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Transcriptional regulatory network refinement and quantification through kinetic modeling, gene expression microarray data and information theory
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Date
2007-01-23
Author
Sayyed-Ahmad, Abdallah
Tuncay, Kağan
Ortoleva, Peter J.
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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Background: Gene expression microarray and other multiplex data hold promise for addressing the challenges of cellular complexity, refined diagnoses and the discovery of well-targeted treatments. A new approach to the construction and quantification of transcriptional regulatory networks (TRNs) is presented that integrates gene expression microarray data and cell modeling through information theory. Given a partial TRN and time series data, a probability density is constructed that is a functional of the time course of transcription factor (TF) thermodynamic activities at the site of gene control, and is a function of mRNA degradation and transcription rate coefficients, and equilibrium constants for TF/gene binding.
Subject Keywords
Biochemistry
,
Applied Mathematics
,
Molecular Biology
,
Structural Biology
,
Computer Science Applications
URI
https://hdl.handle.net/11511/34332
Journal
BMC BIOINFORMATICS
DOI
https://doi.org/10.1186/1471-2105-8-20
Collections
Department of Civil Engineering, Article