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Activity Number: 130
Type: Contributed
Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #316134
Title: Intrinsic Noise in Nonlinear Gene Regulation Inference
Author(s): Chao Du* and Wing Hung Wong
Companies: University of Virginia and Stanford University
Keywords: Intrinsic Noise ; Gene regulation Model ; Kolmogorov backward equation ; Markov Process ; Nonlinear Model ; Genetic Toggle Switch
Abstract:

Cellular intrinsic noise plays an essential role in the regulatory interactions between genes. Although a variety of quantitative methods are used to study gene regulation system, the role of intrinsic noises has largely been overlooked. Using the Kolmogorov backward equation (master equation), we formulate a causal and mechanistic Markov model. This framework recognizes the discrete, nonlinear and stochastic natures of gene regulation and presents a more realistic description of the physical systems than many existing methods. Within this framework, we develop an associated moment-based statistical method, aiming for inferring the unknown regulatory relations. By analyzing the observed distributions of gene expression measurements from both unperturbed and perturbed steady-states of gene regulation systems, this method is able to learn valuable information concerning regulatory mechanisms. This design allows us to estimate the model parameters with a simple convex optimization algorithm. We apply this approach to a synthetic system that resembles a genetic toggle switch and demonstrate that this algorithm can recover the regulatory parameters efficiently and accurately.


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