Systems immunology

Last updated

Systems immunology is a research field under systems biology that uses mathematical approaches and computational methods to examine the interactions within cellular and molecular networks of the immune system. [1] The immune system has been thoroughly analyzed as regards to its components and function by using a "reductionist" approach, but its overall function can't be easily predicted by studying the characteristics of its isolated components because they strongly rely on the interactions among these numerous constituents. It focuses on in silico experiments rather than in vivo .

Contents

Recent studies in experimental and clinical immunology have led to development of mathematical models that discuss the dynamics of both the innate and adaptive immune system. [2] Most of the mathematical models were used to examine processes in silico that can't be done in vivo . These processes include: the activation of T cells, cancer-immune interactions, migration and death of various immune cells (e.g. T cells, B cells and neutrophils) and how the immune system will respond to a certain vaccine or drug without carrying out a clinical trial. [3]

Techniques of modelling in Immune cells

A scheme that describes how mathematical models are used in immunology. 11538 2016 214 Fig4 HTML.webp
A scheme that describes how mathematical models are used in immunology.

The techniques that are used in immunology for modelling have a quantitative and qualitative approach, where both have advantages and disadvantages. Quantitative models predict certain kinetic parameters and the behavior of the system at a certain time point or concentration point. The disadvantage is that it can only be applied to a small number of reactions and prior knowledge about some kinetic parameters is needed. On the other hand, qualitative models can take into account more reactions but in return they provide less details about the kinetics of the system. The only thing in common is that both approaches lose simplicity and become useless when the number of components drastically increase. [4]

Ordinary Differential Equation model

Ordinary differential equations (ODEs) are used to describe the dynamics of biological systems. ODEs are used on a microscopic, mesoscopic and macroscopic scale to examine continuous variables. The equations represent the time evolution of observed variables such as concentrations of protein, transcription factors or number of cell types. They are usually used for modelling immunological synapses, microbial recognition and cell migration. Over the last 10 years, these models have been used to study the sensitivity of TCR to agonist ligands and the roles of CD4 and CD8 co-receptors.
Kinetic rates of these equations are represented by binding and dissociation rates of the interacting species. These models are able to present the concentration and steady state of each interacting molecule in the network. ODE models are defined by linear and non-linear equations, where the nonlinear ones are used more often because they are easier to simulate on a computer ( in silico ) and to analyse. The limitation of this model is that for every network, the kinetics of each molecule has to be known so that this model could be applied. [5]

The ODE model was used to examine how antigens bind to the B cell receptor. This model was very complex because it was represented by 1122 equations and six signalling proteins. The software tool that was used for the research was BioNetGen. [6] The outcome of the model is according to the in vivo experiment. [7]

The Epstein-Barr virus (EBV) was mathematically modeled with 12 equations to investigate three hypotheses that explain the higher occurrence of mononucleosis in younger people. After running numerical simulations, only the first two hypotheses were supported by the model. [8]

Partial Differential Equation model

Partial differential equation (PDE) models are an extended version of the ODE model, which describes the time evolution of each variable in both time and space. PDEs are used on a microscopic level for modeling continuous variables in the sensing and recognition of pathogens pathway. They are also applied for physiological modeling [9] to describe how proteins interact and where their movement is directed in an immunological synapse. These derivatives are partial because they are calculated with the respect to time and also with the respect to space. Sometimes a physiological variable such as age in cell division can be used instead of the spatial variables. Comparing the PDE models, which take into account the spatial distribution of cells, to the ODE ones, the PDEs are computationally more demanding. Spatial dynamics are an important aspect of cell signalling as it describes the motion of cells within a three dimensional compartment. T cells move around in a three dimensional lymph node while TCRs are located on the surface of cell membranes and therefore move within a two dimensional compartment. [10] The spatial distribution of proteins is important especially upon T cell stimulation, when an immunological synapse is made, therefore this model was used in a study where the T cell was activated by a weak agonist peptide. [11]

