[PRL] Fwd: visit in Boston

[Karl Lieberherr]

Here is the abstract of Vincent's talk (Feb. 11).

-- Karl

---------- Forwarded message ----------
From: Vincent Danos <vincent.danos at gmail.com>
Date: Jan 22, 2008 5:48 PM
Subject: Re: visit in Boston
To: Karl Lieberherr <lieber at ccs.neu.edu>
Cc: Ravi Sundaram <koods at yahoo.com>


Dear karl, dear Ravi

please find my title and abstract below.

Best wishes
Vincent

Rule-based modelling of cellular signalling

[Joint work with Jerome Feret, Walter Fontana, Russell Harmer, and Jean
Krivine]

Modelling is becoming a necessity in studying biological signalling
pathways, because the combinatorial complexity of such systems rapidly
overwhelms intuitive and qualitative forms of reasoning. Yet, this
same combinatorial explosion makes the traditional modelling paradigm
based on systems of differential equations impractical. In contrast,
agent-based or concurrent languages, such as Kappa or the closely
related BioNetGen language describe biological interactions in terms
of rules, thereby avoiding the combinatorial explosion besetting
differential equations. Rules are expressed in an intuitive graphical
form that transparently represents biological knowledge. In this way,
rules become a natural unit of model building, modification, and
discussion. We illustrate this with a sizeable example obtained from
refactoring two models of EGF receptor signalling that are based on
differential equations. An exciting aspect of the agent-based approach
is that it naturally lends itself to the identification and analysis
of the causal structures that deeply shape the dynamical, and perhaps
even evolutionary, characteristics of complex distributed biological
systems. In particular, one can adapt the notions of causality and
conflict, familiar from concurrency theory, to Kappa, our
representation language of choice. Using the EGF receptor model as an
example, we show how causality enables the formalization of the
colloquial concept of pathway and, perhaps more surprisingly, how
conflict can be used to dissect the signalling dynamics to obtain a
qualitative handle on the range of system behaviours. By taming the
combinatorial explosion, and exposing the causal structures and key
kinetic junctures in a model, agent- and rule-based representations
hold promise for making modelling more powerful, more perspicuous, and
of appeal to a wider audience.
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