# Colloquium: Modeling the dynamic repertoire of biological networks reveals structure - function connections

## Main Content

My group is collaborating with wet-bench biologists to develop and validate predictive models of various biological systems, from the molecular to the ecological level. Over the years we found that discrete dynamic modeling (e.g. Boolean modeling) is very useful in synthesizing qualitative interaction information into a predictive model. The dynamic attractors of these models can be directly related to the real system’s behaviors. We developed an efficient method to determine the attractor repertoire of a Boolean model based on an integration of the regulatory logic into an expanded network. Specific strongly connected components of this expanded network, called stable motifs, can maintain an associated state regardless of the rest of the network, and thus represent points of no return in the dynamics of the system. Our group's current work generalizes the concept of stable motif to systems with multi-level or continuous variables. We have shown that control of (a subset of) stable motifs can guide the system into a desired attractor. Such attractor control can form the foundation of therapeutic strategies on a wide application domain. I will illustrate such applications in our model of a cell fate change that represents the first step toward cancer metastasis. Several model-predicted therapeutic interventions to block this cell fate change were validated experimentally.