46; This is the case for encounters between DC and antigen populations&#44; DCs and antigen-specific T-cells&#44; antigen-specific T-cells and B-cells&#44; and other interactions implemented in this model&#44; which are illustrated in <a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>&#46;</p><p id="par0285" class="elsevierStylePara elsevierViewall">Rotation of cell populations results from DPAC processes&#46; Cell differentiation is directly affected by the expression levels of various cytokines&#44; leading to the definition of specific cell population types&#44; as presented by the process of T-helper and T-regulatory cell flexibility and plasticity&#46; Cell proliferation is linked to a rate of division&#44; depending on the type of population and the conditions of its microenvironment&#46; Cell elimination occurs in response to the activation of programmed cell death processes or apoptosis induced by interactions arising between elements of the microenvironment&#44; as may occur between cytotoxic T-cells &#40;CTLs&#41; and infected KCs&#46;</p><p id="par0290" class="elsevierStylePara elsevierViewall">The specific rules that affect all the procedures implemented in HPV16-ALIFE obey biological conditions that have been previously documented by the scientific community&#44; which are explicitly referenced in each of the processes involved&#46;<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a></p></span><span id="sec0075" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">States</span><p id="par0295" class="elsevierStylePara elsevierViewall">From the host&#8217;s perspective&#44; the HPV16-ALIFE model considers five possible stable states of cell populations&#44; including virgin&#44; suppressed&#44; immune&#44; autoimmune&#44; and tolerance states&#44; the scope of which corresponds to that proposed by G&#46; W&#46; Hoffmann in his immune network theory&#46;<a class="elsevierStyleCrossRef" href="#bib0180"><span class="elsevierStyleSup">36</span></a></p><p id="par0300" class="elsevierStylePara elsevierViewall">The host being in a virgin state implies that they have never been exposed to the pathogen&#46; In this context&#44; the model preserves a balancing environment&#44; induced by mutual elimination between clones of complementary specificities&#46; The behaviour of IgM and IgG antibodies is also incorporated into the model&#44; because of their importance for the microenvironment when this state is reported&#46; The lifespan of B-cells depends on their environment and interaction with other microenvironments&#44; which are mainly modulated by the expression of cytokine populations&#46; For example&#44; TGF plays a protective role and apoptosis mediated by BCR &#40;B-cell receptor&#41; and CD40 &#40;cluster of differentiation 40&#41; is induced after class switch recombination&#46; In this state&#44; cell apoptosis has been observed in the presence of BCR and CD40 after three to four days&#46;<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">37</span></a> In addition&#44; IL-21 also produces pro-apoptotic effects on B-cells stimulated with CD40-specific antibodies&#46;<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">38</span></a> The virgin state is perturbed by the detection of an infectious condition&#44; which is activated by interactions between PAMPs and TLRs&#46;</p><p id="par0305" class="elsevierStylePara elsevierViewall">The suppressed state has high levels of positive and negative T-cells&#44; and their mutual stimulation leads to a significant level of expression of their receptors&#44; molecules&#44; and cytokines&#46; These components in their negative condition block positive receptors&#44; and the positive components block negative receptors&#46; In the suppressed state&#44; specific B-cell receptors are blocked&#44; and in turn&#44; these blocks are generated by the expression of certain cytokines&#46;</p><p id="par0310" class="elsevierStylePara elsevierViewall">The immune state&#44; or antigen-exposed state&#44; is distinguished by high levels of positive cell populations and low levels of negative cell populations&#46; In our model&#44; the type of cells initially infected with HPV16 are KCs&#46; If persistent infection develops&#44; high levels of proliferation of KCs are reported&#44; while other cell populations may decrease&#44; such as CTLs and NK &#40;Natural Killer&#41; cells&#46;</p><p id="par0315" class="elsevierStylePara elsevierViewall">Autoimmune state is the opposite of immune state&#44; and is characterised by high levels of negative clones and decreased levels of positive cells&#46;</p><p