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

拟议的研究项目的总体目标是使用通信理论模型分析生化信息处理网络的基本构建模块。我们希望了解这些系统所受到的基本约束，并希望进一步了解随机机制在生命系统中的功能作用。这将有助于阐明分子网络是否已朝着最佳信息处理能力的方向进化。特别是，我们将探索通过简单的反馈和反馈可以传输的信息量的界限-前向循环用于生物学上有意义的参数选择。特别感兴趣的是信息传输的可靠性和它的成本之间的权衡，例如，在出生率方面的信号分子。分析将是基于泊松通信理论的概念，最近已应用Lestas等。[Nature，vol 467（7312），2010]}，以导出生物系统中噪声抑制的基本下限。这将通过类似于布尔动力学的概念（已经成功地用于确定性模型）和布尔函数的傅立叶分析结果来进一步扩展。

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.