Biological systems as complex dynamical networks
Many real-world complex systems are composed of interacting entities, where their measured activity is a result of underlying complex, usually nonlinear, dynamics. Examples of such network-based dynamical models include: biochemical, ecological and regulatory dynamics. Understanding the underlying dynamics is a key in order to control those systems. Indeed, theory of nonlinear dynamics in mathematics and statistical physics provide deep and detailed understanding of such systems. Yet, the main challenge is that most of the available data comes from snapshots originated in different individuals, and thus is considered as insufficient to extract the actual underlying dynamics, which remain unknown. I will present a novel approach to address this gap between the theory of nonlinear dynamics and the available data from dynamical systems. The approach will be demonstrated on two systems: (i) the human microbiome, the ecological community of microbes living in and on our body, and (ii) gene regulatory networks in human cells.