id="par0320" class="elsevierStylePara elsevierViewall">Tolerant state is defined as a lack of response that occurs without suppression being evident&#46; Cells in virgin and immune states combine to produce a condition in which there is no response &#40;anergy&#41;&#46;</p><p id="par0325" class="elsevierStylePara elsevierViewall">State change is generated by interactions that occur between the various components of this model&#44; including receptors&#44; growth factors&#44; TLRs&#44; and cytokines&#46; These recent states can stimulate or override certain conditions associated with CDPA processes&#44; thus modifying the degrees of interaction between components and cell populations&#46;</p><p id="par0330" class="elsevierStylePara elsevierViewall">From a cell perspective and as a member of a population&#44; the HPV16-ALIFE model considers ten possible cell states&#58; immature&#44; mature&#44; healthy&#44; infected&#44; cytokine secretion&#44; transcription &#40;infecting from one cell to another&#44; from one generation to another&#41;&#44; replication &#40;copying itself&#41;&#44; differentiation &#40;acquiring a new phenotype and function&#41;&#44; proliferation &#40;multiplication of a cell population&#41;&#44; death &#40;natural or by apoptosis&#41;&#46;</p></span><span id="sec0080" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0130">Transitions</span><p id="par0335" class="elsevierStylePara elsevierViewall">Given the different cell types involved in the HPV16-ALIFE model&#44; the transitions of each population are affected by several events&#44; including the following&#58; &#40;i&#41; CDPA processes&#59; &#40;ii&#41; the influence exerted by the expression of particular molecules on each cell population&#59; &#40;iii&#41; cytokine networks&#59; and &#40;iv&#41; signalling pathways triggered by TLRs&#46; The biological background associated with CDPA processes&#44; as well as the molecules and cytokines that each population expresses&#44; along with the profile and functions of each of the five cytokine families considered in this model&#44; are extensively documented in the works of Goutagny&#44;<a class="elsevierStyleCrossRef" href="#bib0165"><span class="elsevierStyleSup">33</span></a> Akdis&#44;<a class="elsevierStyleCrossRef" href="#bib0195"><span class="elsevierStyleSup">39</span></a> De Silva&#44;<a class="elsevierStyleCrossRef" href="#bib0200"><span class="elsevierStyleSup">40</span></a> Turner&#44;<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">41</span></a> and Vazquez&#46;<a class="elsevierStyleCrossRef" href="#bib0210"><span class="elsevierStyleSup">42</span></a></p><p id="par0340" class="elsevierStylePara elsevierViewall">Regarding TLRs&#44; and considering that these receptors are implemented in the model under the ABM &#40;agent-based modelling&#41; concept&#44; it is appropriate to specify that transitions will be marked by the perceptions&#44; sensors&#44; agents&#44; actuators&#44; and actions inherent to this technique&#46; Perceptions emerge from the microenvironment in which interactions between cell populations&#44; HPV16 actions&#44; and cytokine networks occur&#46; Host-derived damage-associated molecular patterns &#40;DAMPs&#41; are endogenous molecules normally found in cells that are released during necrosis&#44; including some cytokines&#46; In contrast&#44; pathogen-derived PAMPs regularly become essential components for their survival&#46; DAMPs are recognised by several PRRs&#44; including toll-like receptors&#46; TLRs are receptors that identify PAMPs&#44;<a class="elsevierStyleCrossRefs" href="#bib0215"><span class="elsevierStyleSup">43&#44;44</span></a> including those that recognise viruses&#46; Thus&#44; the viral PAMPs signals produced in the model are detected by TLRs &#40;agents&#41; in the extracellular and endosomal environments&#46; In combination with different molecular adaptors &#40;such as MyD88&#44; TIRAP&#44; TRIF and TRAM&#41; - which assume the role of actuators in this model - different TLRs trigger their own signalling pathways&#46; These pathways induce the activation of different components&#44; including transcription factors &#40;e&#46;g&#46;&#44; IRFs&#44; CRB&#44; AP-1&#44; NF-kB&#41;&#44; effector pathways &#40;e&#46;g&#46;&#44; NF-kB&#41;&#44; and cytokines &#40;e&#46;g&#46;&#44; IFNs and ILs&#41;&#46;</p></span><span id="sec0085" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0135">Interactions</span><p id="par0345" class="elsevierStylePara elsevierViewall">The most general interaction produced in this model is between HPV16 and the host immune system&#46; Regarding