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

利用离散过程通信理论分析分子网络中的信息处理

• 批准号: 240733145
• 负责人: Professor Dr.-Ing. Steffen Schober
• 金额: --
• 依托单位: Fakultät Informatik und Informationstechnik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2013
• 资助国家: 德国
• 起止时间: 2012-12-31 至 2014-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/240733145?language=en
• 关键词: Analysis; Information; Processing; Molecular; Networks

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.

拟议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组成部分。我们想要了解对这些系统施加的基本限制，并想要进一步深入了解随机机制在生命系统中的功能作用。这将揭示分子网络是否已经进化到最优的信息处理能力的问题。特别是，我们将探索在简单的反馈和前馈环路上可以传输的信息量的界限，以获得生物上有意义的参数选择。特别令人感兴趣的是信息传输的可靠性与其成本之间的权衡，例如，根据信令分子的出生率来衡量。分析将基于Lestas et最近应用的泊松通信理论的概念。艾尔[自然，卷467(7312)，2010]}来推导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和布尔函数的傅里叶分析结果来进一步扩展，布尔动力学已经成功地用于确定性模型。