Particle-based Stochastic model

Particle-based stochastic models are obtained based on the dynamics of an ODE model. What differs this model from others, is that it considers the components of the model as discrete variables, not continuous like the previous ones. They examine particles on a microscopic and mesoscopic level in immune-specific transduction pathways and immune cells-cancer interactions, respectively. The dynamics of the model are determined by the Markov process, which in this case, expresses the probability of each possible state in the system upon time in a form of differential equations. The equations are difficult to solve analytically, so simulations on the computer are performed as kinetic Monte Carlo schemes. The simulation is commonly carried out with the Gillespie algorithm, which uses reaction constants that are derived from chemical kinetic rate constants to predict whether a reaction is going to occur. Stochastic simulations are more computationally demanding and therefore the size and scope of the model is limited.

The stochastic simulation was used to show that the Ras protein, which is a crucial signalling molecule in T cells, can have an active and inactive form. It provided insight to a population of lymphocytes that upon stimulation had active and inactive subpopulations. [12]

Co-receptors have an important role in the earliest stages of T cell activation and a stochastic simulation was used to explain the interactions as well as to model the migrating cells in a lymph node. [13]

This model was used to examine T cell proliferation in the lymphoid system. [14]

Agent-based models

Summary of interactions between CD8+ T cells and Beta cells in Diabetes I Summary of interactions between CD8+ T cells and Beta cells in Diabetes I.tif
Summary of interactions between CD8+ T cells and Beta cells in Diabetes I

Agent-based modeling (ABM) is a type of modelling where the components of the system that are being observed, are treated as discrete agents and represent an individual molecule or cell. The components - agents, called in this system, can interact with other agents and the environment. ABM has the potential to observe events on a multiscale level and is becoming more popular in other disciplines. It has been used for modelling the interactions between CD8+ T cells and Beta cells in Diabetes I [15] and modelling the rolling and activation of leukocytes. [16]

Boolean model

Logic models are used to model the life cycles of cells, immune synapse, pathogen recognition and viral entries on a microscopic and mesoscopic level. Unlike the ODE models, details about the kinetics and concentrations of interacting species isn't required in logistic models. Each biochemical species is represented as a node in the network and can have a finite number of discrete states, usually two, for example: ON/OFF, high/low, active/inactive. Usually, logic models, with only two states are considered as Boolean models. When a molecule is in the OFF state, it means that the molecule isn't present at a high enough level to make a change in the system, not that it has zero concentration. Therefore, when it is in the ON state it has reached a high enough amount to initiate a reaction. This method was first introduced by Kauffman. The limit of this model is that it can only provide qualitative approximations of the system and it can’t perfectly model concurrent events. [17]

This method has been used to explore special pathways in the immune system such as affinity maturation and hypermutation in the humoral immune system [18] and tolerance to pathologic rheumatoid factors. [19] Simulation tools that support this model are DDlab, [20] Cell-Devs [21] and IMMSIM-C. IMMSIM-C is used more often than the others, as it doesn’t require knowledge in the computer programming field. The platform is available as a public web application and finds usage in undergraduate immunology courses at various universities (Princeton, Genoa, etc.). [22]

For modelling with statecharts, only Rhapsody has been used so far in systems immunology. It can translate the statechart into executable Java and C++ codes.

This method was also used to build a model of the Influenza Virus Infection. Some of the results were not in accordance with earlier research papers and the Boolean network showed that the amount of activated macrophages increased for both young and old mice, while others suggest that there is a decrease. [23]

The SBML (Systems Biology Markup Language) was supposed to cover only models with ordinary differential equations, but recently it was upgraded so that Boolean models could be applied. Almost all modeling tools are compatible with SBML. There are a few more software packages for modeling with Boolean models: BoolNet, [24] GINsim [25] and Cell Collective. [26]

Computer tools

To model a system by using differential equations, the computer tool has to perform various tasks such as model construction, calibration, verification, analysis, simulation and visualization. There isn’t a single software tool that satisfies the mentioned criteria, so multiple tools need to be used. [27]