HPV16&#44; interactions develop between cell populations&#44; TLRs and cytokines&#46; In relation to TLRs&#44; interactions are generated between effector pathways&#44; molecules&#44; transcription factors and cytokines&#46; However&#44; this crosstalk is part of four fundamental interactions that are defined in the model as stimulation&#44; inhibition&#44; suppression&#44; and death&#46;</p><p id="par0350" class="elsevierStylePara elsevierViewall">Under stimulation scenarios&#44; one component of this model causes the other component&#44; with which it interacts&#44; to be activated and produce an action akin to its natural function&#46; Under inhibition scenarios&#44; the opposite occurs&#44; one component of this model causes the other&#44; with which it interacts&#44; to be temporarily blocked and not allowed to perform its natural function&#46; This scenario continues to operate as long as the established interaction remains active&#46; Under suppression scenarios&#44; one component of this model prevents the action of its counterpart &#40;e&#46;g&#46;&#44; pro-apoptotic vs&#46; anti-apoptotic functions&#44; pro-inflammatory vs&#46; anti-inflammatory functions&#41;&#46; Under death scenarios&#44; one component of this model overrides the other&#44; with which it interacts&#44; and the overridden element then disappears permanently from the microenvironment &#40;e&#46;g&#46;&#44; cellular apoptosis&#41;&#46;</p><p id="par0355" class="elsevierStylePara elsevierViewall">In HPV16-ALIFE&#44; the interactions between the various cell populations are designed under the ABM technique&#44; as well as those related to TLRs and cytokines&#46; Considering that at the cellular level HPV16 exclusively affects KCs&#44; both the proteins expressed by the virus and this cell population are designed in our model as members of CA &#40;cellular automata&#41; controlled by specific lists&#46; Under this approach both the KC and protein ranges can vary between one and six members of the neighbourhood&#46; In each case their members are placed on a hexagonal grid&#44; so that each KC and each protein can have up to six neighbours simultaneously&#46; The KCs are stored in a list &#40;eligibles&#41; containing the identification numbers of the members of this population&#44; and these cells are affected by the expression of proteins whose labels are included in the second list &#40;switches&#41;&#46; Both lists are used to recognise the corresponding neighbours in each case and to support the options that may emerge from their potential interactions&#46;</p></span><span id="sec0090" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0140">Specific interactions</span><p id="par0360" class="elsevierStylePara elsevierViewall">Receptor crosstalk establishes an activation mechanism between B-cell and T-cell populations&#44; representing a stimulatory interaction&#46; Lymphocytes themselves act as efficient antigens since they contain a large number of specific receptors on their surface&#44; allowing them to crosstalk with other lymphocytes&#46; In relation to lymphocytes with mutually complementary receptors&#44; they can be expected to stimulate each other to proliferate or to secrete specific molecules&#46;<a class="elsevierStyleCrossRef" href="#bib0180"><span class="elsevierStyleSup">36</span></a></p><p id="par0365" class="elsevierStylePara elsevierViewall">HPV16-ALIFE produces cell activation of specific populations based on this concept&#46; A cell that complements another cell induces a modification to the state of its cognate receptors&#46; When the cell results from a process of differentiation or proliferation&#44; its receptors are in a zero &#40;0&#41; state&#44; which denotes inactivity&#46; Once the cell is activated through various processes&#44; its state changes to state one &#40;1&#41;&#59; when a successful interaction is generated&#44; the respective receptors and molecules change to state two &#40;2&#41;&#46; In addition&#44; the variables defined in the model&#39;s cell populations preserve traceability&#44; which enables the types of cells and receptors to be evidenced that are coupled because of their interactions&#46; This is what results from the interaction processes between DCs and antigens&#44; DCs and antigen-specific B-cells&#44; T-cells&#44; and antigen-specific B-cells&#44; among other signal exchange actions implemented in this model as detailed below&#46;</p><p id="par0370" class="elsevierStylePara elsevierViewall">With respect to inhibitory or blocking activity&#44; positive components block negative receptors and vice versa&#46; This behaviour