The Evolutive Adaptation of the Transcriptional Information Transmission in Escherichia coli

点击翻译按钮获取中文摘要

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.