Network Model of Immune Responses: Analyzing Co-Infection Dynamics in Rabbits

1. Summary and Analysis of Network Model of Immune Responses Reveals Key Effectors to Single and Co-infection Dynamics by a Respiratory Bacterium and a Gastrointestinal HelminthJuilee Thakar, AshutoshK. Pathak,Lisa Murphy, Reka Albert, Isabella M. Cattadori Please use the model, not me. By Rob Altman CS 502

2. Outline • Boolean Networks • Scientific Methodology + Boolean Networks • Immune System Basics • Specific Problem Investigated • Design Details • Research Results • Future Work

3. Boolean Network Models Introduction • Biology theorist Stuart Kauffman introduced random boolean networks (RBA) • Interconnected behavior of genes • Robust circuitry of life vs. Man made devices • Life forms may be the product of random construction • Profoundly interesting ideas on the general behavior of systems

4. RBM Characteristic Phases[2] • 3 characteristic phases • Ordered • Critical • Chaos • Rate of evolution is maximized at the edge of chaos • Butterfly effect • Similar states and convergence or divergence

5. Critical Phase in Cellular Automata [11]

6. Critical Phase in Cellular Automata [11]

7. RBN Classic ModelProperties • Graph representation • N nodes of boolean value • Exactly K connections (edges) coming in • Can include self-edges • Connections are wired randomly • Logical functions determine values of nodes at update time. • Connections and functions remain static • Synchronous updating

8. RBN Properties Continued • Total number of networks is huge • Attractors of Network properties can be examined • Fixed states • Cycles

9. BNM by Update Type [2]

10. Dynamics of Same BNM Different Update Schemes[2]

11. Specific Model Used • Discrete dynamic model with asynchronous update (DDMA) • Discrete really means Boolean • Dynamic just implies network effects via time • Asynchronous – one at time updating • Random permutations for updating • Remove an unnatural bias • Maintain static attractors, cyclic change • Ultimately allows more realistic sampling

12. BNM + Scientific Methodology(10 steps summarized) Similar to developing a database schema • Background reading • Construct a table of components • Create network using Octums Razor • Develop an equation for each node • Select status for input node • Simulate long term behavior • Replicate simulations and summarize • Check with empirical data • Check robustness of model (perturbations) • More perturbations • Always OFF or ON [3] Iterate

13. Biological Background(Immune System) • Organs, tissues, cells, and cell products that protect the animal’s body from pathogens • Innate vs. adaptive • Immune system is highly multitasking • Pathogens, tissue repair, preventing immunopathology • TH1/Th2 paradigm • Th1 fights bacteria • Th2 fights multicellular parasites • Phagocytes – final destroyers of pathogens • Neutrophils • Macrophages • Others, but not important for our scope • Cytokines – signalers and switch like • Antibodies – Typically attach to a pathogen and directly attract phagocytes to destroy come and destroy it. [7]

14. Motivation and Demonstration • Who here has recently been sick?

15. N-bug Paradox • To be prone to thinking only one thing causes sickness is natural human psychology and also common causal fallacy • It’s hard to “think outside the box”, but harder still to think inside the network. • You really have to create the network and probe • Researchers made this mistake

16. Specific Research Interests • Interested in Rabbit immune system • Understanding and Exploring immune co-infection dynamics • Extending upon the TH1/Th2 model • include local site components (Respiratory tract and Intestines)

17. Issues Faced • Had empirical data on mice for single infection • bacteria, B. bronchiseptica • helminth (parasite), T. retortaeformis. • Had some qualitative data on co-infection • Little quantitative data • Biological experiments on rabbits would be time consuming and difficult

18. Network Model Approach • Why Discrete Dynamic Model with Asynchronous Update (DDMA) • Known methodology for qualitative data • Cheap, easy to program, flexible, interactive • Ultimately allow for predictions on Components Component interactions • Need to anchor with some empirical data • Create single infection network models for both Bacteria and Parasite based on mouse data and initial knowledge only. Validate with own experiments on rabbits • Merge both single infections into a co-infection model • Validate as best a possible with with co-infection experiment on rabbits • Probe and test dynamics of system for novel insight

19. Single Infection DDMA Model Designs • Component nodes and edges were straight forward • Local site vs. systemic compartmentalization • Boolean transfer rules • Labor intensive • Disambiguation by looking at later network effects • Real ambiguity = brute force iteration on boolean values • Recorded data is not an average but a consensus • Test individual components by simulated knockouts and resulting attractor states • Pathogen clearance, persistence • Qualitative pathogen activity • Robustness • Nature’s designs are robust in general • Empirical validation

20. Single Infection Results • Network behavior vs. Empirical data • antibodies, IFNγ, interleukins, and phagocytes, as well as processes like pathogen clearance were nearly identical • Impressive simulation of known but rather subtle behavior • Momentary increase of bacteria in lungs due to • bacteria temporarily increase IL10 production which inhibits IFNγ, a counter-bacteria cytokine in the rabbit

21. Co-infection DDMA Model Design • Big design question for the co-infection model • how to best join single infection models • Unlimited pool of undifferentiated T cells • Potential over-simplification? • Key link established through cytokine components as 3 pools • Lungs, Intestines, Systemic (lymphatic system) • Allows cross-regulatory effects of the bacteria and parasite to be simulated