can be seen during the expression of pro-inflammatory and&#47;or anti-inflammatory cytokines&#46; In HPV16-ALIFE&#44; when the expression of a cytokine is active&#44; its state will be identified with a value equal to one &#40;1&#41; and&#44; conversely&#44; when its expression is inactivated or blocked by some element of the environment&#44; its state will change to zero &#40;0&#41;&#46; Regarding death or elimination activities&#44; cell populations will respond to programmed cell death processes&#46; These processes are triggered as a condition of natural death or death by apoptosis&#44; which will depend on the microenvironment events resulting from interactions between cell populations&#44; types of cell populations affected&#44; and state of some specific markers&#46;</p><p id="par0375" class="elsevierStylePara elsevierViewall">Regarding the HPV16 life cycle within the model&#44; the rules are dictated by the functions associated with the early and late proteins expressed by the virus &#40;E1&#44; E2&#44; E4&#44; E5&#44; E6&#44; E7&#44; L1 and L2&#41;&#46; This process&#44; and those associated with their states&#44; transitions&#44; and interactions&#44; are extensively documented in our previous publication&#46;<a class="elsevierStyleCrossRef" href="#bib0225"><span class="elsevierStyleSup">45</span></a></p></span></span><span id="sec0095" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0145">Conclusions</span><p id="par0380" class="elsevierStylePara elsevierViewall">This work enabled us to establish a biological knowledge base&#44; sufficient to define a conceptual design&#44; determine the general logic&#44; and produce the implementation dynamics of the HPV16-ALIFE artificial life model&#46; All these components are fundamental inputs for the construction of a functional prototype&#46; This prototype operates as a virtual laboratory through which we can observe the functionality of the model&#46; Due to its length&#44; the HPV16-ALIFE prototype is presented and evaluated in a separate article&#46;</p></span><span id="sec0100" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0150">Compliance with ethical standards</span><p id="par0385" class="elsevierStylePara elsevierViewall">This article meets the applicable ethical standards&#46;</p></span><span id="sec0105" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0155">Conflict of interests</span><p id="par0390" class="elsevierStylePara elsevierViewall">The authors have no conflict of interests to declare&#46;</p></span></span>"
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              "titulo" => "Therapeutic vaccine monitoring"
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            0 => "Artificial life"
            1 => "Artificial immune system"
            2 => "Cervical cancer"
            3 => "Hpv16"
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            0 => "Vida artificial"
            1 => "Sistema inmune artificial"
            2 => "C&#225;ncer c&#233;rvix"
            3 => "Hpv16"
            4 => "Modelo simulaci&#243;n"
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        "titulo" => "Abstract"
        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Objective</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">To present an artificial life model that simulates behaviours based on interactions that emerge between the human immune system&#44; the life cycle of human papillomavirus type 16&#44; some types of therapeutic virus type 16&#44; and some types of therapeutic vaccines&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Materials and methods</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Using the approach of complex systems theories and biological systems&#44; we created an artificial life model that allowed us to simulate virtual patient immune systems that develop infectious viral processes&#46; These patients receive a therapeutic vaccine capable of releasing the necessary doses autonomously&#44; once the model establishes that the immune system has outgrown its ability to counteract lesions caused by persistent viral infection&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Conclusions</span><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">The methodology developed provides independence to the modelling of each of the components that make up each of the components that constitute the three proposed domains and reflects the dynamics that emerge from their interactions&#44; thus ensuring the corresponding feedback processes&#46; With this work&#44; a knowledge base is established that allows us to define the conceptual design&#44; determine the general logic&#44; and produce the dynamics of implementation of the HPV16-ALIFE model&#46;</p></span>"
