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Correlation and Causality in Genetic Networks
From Q-bio
Mary J. Dunlop, R. Sidney Cox, Richard M. Murray, and Michael B. Elowitz
- Abstract
- We present a method for determining the structure of a genetic network given temporal measurements of gene expression. Correlations in time series data are used to determine which 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. Mathematical models of simple genetic networks demonstrate that network inference based on correlation data is possible. A library of synthetic genetic circuits is being developed to test these predictions experimentally.
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