Toward Systems Biology

May 30 - 31, June 1, 2011


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Energy as Syntax

There is a growing interest in domain-specific modeling/programming languages in the context of systems and synthetic biology - and in the larger view, in formal approaches to complex systems, where syntax meets dynamics (and learning). We have developed the concepts of rule-based modeling of biomolecular networks and the accompanying kappa language. The approach is gaining traction in the systems and synthetic biology communities. Here is a Nov 2009 Nature feature article mentioning our approach. Here is another one (Jul 2009). Yet another one in a recent issue of Nature Methods (Feb 2011) - with a vibrant endorsement of our methods! In the realm of synthetic biology, the Edinburgh IGEM 2010 team has won the "best model" prize for a comprehensive rule-based modeling effort.

This increasing recognition is part of a broader realization that in order to address decentralized dynamics of high complexity and connectedness, one has to go beyond basic descriptive tools such as Markov chains and differential equations. New modeling situations present a diversity of structures and scales where the representational challenge can be insurmountable without abstract and structured syntaxes to describe the dynamics of interest. Part of the modeling activity gradually morphs into a sort of domain-specific programming. Now, enriching the representational apparatus of a modeling domain is not just a way to make the modeling more agile and far-reaching. In a way that has been long recognised in the context of programming, one can lean on syntactic structures to develop various analyses that would otherwise be unfeasible.

With this in mind, and in order to advance the specific modeling and analysis of biomolecular networks, I will explain how now we are trying to borrow some structuring features from biophysics and develop a modeling language where energetic and thermodynamic constraints are put front and center. The idea is to develop energy as a syntax. That is to say, to investigate how one can structure and program the dynamics of rule-based stochastic binding systems by the means of local energy functionals describing their equilibrium properties. In this new fashion of modeling, the dynamics is inferred from the statics (as in MCMC methods) and rules recede in the modeling infrastructure. This leads to less parameter-hungry modeling showing a structured interface to statistical mechanical and machine-learning techniques - and therefore, perhaps, to robust modeling with stronger explanatory power.

Vincent Danos, University of Edinburgh