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        "resumen" => "<span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0030">Objetivo</span><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Presentar un modelo de vida artificial que simula comportamientos basados en interacciones que emergen entre el sistema inmune humano&#44; ciclo de vida del virus de papiloma humano tipo 16&#44; y algunos tipos de vacunas terap&#233;uticas&#46;</p></span> <span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Materiales y m&#233;todos</span><p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Bajo el enfoque de teor&#237;as de sistemas complejos y sistemas biol&#243;gicos&#44; creamos un modelo de vida artificial que nos permite simular sistemas inmunes de pacientes virtuales que desarrollan procesos virales infecciosos&#46; Estos pacientes reciben una vacuna terap&#233;utica capaz de liberar las dosis necesarias de forma aut&#243;noma&#44; una vez el modelo establece que el sistema inmune ha superado su capacidad de contrarrestar lesiones causadas por la infecci&#243;n viral persistente&#46;</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Conclusiones</span><p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">La metodolog&#237;a desarrollada proporciona independencia al modelamiento de cada uno de los componentes que integran los tres dominios propuestos y refleja la din&#225;mica que surge a partir de sus interacciones&#44; garantizando de esta forma los procesos de retroalimentaci&#243;n correspondientes&#46; Con este trabajo se establece una base de conocimiento que nos permite definir el dise&#241;o conceptual&#44; determinar la l&#243;gica general&#44; y producir la din&#225;mica de implementaci&#243;n del modelo HPV16-ALIFE&#46;</p></span>"
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        "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Please cite this article as&#58; Escobar Ospina ME&#46; Simulador de interacciones del sistema inmune humano&#58; descripci&#243;n del modelo para el ciclo de vida del virus de papiloma humano tipo 16 y vacunas terap&#233;uticas&#46; Vacunas&#46; 2022&#59;23&#58;1&#8211;16&#46;</p>"
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          "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">General design of the HPV16-ALIFE model&#46; This figure depicts some of the actions that arise between the host &#40;left-side&#41; and virus &#40;right-side&#41; domains&#46; The vertical interactions that occur between the two domains are organised into three different levels&#58; Level-1&#44; Level-2&#44; and Level-3&#44; represented as green rectangles&#44; which segment the Figure into three areas&#46; Within each level a specific microenvironment is simulated&#44; represented by hexagons&#44; which correspond to each of the microenvironments&#58; cellular&#44; TLRs and cytokines&#44; respectively&#44; when viewed from top to bottom&#46; The horizontal interactions that emerge between host and pathogen&#44; within each level are also depicted&#46; The downward-pointing arrows &#40;red&#41; represent downstream signalling processes&#46; The upward pointing arrows &#40;blue&#41; represent upstream signalling processes&#46; The syringes shown at each level represent the nanodevice that simulates therapeutic vaccines&#44; with or without adjuvant &#40;vaccine domain&#41;&#44; which enter to modify the immune system responses to each microenvironment once delivered to the virtual patient&#46; Additional details to each level in the text&#46;</p>"
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          "en" => "<p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Cell differentiation process in the HPV16-ALIFE model&#46; Activity starts from a haematopoietic stem cell &#40;HSC&#41; &#40;follow the Figure from left to right&#41;&#44; which can diverge into a common progenitor&#44; either myeloid or lymphoid&#46; From a myeloid progenitor&#44; the cell can differentiate into populations of macrophages or DCs&#46; From a lymphoid progenitor&#44; the cell can differentiate into a B-cell lineage or T&#47;NK cell progenitor&#46; For the macrophage population&#44; the cell can differentiate between M1-type macrophages and M2-type macrophages&#46; With respect to the DC population&#44; the cell can differentiate into cDC&#44; pDC&#44; mDC&#44; and LC&#46; In relation to the B-cell lineage&#44; the cell can differentiate into memory