GINsim

GINsim [28] is a computer tool that generates and simulates genetic networks based on discrete variables. Based on the regulatory graphs and logical parameters, GINsim [29] calculates the temporal evolution of the system which is returned as a State Transition Graph (STG) where the states are represented by nodes and transitions by arrows.
It was used to examine how T cells respond upon activation of the TCR and TLR5 pathway. These processes were observed both separately and in combination. First, the molecular maps and logic models for both TCR and TLR5 pathways were built and then merged. Molecular maps were produced in CellDesigner [30] based on data from literature and various databases, such as KEGG [31] and Reactome. [32] The logical models were generated by GINsim [33] where each component has the value of either 0 or 1 or additional values when modified. Logical rules are then applied to each component, which are called logical nodes in this network. After merging the final model consists of 128 nodes. The results of modelling were in accordance with the experimental ones, where it was demonstrated that the TLR5 is a costimulatory receptor for CD4+ T cells. [34]

Boolnet

Boolnet [35] is a R package which contains tools for reconstruction, analysis and visualization of Boolean networks. [36]

Cell Collective

The Cell Collective [37] is a scientific platform which enables scientists to build, analyse and simulate biological models without formulating mathematical equations and coding. It has a Knowledge Base component built in it which extends the knowledge of individual entities (proteins, genes, cells, etc.) into dynamical models. The data is qualitative but it takes into account the dynamical relationship between the interacting species. The models are simulated in real-time and everything is done on the web. [38]

BioNetGen

BioNetGen (BNG) is an open-source software package that is used in rule-based modeling of complex systems such as gene regulation, cell signaling and metabolism. The software uses graphs to represent different molecules and their functional domains and rules to explain the interactions between them. In terms of immunology, it was used to model intracellular signalling pathways of the TLR-4 cascade. [39]

DSAIRM

DSAIRM (Dynamical Systems Approach to Immune Response Modeling) is a R package that is designed for studying infection and immune response dynamics without prior knowledge of coding. [40]

Other useful applications and learning environments are: Gepasi, [41] [42] Copasi, [43] BioUML, [44] Simbiology (MATLAB) [45] and Bio-SPICE. [46]

Conferences

The first conference in Synthetic and Systems Immunology was hosted in Ascona by CSF and ETH Zurich. [47] It took place in the first days of May 2019 where over fifty researchers, from different scientific fields were involved. Among all presentations that were held, the best went to Dr. Govinda Sharma who invented a platform for screening TCR epitopes.

Cold Spring Harbor Laboratory (CSHL) [48] from New York, in March 2019, hosted a meeting where the focus was to exchange ideas between experimental, computational and mathematical biologists that study the immune system in depth. The topics for the meeting where: Modelling and Regulatory networks, the future of Synthetic and Systems Biology and Immunoreceptors. [49]

Further reading

Related Research Articles

<span class="mw-page-title-main">Antigen</span> Molecule triggering an immune response (antibody production) in the host

In immunology, an antigen (Ag) is a molecule, moiety, foreign particulate matter, or an allergen, such as pollen, that can bind to a specific antibody or T-cell receptor. The presence of antigens in the body may trigger an immune response.

<span class="mw-page-title-main">Immune system</span> Biological system protecting an organism against disease

The immune system is a network of biological systems that protects an organism from diseases. It detects and responds to a wide variety of pathogens, from viruses to parasitic worms, as well as cancer cells and objects such as wood splinters, distinguishing them from the organism's own healthy tissue. Many species have two major subsystems of the immune system. The innate immune system provides a preconfigured response to broad groups of situations and stimuli. The adaptive immune system provides a tailored response to each stimulus by learning to recognize molecules it has previously encountered. Both use molecules and cells to perform their functions.

<span class="mw-page-title-main">Immunology</span> Branch of medicine studying the immune system

Immunology is a branch of biology and medicine that covers the study of immune systems in all organisms.

