Agent Based Models of the Acute Inflammatory Response: Update on Development and Future Directions

1. Agent Based Models of the Acute Inflammatory Response:Update on Development and Future Directions Swarmfest 2004, Ann Arbor, MI May 11, 2004 Gary An, MD Department of Trauma Cook County Hospital

2. Acute Inflammatory Response (AIR) • Initial defense and repair mechanism • Specialized cellular/molecular pathways • Diffusely distributed/Tissue Nonspecific • Activation is non-specific to insult • Precedes Adaptive Immune response (self/non-self distinction=>Antibodies)

3. Systemic Inflammatory Response Syndrome/Multiple Organ Failure (SIRS/MOF) • Disease of the ICU => “Unexplored State” • Pathologic state of Acute Immune Response (AIR) • Physiologic manifestations result from endogenous mediators • Hyperinflammation vs. Immune-suppression =>Temporal and Spatial

4. Challenge of SIRS/MOF • Gap between Pathophysiology and Diagnosis • Gap between Mechanisms and Treatment • Gap between Basic Science and Clinical Implementation • Nonlinear Behavior => Complexity

5. Components Rules Locality Emergent Properties Unexpected Behavior Cells Cellular Programming Membranes/ Receptors Organ Physiology SIRS/MOF AIR as a Complex System

6. Applications of ABM to AIR/SIRS/MOF • Base Global Model • Pathophysiology • Therapeutic Interventions • Specific Disease Processes/Pathogens/Mechanism • Cutaneous and Inhalational Anthrax • Basic Science Experiment Simulation • Epithelial Permeability Model

7. ABM of Global Systemic Inflammation • Endothelial/Blood interface • Activation/Propagation of Inflammation • Endothelial Cells and White Blood Cells • Dynamics of Pathophysiology • Proto-Testing Platform for Systemic Therapies • Very Abstract!

8. Current Model of Global Inflammation

9. Validation Strategies • Agent Rules=>Transparency wrt code • Behavior of Individual wrt global response to injury=>Individual Dynamics • Behavior of Population wrt cytokine patterns=>Population Dynamics • Behavior of Population wrt outcome to intervention=>Population Response

10. Individual Response Dynamics • Four possible dynamics: • Successful healing • “Phase II” or Immune-suppressed SIRS/MOF • “Phase I” or Hyper-inflammatory SIRS/MOF • Overwhelming insult/infection • Function of degree of Initial Insult

15. Population Dynamics:Cytokine Profiles • Patterns of cytokine levels for a population at a specific IIN • 7 days simulated time • IIN generates 50% mortality • N=100 • Pattern Oriented/Qualitative (Very Large Range-not shown)

17. Population Response:Simulating Anti-inflammatory Interventions • Any mediator represented as a variable can be manipulated • Modified based on published effects • No other modifications of the ABM other than simulated intervention • Results all generated prospectively

18. List of In-Silico Experiments

19. ABM of Anthrax Infection • Modification of Base Global Model • Specific Characteristics of B. anthracis • Both Cutaneous and Inhalational Forms • Reproduce effects of Toxin-Component (Lethal Factor, Edema Factor and Protective Antigen) knockout species of B. anthracis

20. Time of Death Distributions in All Modes

23. Basic Science ABMs • Basic Science Paradigm = Linear analysis • Examine Component Sub-Systems • Improve efficiency of Basic Science experiments • Guide further investigation • Modular Components of System-wide Model

24. ABM of Epithelial Cell Permeability: Structure • Based on model of Delude • Epithelial cell culture => Grid of Epi Cell Agents • Agent rules => Tight Junction (TJ) Formation • TJ status determines permeability

25. ABM of Epithelial Cell Permeability: Results • Increased Permeability to NO/Pro-inflammatory cytokine mix • Blocked with NO scavenger/iNOS inhibitor • Matches Basic Science results • Potential Modular Model

26. Uses of ABM of the AIR • Formalize Mental Models • Functional Repository of Basic Science Information • Modular • Community-dependent • Drug Engineering • Identify targets for manipulation • Use to pre-test a planned treatment regimes => Multi-Modal regimes

27. Uses of ABM of the AIR cont. • Clinical Therapeutics Design • Patient Population Sub-stratification • Generate Cytokine Profiles => “Finer Grained” • Theoretical Tool • Mathematical characterization of system to guide future therapies • “Cross Platform” Validation

28. Future Development • Multi-Tissue Model • Directional Flow • Coagulation • Multiple Organ Failure/Support • Modular Model • Basic Science Models • Community/Web-based • “Functional Data-bank”

30. Complex Systems • Rules drive Local interactions between individual components • Feedback loops =>non-linearity • Interaction dynamics result in meta-stable structures=> Emergence • Hierarchies of Emergent properties • Non-intuitive, paradoxical behavior

31. Agent Based Modeling (ABM) • System of Components=>Agents • Agent Rule systems=>Basic Science • Populations of agents in virtual world • Runs = agent actions/interactions=> Locality • Multiple runs=Random Number Generators=> basic science experiments • Stochastic and Deterministic

32. Why use ABM to model AIR/SIRS/MOF? • Lots of information about potential agents (cells and molecules) • Process is driven by local interactions • Dynamics may be too complex for top-down modeling • Multiple possible levels of model validation • Integration of Models => Total System

33. Doing Science with ABM • In-Silico Experiments => Virtual control and experimental populations • Apply standard statistical tools • Use Pattern Oriented Analysis • Formalize mental model building/testing hypotheses • Develop Theories

34. Population Runs • Random number generators are active=> Heterogeneity • Multiple runs at a specific IIN generates a study “population” • Generates a “mortality rate” for a particular IIN (Mortality at >80% Total Damage)=>“Control Population”

35. Results of In-Silico Experiments in Sterile Mode (n=100)

36. Results of In-Silico Experiments in Infectious Mode (n=100)

37. End Oxy Deficit Distributions in All Modes

38. What ABM is not! • NOT a replacement of current techniques of scientific investigation. => “Software vs. Hardware” • NOT a clinical tool to provide a prognosis or determine a treatment course for an individual patient*

39. Translation, Synthesis and ABMs • Requires data from Basic Science • “What we look for and find out.” • Places it into Synthetic framework • “How do the pieces fit together.” • Uses Multiple Hierarchies • “Little pieces make big pieces.” • We Do This Already! • Mental Models => Software Engineering

40. “Theories of SIRS/MOF” • “Dynamic Equilibrium”=>Response is appropriate, degree is not • Concept of “anatomic containment” and “physiologic containment” • Identify “Amplifiers” of response • Importance of all aspects of the response => “If a mediator does a lot of different stuff don’t mess with it.” • Supplementation, not Blockade (WBCs smarter than ICU MDs)

41. Summary of Key Points • The Acute Inflammatory Response is a complex system that cannot be fully characterized using existing techniques. • Agent Based Modeling is well suited to modeling the Inflammatory Response. • ABM would be an useful adjunct to existing techniques of investigation.