B-cells and plasma cells&#44; both populations capable of producing antibodies&#46; From a T&#47;NK cell progenitor&#44; the cell can diverge into a T-cell lineage or an NK cell lineage per se&#46; From the T-cell lineage&#44; a cell can differentiate into effector T-cells or memory T-cells&#46; Effector T-cells can differentiate into CD8 T-cells and CD4 T-cells&#46; In turn&#44; CD8 T-cells can differentiate into CTLs&#46; In relation to CD4 T-cells&#44; a cell can differentiate into Th1 helper T-cells&#44; Th2 helper T-cells&#44; and type 3 helper T-cells &#40;Th3&#41;&#46; In reality&#44; the Th3 population comprises several types of T-helper cells that&#44; due to a characteristic of plasticity&#44; one group of cells can become another&#46; Within the Th3 population&#44; T-cells can differentiate into type-9 &#40;Th9&#41;&#44; type-17 &#40;Th17&#41;&#44; type-22 &#40;Th22&#41;&#44; regulatory T-cells &#40;Treg&#41;&#44; and follicular helper T-cells &#40;Tfh&#41;&#46; In turn&#44; memory T-cells can diverge into CD4 memory T-cells or CD8 memory T-cells&#46; As for CD4 memory T-cells&#44; a cell can differentiate into Tcm&#46; With respect to CD8 memory T-cells&#44; a cell can differentiate into Tcm&#44; Tem&#44; Trm&#44; and Tsm&#46; The shaded areas in the Figure correspond to the range established for each of the cell populations incorporated in this model&#46; In the figure&#44; the yellow rectangles represent master regulators of cell differentiation&#59; the green rectangles represent cytokines secreted by each cell group&#44; and the fuchsia rectangles represent surface molecules expressed by each cell population&#46;</p>"
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          "en" => "<p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Symbols that represent cell populations in the HPV16-ALIFE model&#46; Each symbol represents a cell population within HPV16-ALIFE&#46; <span class="elsevierStyleItalic">Bcell</span>&#58; B-cell lineage&#59; <span class="elsevierStyleItalic">gcB cell</span>&#58; germinal centre B-cell&#59; <span class="elsevierStyleItalic">slPC</span>&#58; short-lived plasma cell&#59; <span class="elsevierStyleItalic">slmBC</span>&#58; short-lived memory B-cell&#59; <span class="elsevierStyleItalic">llPC</span>&#58; long-lived plasma cell&#59; <span class="elsevierStyleItalic">llmBC</span>&#58; long-lived memory B-cell&#59; <span class="elsevierStyleItalic">Antibody</span>&#59; <span class="elsevierStyleItalic">pDC</span>&#58; dendritic cell&#59; <span class="elsevierStyleItalic">Tcell</span>&#58; T-cell lineage&#59; <span class="elsevierStyleItalic">NK</span>&#58; Natural Killer cell lineage&#59; <span class="elsevierStyleItalic">TCD4</span>&#58; new CD4 T-cell&#59; <span class="elsevierStyleItalic">TCD8</span>&#58; new CD8 T-cell&#59; <span class="elsevierStyleItalic">CTL</span>&#58; cytotoxic T-lymphocyte&#59; <span class="elsevierStyleItalic">mTCD8</span>&#58; memory CD8 T-cell&#59; <span class="elsevierStyleItalic">KC</span>&#58; keratinocyte&#59; <span class="elsevierStyleItalic">FDC</span>&#58; follicular dendritic cell&#46; <span class="elsevierStyleItalic">Th1</span>&#58; T-helper cell type-1&#59; <span class="elsevierStyleItalic">Th2</span>&#58; T-helper cell type-2&#59; <span class="elsevierStyleItalic">Tfh</span>&#58; follicular T-helper cell&#59; <span class="elsevierStyleItalic">Th9</span>&#58; T-helper cell type-9&#59; <span class="elsevierStyleItalic">Th17</span>&#58; T-helper cell type-17&#59; <span class="elsevierStyleItalic">Th22</span>&#58; T-helper cell type-22&#59; <span class="elsevierStyleItalic">Treg</span>&#58; regulatory T-cell&#59; <span class="elsevierStyleItalic">mfm1</span>&#58; M1-type macrophage&#46;</p>"
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          "en" => "<p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">TLR signalling process in the HPV16-ALIFE model&#46; In an abstraction from reality&#44; this figure represents cytoplasm&#44; endocytosis area&#44; endosomal membrane&#44; and cell nucleus&#46; Some TLRs are expressed on the cell surface and others intracellularly&#46; This Figure shows each of the currently reported TLRs&#46; Next to each TLR is the year it was identified&#44; the molecular patterns associated with the pathogens it recognises&#44; and the signalling that mediates its activation&#46; TLRs have a common intracellular domain called TIR &#40;<span class="elsevierStyleItalic">Toll-IL-1R</span>&#41;&#46; TLRs are activated by different ligands&#44; which localise to different types of organisms and structures and have adapters that allow them to