Modeling T-Cell
Activation Using Visual
Formalism
by Evren SAHIN
11/26/2002
Outline
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Introduction
What is this Biological System?
Statecharts
Model
Results and Conclusion
Introduction
From analysis (finding building blocks)
 To synthesis (integration of parts)
requires language of math
;since
The definition of reactive systems suits
biological systems at different levels…
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Specially cell biology, immunology
Introduction
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Visual formalism provides a clarity
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powerful statecharts
purpose:
“Statechart visual formalism on modeling and
analysis of a biological system”
What is this Biological
System?
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Basically, immune
system
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so called T-Cell
Activation (coming soon)
Why modeling
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Cell  combination of
inner states
Signal transductions
change cell’s state
Cell Cycle
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G0: "resting" stage
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G1: Gap 1 & Gap 2
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protein and RNA synthesis
growth and preparation of the
chromosomes for replication
S: Synthesis
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the cell is not actively dividing
the period of DNA synthesis
M: Mitosis D: Division
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nucleus and then the rest of the
cell divides
Cell Cycle
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An Interactive Animation from
www.cellsalive.com
T-Cell Activation
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T cell:
A type of white blood cell that is of crucial importance to the immune system protecting you from viral infections; helping other cells fight bacterial and fungal
infections; producing antibodies; fighting cancers; and coordinating the activities of
other cells in the immune system.
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Naive T-Cells live many years without dividing
Trying to sense changes in body
Sensing through interaction with antigen-presenting cells
GETTING READY TO THE BATTLE….
T-Cell Activation
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“Naive T-Cells that recognize their specific antigen on the surface of a
professional antigen presenting cell cease to migrate, and are activated
to proliferate and differentiate into armed effector cells.
The Signaling Process:
1. Initial encounter + (presence of co-stimulatory signal)
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T-Cell to G1
Synthesis of IL-2
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a protein causing infection-fighting cells multiply and mature
Synthesis of IL-2 receptor
Proliferation
2. Binding of IL-2 to the receptor
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Triggers progression through rest of the cycle
Raising questions:
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Where is cell?
What other entities does it encounter?
Which kind of signals does it receive?
What kind of outputs does it produce?
How to differentiate between outside
and inside signals?
How to focus on different levels of
this process?
How to describe dependent and independent
states of T-cell?
An adequate modeling language
must solve all these and must be
clear as much as possible!
STATECHARTS: A Visual Formalism For
Complex Systems.
Very much fits to our needs.
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extends conventional diagrams with essentially
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hierarchy
concurrency
Communication
compact & expressive
viewing the description at different levels
event-driven (continuously having to react external and internal stimuli )
Clear & Realistic
Formal and rigorous
STATECHARTS: A Visual Formalism For
Complex Systems.
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States and events for describing dynamic behavior
State-transition diagrams : formal mechanism
However;
too naive for a complex system
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unmanageable, exponential grow up
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unrealistic
etc…
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STATECHARTS: A Visual Formalism For
Complex Systems.
To be useful;
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modular, hierarchical, well-structured
solving exponential blow-up by relaxing on “showing
every state explicitly”
cluster states into super-state / refining super-state
into states
orthogonality
need for general transitions.
Remember the things to model T-Cell Activation
Clustering
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capturing depth and hierarchy
arrows can originate and terminate at any level
economizes arrows
arrows labeled with events; plus optional conditions
D = A XOR C
Refinement
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clustering was bottom-up
reverse (top-down) refines abstractions
zoom-in zoom out
Entrances
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suppose A is default to enter A,B,C group
possible representations:
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(iii)  D is default among D,B and A is default among A,C
For Figure 2
For Figure 1
For Figure 2(alternative)
Conditional Entrances
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entering a group of states through a condition
Orthogonality
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clustering was XOR
orthogonality: AND Decomposition
capturing the property: being in a state, the system must in all of its AND
components
dashed lines
Y is orthogonal product of A and D
entering Y  entering combination of (B,F)
Orthogonality- cont.
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event a(alpha) triggers BC and FG simultaneously
(B,F)(C,G)
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synchronization
event u(mu) triggers only FE
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independence
Orthogonality- cont.
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AND-free equivalent has 6 states (2*3)
with two components each having 1000 states???
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exponential blow-up
orthogonality is the way to avoid it
STATECHARTS
There are many
more features of
STATECHARTS
But, I guess it is better not to
get stuck with these details
and turn back to our actual
goal: creating a clear, visual
model of T-Cell Activation.
Dynamics of T-Cell Activation
Here is a basic model of the activation.
Dynamics of T-Cell Activation
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3 orthogonal components of behavior of T-Cell
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Immunological State (Active or not)
Phase in the cell cycle
Anatomical location
Dynamics of T-Cell Activation
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Immunological State
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Active and non-Active
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no cell cycle  no proliferation has occurred
 T-Cell still Naïve
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otherwise  active division process newly
born cell is active progeny of an activated
parent
Dynamics of T-Cell Activation
non-Active
1) Naive  no meet by an antigen
2) Standby  met antigen
3) Anergic  could not get cosimulation
4) Memory  resting after clearing
antigen
5) IL2 Production  produce IL-2 and
its binder. Binding leads full
activation
Active
Proliferation (in cell cycle)
Differentiation (here)
Dynamics of T-Cell Activation
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Cell Cycle Control
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Naive G0
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Initial encounter with specific antigen
in presence of co-stimulatory signal
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From GO to G1 by triggering EnterCellCycle()
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Binding leads to S stage (also as
stated earlier leads active state)
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If no death signal while in S  rest of
cycle depending on timeout
Results
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To check whether the formal representation of the model fulfills the
requirements from immunological data, model executed on Rhapsody
tool. It can translate the model to executable code and then animate
the statecharts. (not in paper)
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came up with an unexpected behavior
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could not reach steady Memory state
when from Active to Memory, right back to Active
IL-2 molecules still in the system
only after an extensive search in literature
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found that Active to Memory down-regulates IL-2 Receptor
Conclusion/Comment
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simulations allow us compare model dynamics with
actual experimental data
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so; modeling with powerful techniques such as
statecharts can help in finding open questions in
biology that can not be addresses in standard
laboratory conditions alone.
Thanks for your attention.
Any Question?