Controlling variability in gene expression with Two-Component Systems

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Alejandro Granados, Josh Smith, Tom Ellis and Reiko Tanaka

Imperial College London, United Kingdom

Cells must process information from environment using molecular interaction networks that orchestrate adequate responses often by regulating changes in gene expression. Stochastic fluctuations in different steps on the path from gene to protein generate cell-to-cell variability in the levels of expression. A current challenge in systems and synthetic biology is to understand how different molecular processes contribute to the variability observed in the phenotype and to what extent this variability is under cellular control. At the level of promoter dynamics, stochastic effects have been characterized, showing that the levels of variability in expression might be encoded in the organization of the regulatory elements in the promoter, i.e., promoter architecture. However, signaling networks and transcription factor activation dynamics, which have been shown to account for an important source of additional variation, have not been considered in these studies. In this work we seek to address the question of whether different promoter architectures can process the variation generated from the signaling network and consequently producing different population-scale phenotypes. We will focus on the family of Two-Component Systems signaling pathways as they have been extensively characterized both experimentally and theoretically. By creating and characterizing a library of semi-randomly mutated promoters regulated by the CusRS TCS in Escherichia Coli we aim to identify those motifs in the promoter architecture that significantly affect variability in expression. The library will allow us to explore the potential of a single TCS in generating different population scale responses. We will then combine these results with mathematical modeling in order to engineer promoter architectures for fine tuning population-scale expression. Our final goal is to achieve precise control on the dynamics of the proportion of cells that turn on gene expression and understand how population scale organization arises from molecular dynamics.