respond to activation processes&#46; Different adaptors couple to different receptors&#44; and once this coupling is activated it determines the signalling pathway that will be triggered&#46; The characterisation of TIR domain-containing adaptors has established essential roles in TLR signalling pathways&#46; The antiviral TLRs are TLR3&#44; TLR7&#44; TLR8&#44; and TLR9&#46;<a class="elsevierStyleCrossRefs" href="#bib0130"><span class="elsevierStyleSup">26&#8211;29</span></a> The most complex signalling scenario is TLR4&#44; which can activate two pathways&#44; one reported on the cell surface and the other in the area of endocytosis&#46; Once TLRs are activated through binding to a ligand&#44; a cascade of intracellular kinases is triggered by intermediate adaptor molecules&#46; Depending on their nature&#44; these adaptors recruit and activate certain complexes such as IRAKs&#44; TBKs&#44; IKKs&#44; some ubiquitin ligases &#40;TRAF6&#44; TRAF3&#44; and Pellino-1&#41;&#44; and then conclude in coupling the NF-kB&#44; IFN type I&#44; MAPK-p38&#44; and MAPK-JNK JNK<a class="elsevierStyleCrossRefs" href="#bib0135"><span class="elsevierStyleSup">27&#44;28&#44;30</span></a> pathways&#46;</p>"
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          "en" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Interleukin families and cancer&#46; Interleukins are classified into superfamilies&#46; Each superfamily brings together several interleukins that have one or more common characteristics&#46; Currently&#44; more than 38 cytokines have been identified&#44; although only a few can be considered anti-cancer agents&#44; some others are referred to as pro-cancer agents&#44; and there are others that show both pro- and anti-carcinogenic activities&#46; Therefore&#44; the impact of cytokines on cancer is ambiguous&#46; There is also a group of ILs that have been little investigated in cancer&#46; In the context of cancer&#44; this Figure classifies ILs according to the colour represented by their members&#44; as follows&#58; green&#44; corresponding to ILs with an anti-cancer profile&#59; yellow&#44; corresponding to ILs with a pro-cancer profile&#59; purple&#44; corresponding to ILs with both pro- and anti-cancer activities&#59; and red&#44; corresponding to ILs that so far have not been associated with cancer&#46;</p>"
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          "en" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Global parameters of the HPV16-ALIFE prototype&#46; This Figure shows the components that allow the initial values of the model to be modified before activating the simulation&#46; These components include&#58; &#40;A&#41; Initial state of viral proteins and activity state of TLR ligands&#59; &#40;B&#41; Initial parameters of therapeutic vaccine under evaluation&#59; &#40;C&#41; Components associated with TLRs &#40;left-side&#41; and cytokines &#40;right-side&#41; signalling pathways&#46; These components represent part of the GUI incorporated into the developed prototype&#46; These parameters can also be modified at runtime&#46;</p>"
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      "titulo" => "References"
      "seccion" => array:1 [
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          "identificador" => "bibs0005"
          "bibliografiaReferencia" => array:45 [
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              "identificador" => "bib0005"
              "etiqueta" => "1"
              "referencia" => array:1 [
                0 => array:1 [
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              ]
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              "etiqueta" => "2"
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                      "doi" => "10.1007/978-3-030-29022-1_8"
                      "LibroEditado" => array:5 [
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                            0 => "L&#46;S&#46; Chang"
                            1 => "C&#46;H&#46; Leng"
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                            3 => "C&#46;C&#46; Wu"
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                        ]
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                  ]
                  "host" => array:1 [
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                      "doi" => "10.1186/1476-4598-13-60"
                      "Revista" => array:5 [
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                            0 => "C&#46;T&#46; Lin"
