This document summarizes a study that used an agent-based model (ABM) to simulate the process of T cell differentiation. The ABM modeled T cells as agents that move through different states, including motion, survival, death, growth, proliferation, and differentiation. Naive T cells were stimulated by antigens and expressed interleukin-2 (IL-2) receptors. Binding of IL-2 allowed proliferation and differentiation into effector T cells. The ABM found that after 48 hours, there were 556 helper T cells and 183 regulatory T cells, totaling 739 effector cells. This captured the typical T cell doubling time of 24-36 hours and could help understand factors driving T cell development.

1. Modeling the Process of T Cell Differentiation
Ousmane Mbaye1, 2
1Department of Electrical & Computer Engineering, College of Engineering, University of Massachusetts, Amherst,
MA 01003
E-mail: ombaye@student.umass.edu
2Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh,
PA 15260
Abstract
T cells play an important role in an adaptive immune
response. Thus, understanding the process through
which they differentiate from naïve to effector cells
is valuable. In this project, we use NetLogo to build
an agent based model (ABM) in order to capture the
dynamics of T cell survival, differentiation, growth,
proliferation, and death. We start with a population
of naïve cells which are stimulated by their specific
antigens. Then, these activated cells express the
growth factor known as interleukin-2 (IL-2) and a
part of the high-affinity IL-2 receptor, known as
CD25, which allow them to bind with the IL-2
molecules present in the environment. After a
sufficient number of bonds, T cells proliferate and
differentiate into active effector cells, which can act
upon their target cells. An ABM allows for better
understanding of the T cell dynamics and it can be
used as a substitute for experimentation.
Introduction / Background
The purpose of this project is to use a computer
program to simulate T cells differentiation. To
achieve that goal T cells are modeled as mobile
agents interacting between them and with their
environment filled with antigen and IL-2. During the
process toward differentiation, T cells change their
behaviors and go through different states which
are:
 Motion
 Survival
 Death
 Growth
 Proliferation
 Differentiation
Using the agent based modeling environment
NetLogo, each state is implemented as a procedure
which is run by a main procedure at the start of the
simulation.
Methods
Results
1. Setup
The simulation takes place into the world which is
divided up into a grid of patches of equal size.
Each patch is a square of size 10 pixels. At start
up antigens (Ag) and naïve cells are randomly
distributed on patches.
 Round blue agents represent naïve cells
 Yellow patches contain antigens
 White patches do not contain antigens
2. Motion, Activation, and IL-2 Secretion
 Cells move around the world
 Simultaneous presence of antigen and a cell in a
patch is sufficient for cell activation
 The number of times a cell encounters antigens
determines its fate: inactive, TH, or Treg
 Red agents represent TH cells producing IL-2
 Green agents represent Treg cells
3. Growth, Proliferation, and Differentiation
 Activated cells express high affinity IL-2
receptors
 IL-2 receptors bind IL-2 molecules
 When the number of bonds is above a given
threshold, cells proliferate and differentiate into
effector cells
 Cells grow, proliferate, and differentiate into
effector cells: blue = Th and green = Treg
Number of Effector T cells over Time
 Antigen density on patches: 60%
 Number of naïve cells at start up: 200
 Threshold of bonds between IL-2 and its
receptors : 25, 000
 Number of TH cells 48 hours after
stimulation: 556
 Number of Treg cells 48 hours after
stimulation : 183
 Total number of effector cells: 739
Conclusion
 The simulation captures the fact that the doubling
time for T cells is between 24 and 36 hours on
average.
 A better understanding of the biology- IL-2
production and uptake; factors that drive T cells
toward the Th or Treg type- will improve the
predictions of the model.
 In NetLogo the world contains a limited number of
patches: this is a limiting factor for modeling a very
important cell population or a long simulation;
decreasing the patches’ size can increase their
number, but in that case the cells are so small that
they become very difficult to see.
Acknowledgments
 Professor James R. Faeder: Department of
Computational and Systems Biology, School of Medicine,
University of Pittsburgh, PA
 Dr. Natasa Miskov‐Zivanov: Department of Computational
and Systems Biology, School of Medicine, University of
Pittsburgh, PA
 Professor Penelope A. Morel: Department of Immunology,
School of Medicine, University of Pittsburgh, PA
 Professor Robert Parker: Department of Chemical and
Petroleum Engineering, University of Pittsburgh, PA
References
1. Busse et al. Competing feedback loops shape
IL-2 signaling between helper and regulatory T
lymphocytes in cellular microenvironments.
PNAS [Online] 2010.
2. Murphy, K.; Travers, P.; Walport, M. Janeway’s
Immunobiology, 7th ed.; Garland Science:
New York and London, 2008; pp 1-38; pp343-
350.
The 2012 Systems Medicine REU Program at Pittsburgh University is supported by the
National Science Foundation under Grant NSF EEC #1156889