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Invited speakers |
- Daniel T. Gillespie
- Mark Girolami
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Invited speakers for CMSB 2007
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| Daniel T. Gillespie
(Dan T Gillespie Consulting, Castaic, California) |
| Title: Stochastic Chemical Kinetics |
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Abstract:
The time evolution of a well-stirred chemically reacting system
is traditionally modeled by a set of coupled ordinary differential equations
called the reaction rate equation (RRE). The resulting picture of
continuous deterministic evolution is, however, valid only for infinitely
large systems. That condition is usually well approximated in laboratory
test tube systems. But in biological systems formed by single living cells,
the small population numbers of some reactant species can result in
dynamical behavior that is noticeably discrete rather than continuous, and
stochastic rather than deterministic. In that case, a more physically
accurate mathematical modeling is obtained by using the machinery of Markov
process theory, specifically, the chemical master equation (CME) and the
stochastic simulation algorithm (SSA). After reviewing the theoretical
foundations of stochastic chemical kinetics, we will describe a way to
approximate the SSA by a faster simulation procedure, and then show how this
way also provides a logical bridge between the CME/SSA description and the
RRE description.
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| Mark Girolami
(Department of Computing Science, University of Glasgow) |
| Title:
The Convergence of Mechanistic and Statistical Modelling within
Systems Biology |
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Abstract:
Statistical inferential modelling plays a foundational role in most
aspects of computational biology whereas mechanistic computational
modelling dominates the research advances being made in systems biology.
However there are a number of fundamental questions which require the
principled unification of both of these modelling paradigms thus
enabling inference over and between competing model hypotheses. This
talk will illustrate the potential of the synthesis of these modelling
paradigms by way of two important examples, the identification of
prognostic signatures from breast cancer data and the model-based
discrimination of plausible biochemical pathway topologies describing
the ERK signalling pathway.
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