<span class="mw-page-title-main">T cell</span> White blood cells of the immune system

T cells are one of the important types of white blood cells of the immune system and play a central role in the adaptive immune response. T cells can be distinguished from other lymphocytes by the presence of a T-cell receptor (TCR) on their cell surface.

<span class="mw-page-title-main">T helper cell</span> Type of immune cell

The T helper cells (Th cells), also known as CD4+ cells or CD4-positive cells, are a type of T cell that play an important role in the adaptive immune system. They aid the activity of other immune cells by releasing cytokines. They are considered essential in B cell antibody class switching, breaking cross-tolerance in dendritic cells, in the activation and growth of cytotoxic T cells, and in maximizing bactericidal activity of phagocytes such as macrophages and neutrophils. CD4+ cells are mature Th cells that express the surface protein CD4. Genetic variation in regulatory elements expressed by CD4+ cells determines susceptibility to a broad class of autoimmune diseases.

<span class="mw-page-title-main">Major histocompatibility complex</span> Cell surface proteins, part of the acquired immune system

The major histocompatibility complex (MHC) is a large locus on vertebrate DNA containing a set of closely linked polymorphic genes that code for cell surface proteins essential for the adaptive immune system. These cell surface proteins are called MHC molecules.

<span class="mw-page-title-main">Adaptive immune system</span> Subsystem of the immune system

The adaptive immune system, also known as the acquired immune system, or specific immune system is a subsystem of the immune system that is composed of specialized, systemic cells and processes that eliminate pathogens or prevent their growth. The acquired immune system is one of the two main immunity strategies found in vertebrates.

<span class="mw-page-title-main">Antigen-presenting cell</span> Cell that displays antigen bound by MHC proteins on its surface

An antigen-presenting cell (APC) or accessory cell is a cell that displays antigen bound by major histocompatibility complex (MHC) proteins on its surface; this process is known as antigen presentation. T cells may recognize these complexes using their T cell receptors (TCRs). APCs process antigens and present them to T-cells.

<span class="mw-page-title-main">T-cell receptor</span> Protein complex on the surface of T cells that recognises antigens

The T-cell receptor (TCR) is a protein complex found on the surface of T cells, or T lymphocytes, that is responsible for recognizing fragments of antigen as peptides bound to major histocompatibility complex (MHC) molecules. The binding between TCR and antigen peptides is of relatively low affinity and is degenerate: that is, many TCRs recognize the same antigen peptide and many antigen peptides are recognized by the same TCR.

<span class="mw-page-title-main">Clonal selection</span> Model of the immune system response to infection

In immunology, clonal selection theory explains the functions of cells of the immune system (lymphocytes) in response to specific antigens invading the body. The concept was introduced by Australian doctor Frank Macfarlane Burnet in 1957, in an attempt to explain the great diversity of antibodies formed during initiation of the immune response. The theory has become the widely accepted model for how the human immune system responds to infection and how certain types of B and T lymphocytes are selected for destruction of specific antigens.

In immunology, central tolerance is the process of eliminating any developing T or B lymphocytes that are autoreactive, i.e. reactive to the body itself. Through elimination of autoreactive lymphocytes, tolerance ensures that the immune system does not attack self peptides. Lymphocyte maturation occurs in primary lymphoid organs such as the bone marrow and the thymus. In mammals, B cells mature in the bone marrow and T cells mature in the thymus.

<span class="mw-page-title-main">Fc receptor</span> Surface protein important to the immune system

In immunology, an Fc receptor is a protein found on the surface of certain cells – including, among others, B lymphocytes, follicular dendritic cells, natural killer cells, macrophages, neutrophils, eosinophils, basophils, human platelets, and mast cells – that contribute to the protective functions of the immune system. Its name is derived from its binding specificity for a part of an antibody known as the Fc region. Fc receptors bind to antibodies that are attached to infected cells or invading pathogens. Their activity stimulates phagocytic or cytotoxic cells to destroy microbes, or infected cells by antibody-mediated phagocytosis or antibody-dependent cell-mediated cytotoxicity. Some viruses such as flaviviruses use Fc receptors to help them infect cells, by a mechanism known as antibody-dependent enhancement of infection.

