Section: Research Program

Comparative modeling

A general goal of systems biology is to acquire a detailed quantitative understanding of the dynamics of living systems. Different formalisms and simulation techniques are currently used to construct numerical representations of biological systems, and a recurring challenge is that hand-tuned, accurate models tend to be so focused in scope that it is difficult to repurpose them. We claim that, instead of modeling individual processes de novo, a sustainable effort in building efficient behavioral models must proceed incrementally. Hierarchical modeling is one way of combining specific models into networks. Effective use of hierarchical models requires both formal definition of the semantics of such composition, and efficient simulation tools for exploring the large space of complex behaviors. We have combined uses theoretical results from formal methods and practical considerations from modeling applications to define a framework in which discrete and continuous models can communicate with a clear semantics. Hierarchical models can be assembled from existing models, and translated into their execution semantics and then simulated at multiple resolutions through multi-scale stochastic simulation. These models are compiled into a discrete event formalism capable of capturing discrete, continuous, stochastic, non deterministic and timed behaviors in an integrated and non-ambiguous way. Our long-term goal to develop a methodology in which we can assemble a model for a species of interest using a library of reusable models and a organism-level “schematic” determined by comparative genomics.

Comparative modeling is also a matter of reconciling experimental data with models [4] [20] and inferring new models through a combination of comparative genomics and successive refinement [29] , [30] .