Phenotype control in biological systems

My group is using network science and dynamic modeling to understand the emergent properties of biological systems. For example, we think of cell phenotypes as attractors of a system of interacting molecules. We collaborate with wet-bench biologists to develop and validate dynamic models of the processes that underlie cellular phenotypes. We have found that network-based discrete dynamic modeling is very useful in synthesizing causal interaction information into a predictive model. We use the accumulated knowledge gained from specific models to draw general conclusions that connect a network's structure and dynamics. Such a general connection is our identification of stable motifs, which are self-sustaining cyclic interaction structures that determine trap spaces of the system's state space. We have elucidated a system's decision-making as a choice between two mutually exclusive stable motifs and have shown that the control of stable motifs can guide the system into a desired phenotype. We have recently developed the software library pystablemotifs, which implements efficient algorithms to determine and control any Boolean system's attractor repertoire. Stable motif - based phenotype control can form the foundation of therapeutic strategies on a wide application domain.