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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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

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Citation Formats

A. Sayyed-Ahmad, K. Tuncay, and P. J. Ortoleva, “Transcriptional regulatory network refinement and quantification through kinetic modeling, gene expression microarray data and information theory,” BMC BIOINFORMATICS, pp. 0–0, 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/34332.