22. Full Co-Infection Network T0 - Naïve T-Cells, All others Cytokines

23. Co-Infection Hypothesis • Considering co-infection dynamics to single infection dynamics • In layman’s terms, the biologists expected: • The bacteria in the lungs would increase • Parasites in the intestines would not change much • More Technically • Parasites in intestines AND Bacteria in respiratory System -> More T0 cells become Th2 cells -> polarization effect towards intestines -> bystander effect in lungs -> Th2 cells suppresses IFNγ AND IFNγ needed to fight bacteria -> bacteria increase in lungs • Parasite infection -> not much changed expected? • N-bug Paradox

24. Co-Infection Results Bacteria • Similar clearance, but low amount of remaining bacteria • Attributed to IL4 persistence due to parasite caused Th2 environment • Later proven by empirical testing • Interesting observation regarding knockout of bacterial-active epithelial cells having no effect • On bacteria, due to pro-inflammatory response caused by parasite

25. Co-Infection Results Parasite 1 • Big surprise, the parasites cleared faster in the Co-infection model, Why? • Neutrophils were significantly more active, Why? • Knockout analysis and corresponding at attractor states • 92% percent of overall nodes led to increased parasite activity, but not persistence: suggests synergistic dynamics • eosinophils, neutrophils, or cytokines IL5 and IL13 • Did lead to long-term persistence (fixed state) • This insight led to further support of a hypothesis cited in several papers.

26. Co-Infection Results Parasite 2 • The mechanism is interesting to biologists • It involves a pro-inflammatory response in which: • The antibodies act in an indirectway to the recruit both phagocytes (neutrophils and eosinophils) • Typically antibodies form complexes (which directly attract phagocytes)

27. Parasite Single Infection

28. Parasite Co-Infection overall lower clearance higher and longer acting

29. Summary • Biologist motivation was to extend the current co-infection model using qualitative data. • Although well informed, the scientists initial hypotheses were myopic. • The specialized Boolean network model built was essentially a qualitative causal • Using the model corrected the biologists’ non-network-biased intuition • Stimulating and probing the network model led to the validation and support of several immune level co-infection mechanisms

30. Future Work Considerations • For biologists • For computer scientists to help biologists • For computer scientists who might find this study stimulating

31. Future Work for Biologists • Success implies iteration • Extend the new model by replacing the most current assumptions • Better model the infinite pool of naïve T-cells assumption • Add more components downstream • Peripherally • Extend by divide and conquer • Advance the “component frontier” of the model using humanintuition of biology as the “search” heuristic

32. Future Work Computer Scientists with Biologists 1 • Better visualization • Cellular automata is visually inspiring • Are a specialized kind of Boolean network that uses spatial structure (earlier picture: Rule 30) • Transforming a Boolean network into a cellular automata • 2D, 3D, or nDcellular automata • May allow for a better visual interface of the system.

33. Future Work Computer Scientists with Biologists 2 • Quantification, potential project ideas • Could be accomplished by categorizing and counting the behavior of cycles in the model • Increase the node complexity • Increase the number of discrete values • Increase the number of states held in a node • Store data • Database • Knowledge base • Much reasoning to derive knowledge from the BNM required knockouts • Knockouts are essential proofs by contradiction • Resolution algorithm might be helpful

34. Future Work Computer Scientists with Biologists 3 • Brute force iteration on Boolean values when trying to determine interactive functions of nodes • Perhaps a curve fitting algorithm might help to automate the process

35. Future Work Computer Scientists • Interesting connection between Algorithms and Methodologies

36. Future Work Computer Scientists • Database schemas and building networks • Similar processes • Consider creating a “Wikipedia” of networks • More dynamic behavior of information exchange • Semantic Web implications • More non-linguistic models are needed • Need more representation of how the world works in non text form • Need closer to the edge of chaos representation

37. Questions? • What was wrong with the mice model?

38. References 1 • Thakar, J., Pathak, A. K., Murphy, L., Albert, R., & Cattadori, I. M. (2012). Network model of immune responses reveals key effectors to single and co-infection dynamics by a respiratory bacterium and a gastrointestinal helminth. PLoS computational biology, 8(1), e1002345. • Gershenson, C. (2004). Introduction to random Boolean networks. arXiv preprint nlin/0408006. • Assmann, S. M., & Albert, R. (2009). Discrete dynamic modeling with asynchronous update, or how to model complex systems in the absence of quantitative information. In Plant Systems Biology (pp. 207-225). Humana Press. • Albert, I., Thakar, J., Li, S., Zhang, R., & Albert, R. (2008). Boolean network simulations for life scientists. Source code for biology and medicine, 3(1), 1-8. • Bornholdt, S. (2008). Boolean network models of cellular regulation: prospects and limitations. Journal of the Royal Society Interface, 5(Suppl 1), S85-S94.

39. References 2 • Hoffmann, G. W. (2008). Immune Network Theory. Monograph. URL: www. physics. ubc. ca/~ hoffmann/ni. html. • Reece, J. B., Urry, L. A., Cain, M. L., Wasserman, S. A., Minorsky, P. V., and Jackson, R. B. (2010). Campbell Biology (9th Edition). Benjamin Cummings, 9 edition. • Kauffman, S. A. (1969). Metabolic stability and epigenesis in randomly constructed genetic nets. Journal of theoretical biology, 22(3), 437-467. • Greil, F. (2009). Dynamics of Boolean networks (Doctoral dissertation, TU Darmstadt). • Russell, S. (2009). Artificial intelligence: A modern approach author: Stuart russell, peter norvig, publisher: Prentice hall pa. • "Rule 30." -- from Wolfram MathWorld. N.p., n.d. Web. 10 Mar. 2014.