The Immune System as a Complex Adaptive System: A RePast Simulation of the Anti- Viral Immune Response Virginia A. Folcik, Ph.D. Charles G. Orosz, Ph.D. The Department of Surgery/Transplant, The Ohio State University College of Medicine and Public Health

The Immune System as a Complex Adaptive System: A RePast Simulation of the Anti-Viral Immune Response. Virginia A. Folcik and Charles G. Orosz, Department of Surgery/Transplant, The Ohio State University College of Medicine and Public Health The immune system is a prime example of a complex adaptive system, with individual cells that follow rules for behavior based upon detection of signals and contacts with other cells in the environment. We have created a simulation of a human anti-viral immune response using the RePast software framework. The agent-based simulation includes three windows that represent a generic tissue site with parenchyma that becomes infected with virus, a lymph node site with cells that can become activated to fight the viral infection, and the peripheral blood that carries the responding immune cells and antibodies back to the site of infection. The simulation uses seven agent types and twenty signals to represent Parenchymal Cells, B-Cells, T-Cells, Macrophages, Dendritic Cells, Natural Killer Cells and the virus, and pro- and anti-inflammatory cytokines, chemokines and antibodies that such cells use to communicate with each other. The numbers of agents present as well as the quantity and types of signals present depend upon rules for proliferation and the release of cytokines that the agent types follow. Individual agents have various states, migrate from one window to another and live or die as the rules for their behavior dictate. A typical run of the simulation involves the entry of initial conditions (ratios of immune cell types), then the execution of the simulation during which the numbers of agents and quantities of signals are recorded. Given sufficient time, the outcome of a run may be either that the virus infects all of the parenchymal cells resulting in the death of the tissue (a viral "win") or the elimination of the virus and all virally infected cells with regeneration of healthy cells and restoration of the tissue to equilibrium conditions (an immune system "win"). Consistent with the theoretical properties of a complex system, our experiments have found initial conditions that always produce the same win/loss results, but the profiles of cell proliferation and signal production that occur are unique for every run of the simulation. Other initial conditions have been found that produce varying win/loss ratios. We plan to be able to use our simulation to explore formative patterns of agent behavior that develop within a complex adaptive system, to evaluate how information is used for decision making as responses evolve, and to develop methods of generating and evalulating simulator data that can be used to identify the strengths and weaknesses of clinical and experimental tools that are currently in use.

Leukocytes are more like ants than humans Even human leukocytes are inhuman Careless about needs or rights of individuals Individuals are insignificant and expendable No recognized individuality No independent thought or creativity Colony rules prevail; Human social rules are irrelevant, Network of autonomous peers, each influencing the others No leaders, no rule books, no blueprints Each are thoughtless slaves to environmental cues

Leukocytes always operate “en masse” as leukocyte swarms. Colony Rules for Leukocytes There are unlimited numbers of several different types of leukocytes. Each individual leukocyte operates as an independent entity. Each leukocyte has a finite set of genetically defined behavior patterns. All leukocytes can monitor and integrate many environmental signals, including those made by other leukocytes (pheromone equivalent). Each leukocyte responds predictably to defined patterns of environmental signals. Immune responses are leukocyte swarm functions. Leukocyte swarm functions are unpredictable (due to changing local conditions).

Agents Parenchymal CellsParenchymal Cells impart tissue function Dendritic Cells tissue surveillance, antigen presentation Macrophages scavenging, antigen presentation T Cells lymphocytes, cell mediated immunity Natural Killer Cells kill stressed cells B Cells lymphocytes, humoral immunity (Antibodies) Portals blood vessels, lymphatic ducts

Agents and the Signals That They Produce Parenchymal Cells Parenchymal Cells PK1 (Heat Shock Protein, a stress signal), Virus Dendritic Cells Macrophages T Cells Natural Killer Cells CK1 (IFN-  ), Apoptotic Bodies B Cells DC1 (Pro-inflammatory) DC2 (Anti-inflammatory) MO1 (Pro-inflammatory) MK1 (IL-12) MO2 (Anti-inflammatory) MK2 (IL-10) T1 (Pro-inflammatory) CK1 (IFN-  ) T2 (Anti-inflammatory) CK2 (TGF-  ) Ab1 (cytotoxic) Ab2 (targeting) Necrosis factors, complement

Design: Three zones of activity Zone 1: Tissue Equivalent Zone 2: LN Equivalent Blood Equivalent Zone 3: Blood Equivalent Zone 1: Tissue Equivalent

Initial Studies: Characterization How do major changes in conditions affect the outcome? Given the same initial conditions, how reproducible is the outcome? Does the immune simulator behave as a complex system?

Immune Win What happens when particular agents or signals are excluded? Time to Eliminate Infected Cells Control 100% / No Dendritic Cells 0% NA No Antibodies 0% NA No Macrophages 0% NA No T Cells 0% NA No NK Cells 100% / (p <.0005) No T1 Cells 30% 159 +/ (p <.001) No T2 Cells 100% / (NS)

What happens when the initial number of Dendritic Cells is varied?

Highly Variable Behavior of T Cell Populations Multiple runs with the same parameter settings do not necessarily yield the same outcome Even with locked parameter settings, the pattern of agent activity always differs for each run Win T0 T1 T2 T0 T1 T2T2 Lose T1 T0 T2 Lose Win T0 T1 T2 Four consecutive runs with the same parameter settings

Does the addition of memory cells enhance the simulated immune response? No Memory Memory (10 cells) Memory (50 cells) Time to Eliminate / / /- 6.5 Infected Cells (p <.025) (p <.0005) Time to Appearance / / /- 6.9 of Antibody in Tissue (p <.01) (p <.0005) Time to Appearance / / /- 3.6 of T1 Cells in Tissue (p <.0005) (p <.05)

Observations: Initial conditions exist that always produce the same (win/loss) results. Every run of the simulator has a unique profile of cell proliferation and signal production. Initial conditions exist that produce varying win/loss ratios. Conclusion: The immune simulator remains cohesive when faced with change, ie. it contiues to function. The enhancement of the simulated immune response by “memory cells“ demonstrates the capability to learn. The activity of the immune simulator adapts to major changes in agent profiles. The immune simulator behaves as a complex, adaptive system.