Gene-regulatory networks viewed through statistical mechanical glasses

Cell differentiation was long thought to be an irreversible process, however it is nowadays known that fully differentiated cells can be reprogrammed into a state of pluripotency, by introducing a few transcription factors. Despite its potential in regenerative medicine, no universally accepted theory exists that explains the phenomena. The purpose of this work is to provide a statistical mechanical perspective on cell differentiation and reprogramming. Using neural networks approaches, we model cell types as hierarchically organized dynamical attractors, corresponding to cell cycles, and rationalise phenomenological aspects of cell reprogramming, seen as transitioning between attractors. The mechanism for the effective interactions arising between genes is studied via a bipartite graph model, that integrates genes and transcription factors into a single network. Methods to calculate gene expression profiles are developed and important features of regulatory networks are deduced.

Presential in the seminar room. Zoom link:

https://zoom.us/j/98286706234?pwd=bm1JUFVYcTJkaVl1VU55L0FiWDRIUT09