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The Immune System as a
Complex Adaptive System: A
RePast Simulation of the AntiViral Immune Response
Virginia A. Folcik, Ph.D.
[email protected]
Charles G. Orosz, Ph.D.
[email protected]
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,
No leaders, no rule books, no blueprints
Network of autonomous peers,
each influencing the others
Each are thoughtless slaves to environmental cues
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.
Leukocytes always operate “en masse” as leukocyte swarms.
Immune responses are leukocyte swarm functions.
Leukocyte swarm functions are unpredictable
(due to changing local conditions).
Agents
•
•
•
•
•
•
•
Parenchymal 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
Dendritic Cells
Natural Killer Cells
Macrophages
PK1 (Heat Shock Protein, a stress signal), Virus
DC1 (Pro-inflammatory)
DC2 (Anti-inflammatory)
CK1 (IFN-g), Apoptotic Bodies
MO1 (Pro-inflammatory)
MO2 (Anti-inflammatory)
Ab1 (cytotoxic)
Ab2 (targeting)
B Cells
T Cells
MK1 (I
MK2 (
T1 (Pro-inflammatory)
T2 (Anti-inflammatory)
Necrosis factors, complement
CK1 (I
CK2 (T
Design: Three zones of activity
Blood3:Equivalent
Zone
Blood Equivalent
Zone1:1: Tissue
Tissue Equivalent
Equivalent
Zone
Zone 2: LN 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?
What happens when particular agents or signals
are excluded?
Control
Immune Win
Time to Eliminate
Infected Cells
100%
117.9 +/- 17.4
No Dendritic Cells
0%
NA
No Antibodies
0%
NA
No Macrophages
0%
NA
No T Cells
0%
NA
No T1 Cells
30%
No T2 Cells
100%
121.8 +/- 15.4 (NS)
No NK Cells
100%
171.8 +/- 21.6 (p < .0005)
159 +/- 16.1 (p < .001)
What happens when the initial number of
Dendritic Cells is varied?
% immune win
100
350
300
80
250
60
200
150
40
100
20
50
0
0
0
1
5
10
15
number of DC
20
50
time to eliminate
infected cells
400
Highly Variable Behavior of T Cell Populations
Four consecutive runs with the same parameter settings
T0
T0
T1
Win
Lose
T2
T2
T0
T
2
T1
T1
T0
Win
T1
T2
Lose
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
Does the addition of memory cells
enhance the simulated immune
response?
No Memory
Memory (10 cells)
Memory (50 cells)
117.9 +/- 17.4
107.6 +/-13.1
(p < .025)
100.6 +/- 6.5
(p < .0005)
Time to Appearance
of Antibody in Tissue
32.2 +/- 9.9
23.9 +/- 8.7
(p < .01)
18.7 +/- 6.9
(p < .0005)
Time to Appearance
of T1 Cells in Tissue
23.8 +/- 5.2
19.6 +/- 0.7
(p < .0005)
21.5 +/- 3.6
(p < .05)
Time to Eliminate
Infected Cells
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.
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.
Conclusion:
The immune simulator behaves as a complex, adaptive system.