The Evolutive Adaptation of the Transcriptional Information Transmission in Escherichia coli

进化是生物体通过永久性的基因改变使其适应各自环境的过程，这一过程是通过随机突变和选择最合适的个体来进行的。一个根本的问题是理解一群生物体如何适应其环境。突变导致细胞内信息通道从转录因子到基因活性和代谢通量的改变。因此，传播理论方法是最有前途的。该项目的主要目标是以大肠杆菌种群为例，对适应性进化过程中的细胞内信息交换进行信息论表征和分析。细胞群体的信息论模型是一个复杂的通信系统，其中输入和输出都是随机变量，即转录因子活性、基因表达和代谢通量。在第一个资助期，我们重点关注代谢网络的适应性，在第二个资助期，我们重点关注转录网络在恒定环境条件下的适应性。现在我们将分析其在不断变化的环境条件下的适应性。种群可以以不同的方式对变化的环境条件做出反应(转录调节或分解成亚种群)。利用细胞内信息传递的信息论模型，可以估计在长期变化的环境条件下的最佳行为。模型预测将使用ISYS和IMB建立的进化适应平台在大肠杆菌实验中得到验证。因此，将引入交替供氧和报告蛋白标记的大肠杆菌菌株的适应性进化实验。进一步的实验描述了两个互惠的大肠杆菌菌株的构建。互惠或互惠的利他主义是建立在一个综合信息级联的基础上的。在这里，自适应进化可以用来研究人工感觉系统的优化。

Evolution is the process of the adaptation of organisms to their respective environment by permanent genetic alterations, which proceeds by stochastic mutations and selection of the fittest individuals. One fundamental issue is the understanding how a population of organisms adapts to its environment. Mutations lead to a change of the intracellular information channels from transcription factors to gene activities and metabolic fluxes. Therefore, a communication theoretic approach is most promising. The main goal of this project is the information theoretic characterisation and analysis of the intracellular information exchange during adaptive evolution using the example of Escherichia coli populations. An information theoretic model of a cell population is a complex communication system where the inputs and outputs are stochastic variables, namely, transcription factor activities, gene expression, and metabolic fluxes. In the first funding period we focused on the adaptability of the metabolic network and in the second funding period on the adaptability of the transcriptional network under constant environmental conditions. Now we will analyse the adaptability under changing environmental conditions. Populations can react to alternating environmental conditions in different ways (transcriptional regulation or disintegration into subpopulations). Using an information theoretic model of intracellular information transfer, optimal behaviour under long-lasting changing environmental conditions are to be estimated. Model predictions will be verified in E. coli experiments using the platform for evolutive adaptation established by the ISYS and IMB. Therefore, an adaptive evolution experiment with alternating oxygen supply and reporter protein-tagged E. coli strains will be introduced. A further experiment describes the construction of two mutually benefiting E. coli strains. The mutual benefit or reciprocal altruism is based on a synthetic information cascade. Here, adaptive evolution can be used to study the optimisation of an artificial sensory system.