Co-stimulation is a secondary signal which immune cells rely on to activate an immune response in the presence of an antigen-presenting cell. In the case of T cells, two stimuli are required to fully activate their immune response. During the activation of lymphocytes, co-stimulation is often crucial to the development of an effective immune response. Co-stimulation is required in addition to the antigen-specific signal from their antigen receptors.

<span class="mw-page-title-main">Immunological synapse</span> Interface between lymphocyte and target cell

In immunology, an immunological synapse is the interface between an antigen-presenting cell or target cell and a lymphocyte such as a T/B cell or Natural Killer cell. The interface was originally named after the neuronal synapse, with which it shares the main structural pattern. An immunological synapse consists of molecules involved in T cell activation, which compose typical patterns—activation clusters. Immunological synapses are the subject of much ongoing research.

MHC-restricted antigen recognition, or MHC restriction, refers to the fact that a T cell can interact with a self-major histocompatibility complex molecule and a foreign peptide bound to it, but will only respond to the antigen when it is bound to a particular MHC molecule.

Gamma delta T cells are T cells that have a γδ T-cell receptor (TCR) on their surface. Most T cells are αβ T cells with TCR composed of two glycoprotein chains called α (alpha) and β (beta) TCR chains. In contrast, γδ T cells have a TCR that is made up of one γ (gamma) chain and one δ (delta) chain. This group of T cells is usually less common than αβ T cells. Their highest abundance is in the gut mucosa, within a population of lymphocytes known as intraepithelial lymphocytes (IELs).

Kinetic-segregation is a model proposed for the mechanism of T-cell receptor (TCR) triggering. It offers an explanation for how TCR binding to its ligand triggers T-cell activation, based on size-sensitivity for the molecules involved. Simon J. Davis and Anton van der Merwe, University of Oxford, proposed this model in 1996. According to the model, TCR signalling is initiated by segregation of phosphatases with large extracellular domains from the TCR complex when binding to its ligand, allowing small kinases to phosphorylate intracellular domains of the TCR without inhibition. Its might also be applicable to other receptors of the Non-catalytic tyrosine-phosphorylated receptors family such as CD28.

<span class="mw-page-title-main">Danger model</span>

The danger model of the immune system proposes that it differentiates between components that are capable of causing damage, rather that distinguishing between self and non-self.

Mucosal-associated invariant T cells make up a subset of T cells in the immune system that display innate, effector-like qualities. In humans, MAIT cells are found in the blood, liver, lungs, and mucosa, defending against microbial activity and infection. The MHC class I-like protein, MR1, is responsible for presenting bacterially-produced vitamin B2 and B9 metabolites to MAIT cells. After the presentation of foreign antigen by MR1, MAIT cells secrete pro-inflammatory cytokines and are capable of lysing bacterially-infected cells. MAIT cells can also be activated through MR1-independent signaling. In addition to possessing innate-like functions, this T cell subset supports the adaptive immune response and has a memory-like phenotype. Furthermore, MAIT cells are thought to play a role in autoimmune diseases, such as multiple sclerosis, arthritis and inflammatory bowel disease, although definitive evidence is yet to be published.

Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behaviour of biological molecules or complexes that can adopt a large number of possible functional states.

