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Matrix Formalism to Describe Functional States of Transcriptional Regulatory Systems
Author(s) -
Erwin P. Gianchandani,
Jason A. Papin,
Nathan D. Price,
Andrew R. Joyce,
Bernhard Ø. Palsson
Publication year - 2006
Publication title -
plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.0020101
Subject(s) - gene regulatory network , computational biology , formalism (music) , boolean network , computer science , cis regulatory module , genome , gene , linear subspace , genetics , regulation of gene expression , systems biology , biology , theoretical computer science , mathematics , gene expression , algorithm , pure mathematics , promoter , boolean function , art , musical , visual arts
Complex regulatory networks control the transcription state of a genome. These transcriptional regulatory networks (TRNs) have been mathematically described using a Boolean formalism, in which the state of a gene is represented as either transcribed or not transcribed in response to regulatory signals. The Boolean formalism results in a series of regulatory rules for the individual genes of a TRN that in turn can be used to link environmental cues to the transcription state of a genome, thereby forming a complete transcriptional regulatory system (TRS). Herein, we develop a formalism that represents such a set of regulatory rules in a matrix form. Matrix formalism allows for the systemic characterization of the properties of a TRS and facilitates the computation of the transcriptional state of the genome under any given set of environmental conditions. Additionally, it provides a means to incorporate mechanistic detail of a TRS as it becomes available. In this study, the regulatory network matrix, R , for a prototypic TRS is characterized and the fundamental subspaces of this matrix are described. We illustrate how the matrix representation of a TRS coupled with its environment ( R* ) allows for a sampling of all possible expression states of a given network, and furthermore, how the fundamental subspaces of the matrix provide a way to study key TRS features and may assist in experimental design.

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