Abstract

My group is using network science to understand the emergent properties of biological systems. As an example, we think of cell types as attractors of a dynamic system of interacting (macro)molecules, and we aim to find the network patterns that determine these attractors. We collaborate with wet-bench biologists to develop and validate predictive dynamic models of specific systems. Over the years we found that discrete dynamic modeling (e.g. Boolean modeling) is a useful framework to connect a network's structure and dynamics. 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.