Systems biology from cell to society; Transmission dynamics in complex networks with mesoscopic organization [HBNI Th75]

Abstract

The complete mapping of human and other genomes has revealed that the remarkable complexity of living organisms is expressed by less than 30,000 protein-coding genes. Thus, the observed complexity arises not so much from the relatively few components (in this case, genes), as from the large set of mutual interactions that they are capable of generating. The focus of research in biology is therefore gradually shifting towards understanding how interactions between components, be they genes, proteins, cells or organisms, add a qualitatively new layer of complexity to the biological world. This is the domain of systems biology which aims at understanding organisms as an integrated whole of interacting genetic, protein and biochemical reaction networks, rather than focusing on the individual components in isolation. Reconstructing and analyzing biological networks, be they of genes, proteins or cells, is at the heart of systems biology. The role of such “network biology” is to elucidate the processes by which complex behaviour can arise in a system comprising mutually interacting components. While such emergent behaviour at the systems level is not unique to biology, to explain properties of living systems, such as their robustness to environmental perturbations and evolutionary adaptability, as the outcome of the topological structure of the networks and the resulting dynamics, is a challenge of a different order. As networks appear at all scales in biology, from the intracellular to the ecological, one of the central questions is whether the same general principles of network function can apply to very different spatial and temporal scales in biology. In this thesis, transmission processes on several networks occurring at different scales from cell to society is analysed in detail to show how using a network approach to study the dynamics complex biological systems can reveal unexpected features and allow new insights.