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?