Marc Hütt (Bremen, Germany)

At the core of systems thinking in Biology is the concept of networks. This is not only true for the obvious example of the brain, but also for a single cell, where a wide range of empirical observations – about interacting genes, interacting proteins, biochemical reactions – all can be summarized in the mathematical language of nodes and links.

Simple models (often called 'toy models' or 'minimal models') of dynamics in networks can help relate network architecture to biological function. In fact, beyond networks, our understanding of complex systems is shaped by simple models. From avalanches to the notion that complexity can be found at the boundary between regularity and chaos – simple models have helped us to separate the universal from the specific in a range of complex systems.

Here I will discuss how simple models help us understand dynamics in networks using three examples: (1) Self-organized excitation waves in networks. (2) Networks as structural models to interpret high-throughput data in Biology and Medicine. (3) The digital-analog duality in biology: how network and non-network mechanisms jointly shape biological data and hence systemic function.