References

  1. ↑ Villani, Alexandra - Chloè; Sarkizova; Hacohen (26 Apr 2018). "Systems Immunology: Learning the Rules of the Immune System". Annual Review of Immunology. 36: 813–842. doi:10.1146/annurev-immunol-042617-053035. PMC   6597491 . PMID   29677477.
  2. ↑ Eftimie, Raluca; Gillard, Joseph J.; Cantrell, Doreen A. (6 Oct 2016). "Mathematical Models for Immunology: Current State of the Art and Future Research Directions". Bulletin of Mathematical Biology. 78 (10): 2091–2134. doi:10.1007/s11538-016-0214-9. PMC   5069344 . PMID   27714570.
  3. ↑ Schubert, Charlotte (4 May 2011). "Systems Immunology: Complexity captured". Nature. 473 (7345): 113–114. doi: 10.1038/nj7345-113a . PMID   21548192.
  4. ↑ Thakar, Juilee; Poss, Mary; Albert, Réka; Long, Gráinne H; Zhang, Ranran (20 Aug 2010). "Dynamic models of immune responses: what is the ideal level of detail?". Theoretical Biology and Medical Modelling. 7: 35. doi: 10.1186/1742-4682-7-35 . PMC   2933642 . PMID   20727155.
  5. ↑ Kim, Peter S; Levy, Doron; Lee, Peter P (1 Jan 2009). Modeling and simulation of the immune system as a self-regulating network. Methods in Enzymology. Vol. 467. pp. 79–109. doi:10.1016/S0076-6879(09)67004-X. ISBN   9780123750235. PMID   19897090.
  6. ↑ "BioNetGen". BioNetGen.
  7. ↑ Barua, Dipak; Hlavacek, William S; Lipniacki, Tomasz (18 June 2012). "A computational model for early events in B cell antigen receptor signaling: analysis of the roles of Lyn and Fyn". The Journal of Immunology. 189 (2): 646–658. doi:10.4049/jimmunol.1102003. PMC   3392547 . PMID   22711887.
  8. ↑ Huynh; Adler (29 September 2012). "Mathematical modelling the age dependence of Epstein-Barr virus associated infectious mononucleosis". Mathematical Medicine and Biology. 29 (3): 245–261. doi:10.1093/imammb/dqr007. PMID   21700566.
  9. ↑ Biomedical Engineering Theory And Practice/Physiological Modeling and Simulation.
  10. ↑ Croce, Roberta; Grondelle, Rienk van; Amerongen, Herbert van; Stokkum, Ivo van; Bagshaw, Clive R. (6 September 2018). Das, Jayajit; Jayaprakash, Ciriyam (eds.). Systems Immunology: An introduction to Modelin Methods for scientists (1st ed.). CRC Press. ISBN   9781498717403.
  11. ↑ Čemerski, Sašo; Das, Jayajit; Locasale, Jason; Arnold, Phoebe; Guirisato, Emanuele; Markiewicz, Mary A.; Fremont, Daved; Allen, Paul M.; Chakraborty, Arup K.; Shaw, Andrey S. (26 Mar 2007). "The stimulatory potency of T cell antigens is influenced by the formation of the immunological synapse". Immunity. 26 (3): 345–355. doi:10.1016/j.immuni.2007.01.013. PMC   2763191 . PMID   17346997.
  12. ↑ Bagshaw, Clive R.; Barrick, Douglas E.; Nordlund, Thomas M. (2019). Lyubchenko, Yuri L.; Croce, Roberta; van Grondelle, Rienk; van Amerongen, Herbert; van Stokkum, Ivo; Nadeau, Jay L. (eds.). Systems Immunology: An introduction to Modeling Methods for Scientists. CRC Press. ISBN   978-1-4987-1740-3.
  13. ↑ Bagshaw, Clive R.; Barrick, Douglas E.; Nordlund, Thomas M. (2019). Lyubchenko, Yuri L.; Croce, Roberta; van Grondelle, Rienk; van Amerongen, Herbert; van Stokkum, Ivo; Nadeau, Jay L. (eds.). Systems Immunology: An introduction to Modeling Methods for Scientists. CRC Press. ISBN   978-1-4987-1740-3.
