Analysis of Information Processing in Molecular Networks Using the Communication Theory of Discrete Processes

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The general goal of the proposed research project is to analyse basic building blocks of biochemical information processing networks using a communication theoretical model. We want to understand the fundamental constraints imposed on these systems and want to gain further insights into the functional role of stochastic mechanisms in living systems.This will shed light on the question whether molecular networks have evolved towards optimal information processing capabilities.In particular, we will explore bounds on the amount of information that can be transmitted over simple feedback and feed-forward loops for a biological meaningful choice of parameters. Of special interest is the trade-off between the reliability of informationtransmission and its costs measured, for example, in terms of the birth rate of signalling molecules.The analysis will be based on concepts of Poisson communication theory that have been recently applied by Lestas et. al. [Nature, vol 467 (7312), 2010]}to derive fundamental lower bounds on noise suppression in biological systems.This will be further extended by concepts similar to Boolean kinetics, already used successfully for deterministic models, and Fourier analytical results for Boolean functions.