Dynamics and Correlated Noise in Gene Regulation

Author: Dunlop, Mary Julia

Year: 2008

Degree: Dissertation (Ph.D.)

Advisor: Murray, Richard M.

Committee Members: Murray, Richard M.; Winfree, Erik; Elowitz, Michael B.; Phillips, Robert B.

Option: Mechanical Engineering

DOI: 10.7907/AC8V-6S05

Abstract

Gene regulatory interactions are context dependent, active in some cell types or cellular states but not in others. In this thesis we present a method for determining when a regulatory link is active given temporal measurements of gene expression. Correlations in time-series data are used to determine how genes influence each other and their causal relationships. Natural stochastic noise is shown to aid in the process of network identification by perturbing the expression of genes; the speed and direction at which the noisy signal propagates shows how the network is connected. Cross correlation functions are used to reveal time-delayed correlations.

We develop a stochastic model of gene expression and show that by measuring correlations in cellular noise, it is possible to infer network activity and temporal properties of gene regulation. Using a linearized version of the model, we introduce a method for analytically deriving cross correlation functions for arbitrary networks. These results are validated experimentally using a synthetic gene circuit in E. coli bacteria. Single-cell time-lapse microscopy is used to measure noisy expression of multiple genes over time. Extending this work to natural systems, we study feed-forward loops and determine that certain classes of feed-forward loops are more robust to noise and parameter variations that others. Noise in two naturally occurring feed-forward loops involved in galactose utilization is measured experimentally and it is shown that neither is actively regulating its target in the conditions tested.

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