  14. ↑ Boianelli, Allessandro; Pettini, Ellena; Prota, Gennaro; Medaglini, Donata; Vicino, Antonio (24 Aug 2015). "A Stochastic Model for CD4+ T Cell Proliferation and Dissemination Network in Primary Immune Response". PLOS ONE. 10 (8): e0135787. Bibcode:2015PLoSO..1035787B. doi: 10.1371/journal.pone.0135787 . PMC   4547705 . PMID   26301680.
  15. ↑ Cagdas Ozturk, Mustafa; Xu, Qian; Cinar, Ali (10 January 2018). "Agent-based modeling of the interaction between CD8+ T cells and Beta cells in type 1 diabetes". PLOS ONE. 13 (1): e0190349. Bibcode:2018PLoSO..1390349O. doi: 10.1371/journal.pone.0190349 . PMC   5761894 . PMID   29320541.
  16. ↑ Tang, Jonathan; Hunt, Anthony C. (19 Feb 2010). "Identifying the Rules of Engagement Enabling Leukocyte Rolling, Activation, and Adhesion". PLOS Computational Biology. 6 (2): e1000681. Bibcode:2010PLSCB...6E0681T. doi: 10.1371/journal.pcbi.1000681 . PMC   2824748 . PMID   20174606.
  17. ↑ Kauffman, S.A (March 1969). "Metabolic stability and epigenesis in randomly constructed genetic nets". Journal of Theoretical Biology. 22 (3): 437–467. Bibcode:1969JThBi..22..437K. doi:10.1016/0022-5193(69)90015-0. PMID   5803332.
  18. ↑ Celada, Franco; Seiden, Philip E. (June 1996). "Affinity maturation and hypermutation in a simulation of the humoral immune response". European Journal of Immunology. 26 (6): 1350–8. doi:10.1002/eji.1830260626. PMID   8647216. S2CID   23503933.
  19. ↑ Stewart, J J; Agosto, H; Litwin, S; Welsh, J D; Shlomchik, M; Weigert, M; Seiden, P E (15 Aug 1997). "A solution to the rheumatoid factor paradox: pathologic rheumatoid factors can be tolerized by competition with natural rheumatoid factors". Journal of Immunology. 159 (4): 1728–38. doi: 10.4049/jimmunol.159.4.1728 . PMID   9257834. S2CID   2443677.
  20. ↑ "DDlab".
  21. ↑ "Cell-Devs".
  22. ↑ Castiglione, Filippo; Celada, Franco (7 April 2015). Immune System and Modelling Simulation. CRC Press. ISBN   978-1-4665-9749-5.
  23. ↑ Keef, Ericka; Zhang, Li Ang; Clermont, Gilles; Swingon, David; Urbano, Alisa; Ermentrout, G. Bard; Matuszewski, Michael; R. Toapanta, Franklin; Ross, Ted M.; Parker, Robert S. (Nov 2019). "Discrete Dynamical Modeling of Influenza Virus Infection Suggests Age-Dependent Differences in Immunity". Journal of Virology. 91 (23). doi:10.1128/JVI.00395-17. PMC   5686742 . PMID   28904202.
  24. ↑ "Boolnet".
  25. ↑ "GINsim".
  26. ↑ "Cell Collective".
  27. ↑ Narang, Vipin; Decraene, James; Wong, Shek-Yoon; Aiswarya, Bindu; Wasem, Andrew; Leong, Shiang; Gouaillard, Alexandre (13 Apr 2012). "Systems immunology: a survey of modeling formalisms, applications and simulation tools". Immunologic Research. 53 (1–3): 251–265. doi:10.1007/s12026-012-8305-7. PMID   22528121. S2CID   18190414.
  28. ↑ "GINsim".
  29. ↑ "GINsim".
  30. ↑ "CellDesigner".
  31. ↑ "Kyoto Encyclopedia of Genes and Genomes".
  32. ↑ "Reactome".
  33. ↑ "GINsim".
