Simulation of Immune System

1. Simulation of Immune System Answering Questions on the Natural Immune System Behavior by Simulations

2. INTRODUCTION • Three questions : • Q1: Is the immune innate system able to solve all attacks? • Q2: What is the actual role of the adaptive part? Is it to improve immune reaction that can be resolved by innate part? Is it to defend against attacks unresolved by innate part? • Q3: Quantitative aspect of the intervention of each part.

3. INTRODUCTION I. Simulator Choice CImmSim II. Simulator Description II.A The Computational Model II.B The Entity Description II.C The interactions

4. INTRODUCTION III. Simulator Behavior III.A Basic Step of Algorithms III.B The interaction Model III.C The Mutation IV. The usefulness of CImmSim for Us V. Illustration of the Simulator Use CONCLUSION

5. I. SIMULATOR CHOICE • SIMMUNE : • only immune adaptive system simulated • behavior of the cells can be defined with flexibility • copy will be available after code update • SIMISYS : • not finished project

6. I. SIMULATOR CHOICE • SIS-I,SIS-II : • only T and B cells simulated • IMMSIM: • adaptive part with humoral and cellular mediated response • source code available directly (CImmSim) • Cellular Automaton (CA) based

7. II. SIMULATOR DESCRIPTION II.A The Computational Model • Lymph node is mapped onto a bi-dimensional hexagonal lattice.

8. II. SIMULATOR DESCRIPTION II.A The Computational Model • The hexagonal lattice coordinate system. (x,y) = (u+v*sin(pi/6),u*cos(pi/6))

9. II. SIMULATOR DESCRIPTION II.B The Entity Description

10. II. SIMULATOR DESCRIPTION II.B The Entity Description

11. II. SIMULATOR DESCRIPTION II.C The Interactions • External interactions occur among cells which have the same position on the lattice. v(d) is affinity potential, d is the Hamming distance, h is the affinity enhance.

12. II. SIMULATOR DESCRIPTION II.C The Interactions • The Hamming distance is the number of complementary bits between two bit string. In this case, the Hamming distance is the length string, i.e d=16.

13. II. SIMULATOR DESCRIPTION II.C The Interactions • External interactions occur among cells and molecules which have the same position on the lattice. • Internal interactions inside a cell. (MHC-Ag peptide interactions).

14. III. SIMULATOR BEHAVIOR III.A Basic Step of Algorithm • All the feasible interactions among cells and molecules take place within a lattice site in a single time step. • Diffusion of entities is done at the beginning of each time step. • Time step = mitosis cycle = 8 hours.

15. III. SIMULATOR BEHAVIOR III.B The Interaction Model

16. III. SIMULATOR BEHAVIOR III.C The Mutation • In the natural human system, the normal mutation rate is low. • In very restricted region of B cells chromosomes, high rate of mutation occurs: the somatic hypermutation. • The somatic hypermutation allows to obtain a collection of B cells whose receptors are better to recognize an antigen : affinity maturation

17. III. SIMULATOR BEHAVIOR III.C The Mutation • In CImmSim, we can set a parameter which allow to enhance the affinity to the antigen only by hypermutation (see later).

18. IV. THE USEFULNESS OF CIMMSIM FOR US. • CImmSim does not implement the innate system. • CImmSim simulates well the adaptive system. • We can look at the time taken to clear the pathogen. • We can learn, reuse, improve the model in order to extend the simulator such as be able to simulate the innate and the adaptive part together.

19. V. ILLUSTRATION OF THE SIMULATOR USE. • The EP cells are killed by Tk cells.

20. V. ILLUSTRATION OF THE SIMULATOR USE. • The EP cells are killed by Tk cells.

21. Th and B cells interactions

22. Th and B cells interactions

23. V. ILLUSTRATION OF THE SIMULATOR USE. • The minmatch parameter.

24. V. ILLUSTRATION OF THE SIMULATOR USE. • The minmatch parameter. • !! Drawbacks!!

25. V. ILLUSTRATION OF THE SIMULATOR USE. • The Hole parameter.

26. V. ILLUSTRATION OF THE SIMULATOR USE. • The Bystander parameter.

27. V. ILLUSTRATION OF THE SIMULATOR USE. • The Bystander parameter.

28. V. ILLUSTRATION OF THE SIMULATOR USE. • The Bystander parameter. • Drawback : to much of useless cells

29. CONCLUSION • The distribution in the time of the project: • 2/7 used for choosing the simulator • 3/7 used to full understand the model and the simulator behavior • 2/7 used for visualization and the experiments • Skills exercised: • theoretical comprehension • code comprehension • link between both

30. CONCLUSION • We can use the well-modeled adaptive system to see the impact of some parameters. • We can also retain the model used to simulate the adaptive part, the interactions system, thanks to the concept of entities and cellular automaton. • Visualization is very important. We have to know also where are the molecules and to see the space dependences between entities.

31. CONCLUSION • Future works would be to model a immune system simulator able to highlight the manner of the innate system interacts with the adaptive system using the feature of CImmSim. • We can add entities of innate part, create new interactions between these new entities and the old one. • We can create new states for the adaptive cells, like a “Waiting” state during which the lymphocytes wait the innate part signal.

32. CONCLUSION • We have to add new entities as self entities but not implicated in the immune process in order to check if the system simulated is dangerous for self entities. • Again, visualization is important! We have to be able to trace all the entities : cells of the both system and molecules (have a structure to record the molecule position) and see how they interact.

33. CONCLUSION • We have to be able to see the cytokines secretion between cells which is not possible only with graph. • We have to be able to set the influence of each cell in order to have quantitative aspect of the intervention of each part.