Identification of stable genetic networks using convex programming

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Lab Papers (GRASP)
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biology computing
biotechnology
cellular biophysics
convex programming
genetics
macromolecules
biotechnology
convex programming
fundamental biological process
gene regulatory network
genetic perturbation experiment
genome-scale genetic network
mRNA concentration
micro-array experiment
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Gene regulatory networks capture interactions between genes and other cell substances, resulting in various models for the fundamental biological process of transcription and translation. The expression levels of the genes are typically measured in mRNA concentrations in micro-array experiments. In a so called genetic perturbation experiment, small perturbations are applied to equilibrium states and the resulting changes in expression activity are measured. This paper develops a novel algorithm that identifies a sparse stable genetic network that explains noisy genetic perturbation experiments obtained at equilibrium. Our identification algorithm can also incorporate a variety of possible prior knowledge of the network structure, which can be either qualitative, specifying positive, negative or no interactions between genes, or quantitative, specifying a range of interaction strength. Our method is based on a convex programming relaxation for handling the sparsity constraint, and therefore is applicable to the identification of genome-scale genetic networks.

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2008-06-11
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2023-05-17T03:10:29.000
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Copyright 2008 IEEE. Reprinted from: Zavlanos, M.M.; Julius, A.A.; Boyd, S.P.; Pappas, G.J., "Identification of stable genetic networks using convex programming," American Control Conference, 2008 , vol., no., pp.2755-2760, 11-13 June 2008 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4586910&isnumber=4586444 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
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