Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/4053
Title: Microarray Analysis through Transcription Kinetic Modeling and Information Theory
Authors: Sayyed-Ahmad, Abdallah
Ortoleva, Peter
Tuncay, Kagan
Keywords: DNA microarrays
Protein microarrays
Medical genetics - Methodology
Clinical biochemistry - Methodology
Kinetic theory
Issue Date: 2014
Abstract: cDNA microarray and other multiplex data hold promise for addressing the challenges of cellular complexity, disease progression and drug discovery. We believe that combining transcription kinetic modeling with microarray time series data through information theory will yield more information about the gene regulatory networks than obtained previously. A novel analysis of gene regulatory networks is presented based on the integration of microarray data and cell modeling through information theory. Given a partial network and time series data, a probability density is constructed that is a functional of the time course of intra-nuclear transcription factor (TF) thermodynamic activities, and is a function of RNA degradation and transcription rate and equilibrium constants for TF/gene binding. The most probable TF time courses and the values of aforementioned parameters are computed. Accuracy and robustness of the method are evaluated and an application to Escherichia Coli is demonstrated. A kinetic (and not a steady state) formulation allows us to analyze phenomena with a strongly dynamical character (e.g. the cell cycle, metabolic oscillations, viral infection or response to changes in the extra-cellular medium)
URI: http://hdl.handle.net/20.500.11889/4053
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