Predicting Functional Redundancy in Protein Networks

Intracellular biological networks consist of interacting proteins that together maintain a cellular process and its biological function. It is known that most of the individual proteins of such a biological network are not necessary in the sense that a knock-out mutant appears indistinguishable, at least in a laboratory environment. Redundancy might be under positive selective pressure, for example by increasing adaptability in changing environments, or it might be a neutral byproduct. Quantifying the amount of redundancy in protein networks provides a first step in understanding its evolutionary history and its implications for today’s living organisms. Furthermore, precise knowledge of redundant proteins is essential for targeted manipulations of cellular processes, including the identification of drug targets. The main focus of this IPhD project concerns the development and application of computational methods for the prediction of functional redundancy in protein networks. In particular, the project is designed as a combined experimental and computational approach for developing tools to predict redundant protein pairs in cellular protein networks, which depends on an alternating cycle between experiment and method development.

Schlagworte: Network Redundancy, RNAi, Integrative Network Biology, Statistical Learning



 Fabian Schmich

Fabian Schmich
ETH Zurich
Computational Biology Group
Mattenstrasse 26
CH - 4058 Basel

Tel.: +41 61 387 33 09