  34. ↑ Rodriguez- Jorge, Otoniel; Kempis-Calanis, Linda A.; Abou-Jaude, Wassim; Gutierrez-Reyna, Darely Y.; 16 April 2019 (2019). "Cooperation between T cell receptor and Toll-like receptor 5 signaling for CD4+ T cell activation" (PDF). Science Signaling. 12 (577): eaar3641. doi: 10.1126/scisignal.aar3641 . PMID   30992399. S2CID   119514242.{{cite journal}}: CS1 maint: numeric names: authors list (link)
  35. ↑ Müssel, C.; Hopfensitz, M.; Kestler, HA. (2010). "BoolNet - an R package for generation, reconstruction, and analysis of Boolean networks". Bioinformatics. 26 (10): 1378–1380. doi: 10.1093/bioinformatics/btq124 . PMID   20378558.
  36. ↑ "Cran package- Boolnet". 27 February 2023.
  37. ↑ "Cell Collective".
  38. ↑ Helikar, T; Kowal, B; Rogers, JA (20 Feb 2013). "A Cell Simulator Platform: The Cell Collective". Clinical Pharmacology & Therapeutics. 93 (5): 393–395. doi:10.1038/clpt.2013.41. PMC   5242230 . PMID   23549147.
  39. ↑ Harris, Leonard A.; Hogg, Justin S.; Tapia, José-Juan; Sekar, John A. P.; Gupta, Sanjana; Korsunsky, Ilya; Arora, Arshi; Barua, Dipak; Sheehan, Robert P.; Faeder, James R. (1 November 2016). "BioNetGen 2.2: advances in rule-based modeling". Bioinformatics. 32 (21): 3366–3368. doi:10.1093/bioinformatics/btw469. PMC   5079481 . PMID   27402907.
  40. ↑ Handel, A. (Jan 2020). "A software package for immunologists to learn simulation modeling". BMC Immunol. 21 (1): 1. doi: 10.1186/s12865-019-0321-0 . PMC   6941246 . PMID   31898481.
  41. ↑ "Gepasi".
  42. ↑ "Gepasi tutorial".
  43. ↑ "Copasi".
  44. ↑ "BioUML".
  45. ↑ "Simbiology".
  46. ↑ "Bio-SPICE".
  47. ↑ "ETH Zurich".
  48. ↑ "CSHL".
  49. ↑ "Meetings".
  50. ↑ Christophe, Christophe; Germain, Ronald; Mathis, Diane (April 2006). "A Plaidoyer for 'Systems Immunology'". Immunological Reviews. Wiley-Blackwell. 210 (1): 229–234. doi:10.1111/j.0105-2896.2006.00374.x. PMID   16623774. S2CID   27355198.
  51. ↑ Singh, Shailza, ed. (25 Apr 2020). Systems and Synthetic Immunology (1 ed.). Springer Singapore. ISBN   978-9811533495.
  52. ↑ Maly, Ivan V. (2009). Systems Biology. Humana Press. ISBN   978-1-934115-64-0.
  53. ↑ Katze, Michael G., ed. (2013). Systems Biology. Springer. ISBN   978-3-642-33098-8.
  54. ↑ Houston, M.T.; Gutierrez, J.B. (13 July 2019). "The FRiND Model: A Mathematical Model for Representing Macrophage Plasticity in Muscular Dystrophy Pathogenesis". Bull Math Biol. 81 (10): 3976–3997. doi: 10.1007/s11538-019-00635-8 . PMC   6764940 . PMID   31302876.
  55. ↑ Mitha, F.; Lucas, T. A.; Feng, F.; Kepler, T. B.; Chan, C (2008). "The Multiscale Systems Immunology project: software for cell-based immunological simulation". Source Code for Biology and Medicine. 3: 6. doi: 10.1186/1751-0473-3-6 . PMC   2426691 . PMID   18442405.
  56. ↑ Faeder, JR; Blinov, ML; Hlavacek, WS. (2009). "Rule-Based Modeling of Biochemical Systems with BioNetGen". Systems Biology. Methods Mol Biol. Vol. 500. pp. 113–167. doi:10.1007/978-1-59745-525-1_5. ISBN   978-1-934115-64-0. PMID   19399430.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.