title: Information theory in systems biology. Part I: Gene regulatory and metabolic networks.
abstract: "A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory.
authors.affiliation: Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. Electronic address: zmousavian@ut.ac.ir.
authors.forename: Zaynab
authors.initials: Z
authors.lastname: Mousavian
authors.affiliation: Laboratory of Complex Biological Systems and Bioinformatics (CBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
authors.forename: Kaveh
authors.initials: K
authors.lastname: Kavousi
authors.affiliation: Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. Electronic address: amasoudin@ibb.ut.ac.ir.
authors.forename: Ali
authors.initials: A
authors.lastname: Masoudi-Nejad
mesh_terms: D000465:Algorithms; D000818:Animals; D019277:Entropy; D053263:Gene Regulatory Networks; D006801:Humans; D007257:Information Theory; D053858:Metabolic Networks and Pathways; D049490:Systems Biology; D035501:Uncertainty
keywords: Entropy and mutual information; Gene regulatory networks; Information; Metabolic networks; Uncertainty