- 12:30 PM

Predicting the Evolutionary Landscape of Multicellular Phenotypes

Anand R. Asthagiri, Chemical Engineering, California Institute of Technology, 1200 E. California Blvd MC 210-41, Pasadena, CA 91125

Networks of biological signals guide cells to form multicellular patterns and structures.  Understanding the design and function of these complex networks is a fundamental challenge in developmental biology and has clear implications for biomedical applications, such as tissue engineering and regenerative medicine.  Signaling networks are composed of highly interconnected pathways involving numerous molecular components. Precisely to what extent multicellular structures are susceptible to quantitative variations in underlying signals and to what extent Nature has utilized this mechanism of “quantitative diversification” during evolution are unclear.  I will describe a computational framework that we have developed to explore and to quantify the multicellular diversity that emerges from signaling perturbations.  We have applied this method to study vulval development in C. elegans.  The approach is not only effective in predicting the molecular genetics of multicellular patterning, but also gauges the capacity of this signaling network for creating phenotypic diversity.  In fact, model predictions strongly correlate to multicellular phenotypes observed across ten species related to C. elegans.  These results suggest that systems-level modeling can shed insight into the evolutionary trajectories of regulatory networks that gave rise to divergent multicellular phenotypes.