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                    0 => array:2 [
                      "doi" => "10.1371/journal.pone.0146622"
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                          0 => array:2 [
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                      "autores" => array:1 [
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                          "autores" => array:6 [
                            0 => "H&#46;L&#46; Wang"
                            1 => "H&#46; Xu"
                            2 => "W&#46;H&#46; Lu"
                            3 => "L&#46; Zhu"
                            4 => "Y&#46;H&#46; Yu"
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                      ]
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                      "doi" => "10.1007/s00404-013-2938-1"
                      "Revista" => array:6 [
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                        "paginaFinal" => "162"
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                        0 => array:2 [
                          "etal" => true
                          "autores" => array:6 [
                            0 => "X&#46;M&#46; Wu"
                            1 => "X&#46; Liu"
                            2 => "Q&#46;F&#46; Jiao"
                            3 => "S&#46;Y&#46; Fu"
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                    0 => array:2 [
                      "doi" => "10.7314/apjcp.2014.15.6.2447"
                      "Revista" => array:6 [
                        "tituloSerie" => "Asian Pac J Cancer Prev&#46;"
                        "fecha" => "2014"
                        "volumen" => "15"
                        "paginaInicial" => "2447"
                        "paginaFinal" => "2451"
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                          0 => array:2 [
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                      "autores" => array:1 [
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                          "etal" => false
                          "autores" => array:3 [
                            0 => "A&#46;M&#46; Azmal Moshed"
                            1 => "M&#46;K&#46; Islam Sarkar"
                            2 => "M&#46;A&#46; Khaleque"
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                      "doi" => "10.5099/aj170100001"
                      "Revista" => array:5 [
                        "tituloSerie" => "Am J Biomed Sci&#46;"
                        "fecha" => "2017"
                        "volumen" => "9"
                        "paginaInicial" => "1"
                        "paginaFinal" => "14"
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                    0 => array:2 [
                      "titulo" => "Nanomedicines&#58; redefining traditional medicine"
                      "autores" => array:1 [
                        0 => array:2 [
                          "etal" => false
                          "autores" => array:4 [
                            0 => "W&#46; Lu"
                            1 => "J&#46; Yao"
                            2 => "X&#46; Zhu"
                            3 => "Y&#46; Qi"
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                        ]
                      ]
                    ]
                  ]
                  "host" => array:1 [
                    0 => array:2 [
                      "doi" => "10.1016/j.biopha.2020.111103"
                      "Revista" => array:3 [
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                        "fecha" => "2021"
                        "volumen" => "134"
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            10 => array:3 [
              "identificador" => "bib0055"
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                0 => array:2 [
                  "contribucion" => array:1 [
                    0 => array:2 [
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                      "autores" => array:1 [
                        0 => array:2 [
                          "etal" => false
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                            0 => "M&#46; Hu"
                            1 => "X&#46; Ge"
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                            4 => "X&#46; Qian"
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Article information

ISSN: 24451460
Original language: English

DOI:10.1016/j.vacune.2022.01.002

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