Speaker
Description
Pluripotent stem cells have the capacity to differentiate into all the primary germ layers represented in embryos. Individual cells process information from their environment through biophysical and biochemical cues in order to determine their cell fate (i.e., the germ layer into which they will differentiate). However, cell autonomous decision-making does not fully account for the organizational features associated with developmental patterning. In this work, we investigated the role of intercellular/intracellular signaling and morphogen-based chemotaxis in the context of cell fate decisions. Along with in vitro experiments, we developed an ensemble of multiscale, agent-based models which represent key mechanisms (e.g., signaling and cell division) using discrete mathematical models (e.g., Boolean networks and Markov chains, respectively). The goal of this type of modeling is to connect the local interactions with the population-level patterning that we observed in microscopy images. We also used persistent homology to generate multiscale, topological descriptors (i.e., persistence landscapes) from both microscopy images and model simulations. By comparing descriptors from in vitro and in silico experiments, we were able to perform model selection and to improve model predictions of emergent organization under a variety of differentiation conditions.
