Systems immunology

Source: Wikipedia, the free encyclopedia.

Systems immunology is a research field under

experiments rather than in vivo
.

Recent studies in experimental and clinical

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.

The techniques that are used in

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

ligands and the roles of CD4 and CD8 co-receptors
.
dissociation rates of the interacting species. These models are able to present the concentration and steady state of each interacting molecule in the network
.
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

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

mononucleosis in younger people. After running numerical simulations, only the first two hypotheses were supported by the model.[8]

Partial Differential Equation model

cell membranes and therefore move within a two dimensional compartment.[10]
The
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

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

lymphocytes that upon stimulation had active and inactive subpopulations.[12]

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

lymphoid system.[14]

Agent-based models

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

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.
leukocytes.[16]

Boolean model

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

macrophages increased for both young and old mice, while others suggest that there is a decrease.[23]

The

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

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

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

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

  • A Plaidoyer for ‘Systems Immunology’[50]
  • Systems and Synthetic Immunology[51]
  • Systems Biology[52]
  • Current Topics in Microbiology and Immunology[53]
  • The FRiND model[54]
  • The Multiscale Systems Immunology project[55]
  • Modelling with BioNetGen[56]

References

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  2. PMID 27714570
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  3. PMID 21548192
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  4. PMID 20727155
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  5. PMID 19897090
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  6. ^ "BioNetGen". BioNetGen.
  7. PMID 22711887
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  8. PMID 21700566
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  9. ^ Biomedical Engineering Theory And Practice/Physiological Modeling and Simulation.
  10. ISBN 9781498717403
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  11. PMID 17346997
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  12. ISBN 978-1-4987-1740-3
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  13. ISBN 978-1-4987-1740-3
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  14. PMID 26301680
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  15. PMID 29320541
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  16. PMID 20174606
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  17. PMID 5803332
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  18. S2CID 23503933
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  19. S2CID 2443677
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  20. ^ "DDlab".
  21. ^ "Cell-Devs".
  22. ISBN 978-1-4665-9749-5
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  23. PMID 28904202
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  24. ^ "Boolnet".
  25. ^ "GINsim".
  26. ^ "Cell Collective".
  27. S2CID 18190414
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  28. ^ "GINsim".
  29. ^ "GINsim".
  30. ^ "CellDesigner".
  31. ^ "Kyoto Encyclopedia of Genes and Genomes".
  32. ^ "Reactome".
  33. ^ "GINsim".
  34. S2CID 119514242.{{cite journal}}: CS1 maint: numeric names: authors list (link
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  35. PMID 20378558
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  36. ^ "Cran package- Boolnet". 27 February 2023.
  37. ^ "Cell Collective".
  38. PMID 23549147
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  39. PMID 27402907
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  40. PMID 31898481
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  41. ^ "Gepasi".
  42. ^ "Gepasi tutorial".
  43. ^ "Copasi".
  44. ^ "BioUML".
  45. ^ "Simbiology".
  46. ^ "Bio-SPICE".
  47. ^ "ETH Zurich".
  48. ^ "CSHL".
  49. ^ "Meetings".
  50. S2CID 27355198
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  51. ISBN 978-9811533495
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  52. ISBN 978-1-934115-64-0
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  53. ISBN 978-3-642-33098-8
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  54. PMID 31302876
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  55. PMID 18442405
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  56. PMID 19399430
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