Transcriptional regulatory network refinement and quantification through kinetic modeling, gene expression microarray data and i

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. Results: Our approach yields more physicochemical information that compliments the results of network structure delineation methods, and thereby can serve as an element of a comprehensive TRN discovery/quantification system. The most probable TF time courses and values of...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Abdallah Sayyed-Ahmad, Kagan Tuncay, Peter J. Ortoleva
• Keywords: BMCBI 2007, Gene Expression Microarray, Tf Time Course, Time Course