Chaos, entropy and learning in high-dimensional models of gene networks

Project description

In all living organisms, a number of genes code for proteins which regulate the transcription of other genes (or themselves). This results in “gene networks”, or sets of
genes regulating each other in a sometimes very intricate way. The cross-regulations lead to a dynamical process, where genes can undergo phases of activity and rest, following each other in a way that depends on the state of
the whole system. A number of mathematical models have been proposed to describe the dynamics of gene networks. When a large number of genes are involved, as is typical in living organisms, they display complex dynamics taking place
in a high-dimensional state space.
In this project, one will consider such dynamics with a theoretical viewpoint. Relying on computational and analytical approaches, the research will aim to uncover and classify types of interesting behaviour including chaos, hysteresis/memory, or the ability to 'learn' from environmental inputs.