Speaker
Description
Epithelial-Mesenchymal Transition (EMT) is a central regulatory program in cancer progression that enables epithelial cells to acquire migratory and invasive capabilities. Rather than functioning as a simple binary switch, EMT often generates a spectrum of phenotypic states, including hybrid epithelial-mesenchymal phenotypes that contribute to tumor heterogeneity, collective migration, and metastatic dissemination. These phenotypic states emerge from the nonlinear dynamics of high-dimensional gene regulatory networks controlling EMT. A key question is therefore how the topology of these regulatory networks shapes the attractor landscape governing epithelial, mesenchymal, and hybrid cellular phenotypes. In our recent studies, we address this problem using a network dynamical systems framework by analyzing complex regulatory networks underlying EMT. By combining network topology analysis with dynamical systems modeling, we identify network signatures that promote multistability and stabilize hybrid phenotypes within the regulatory landscape. We further derive minimal regulatory circuits capable of reproducing key EMT-associated cell phenotypic transitions including differentiation, trans-differentiation, and reprogramming. These results provide a quantitative framework linking regulatory network topology to the geometry of cell fate plasticity in carcinomas and may help identify regulatory targets that restrict metastatic phenotypes, thereby limiting cancer progression.
