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Modeling T-Cell Activation Using Visual Formalism
by Evren SAHIN11/26/2002
Outline
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;sinceThe definition of reactive systems suits
biological systems at different levels… Specially cell biology, immunology
Introduction
Visual formalism provides a clarity powerful statecharts
purpose:“Statechart visual formalism on modeling and analysis of a biological system”
What is this Biological System?
Basically, immune system so called T-Cell
Activation (coming soon)
Why modeling Cell combination of
inner states Signal transductions
change cell’s state
Cell Cycle
G0: "resting" stage the cell is not actively dividing
G1: Gap 1 & Gap 2 protein and RNA synthesis growth and preparation of the
chromosomes for replication
S: Synthesis the period of DNA synthesis
M: Mitosis D: Division nucleus and then the rest of
the cell divides
Cell Cycle
An Interactive Animation fromwww.cellsalive.com
T-Cell Activation
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.
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 “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)
T-Cell to G1 Synthesis of IL-2
a protein causing infection-fighting cells multiply and mature
Synthesis of IL-2 receptor
2. Binding of IL-2 to the receptor Triggers progression through rest of the
cycle
Proliferation
Raising questions:
Where is cell? What other entities does it encounter? Which kind of signals does it receive? What kind of outputs does it produce? An adequate modeling
language How to differentiate between outside must solve all
these and must be and inside signals? clear as much as
possible! How to focus on different levels of
this process? How to describe dependent and independent states of T-cell?
STATECHARTS: A Visual Formalism For Complex Systems.
Very much fits to our needs.
extends conventional diagrams with essentially 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.
States and events for describing dynamic behavior
State-transition diagrams : formal mechanism
However;
too naive for a complex system unmanageable, exponential grow up unrealisticetc…
STATECHARTS: A Visual Formalism For Complex Systems.
To be useful;
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
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
clustering was bottom-up reverse (top-down) refines abstractions zoom-in zoom out
Entrances
suppose A is default to enter A,B,C group possible representations:
(iii) D is default among D,B and A is default among A,C
For Figure 1For Figure 2 For Figure 2(alternative)
Conditional Entrances
entering a group of states through a condition
Orthogonality
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.
event a(alpha) triggers BC and FG simultaneously (B,F)(C,G) synchronization
event u(mu) triggers only FE independence
Orthogonality- cont.
AND-free equivalent has 6 states (2*3) with two components each having 1000 states???
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 ActivationHere is a basic model of the activation.
Dynamics of T-Cell Activation 3 orthogonal components of behavior of T-Cell
Immunological State (Active or not) Phase in the cell cycle Anatomical location
Dynamics of T-Cell Activation
Immunological State
Active and non-Active
no cell cycle no proliferation has occurred T-Cell still Naïve
otherwise active division process newly born cell is active progeny of an activated parent
Dynamics of T-Cell Activation
non-Active1) Naive no meet by an antigen2) Standby met antigen3) Anergic could not get co-
simulation4) Memory resting after
clearing antigen5) IL2 Production produce
IL-2 and its binder. Binding leads full activation
ActiveProliferation (in cell cycle)Differentiation (here)
Dynamics of T-Cell Activation
Cell Cycle Control
Naive G0
Initial encounter with specific antigen in presence of co-stimulatory signal From GO to G1 by triggering
EnterCellCycle()
Binding leads to S stage (also as stated earlier leads active state)
If no death signal while in S rest of cycle depending on timeout
Results 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)
came up with an unexpected behavior 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
found that Active to Memory down-regulates IL-2 Receptor
Conclusion/Comment
simulations allow us compare model dynamics with actual experimental data
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?