RESEARCH ARTICLE
Emergent Principles in Gene Expression Dynamics
J. C. Nacher*, a, T. Ochiaib
Article Information
Identifiers and Pagination:
Year: 2011Volume: 5
First Page: 34
Last Page: 41
Publisher ID: TOBIOIJ-5-34
DOI: 10.2174/1875036201105010034
Article History:
Received Date: 18/01/2010Revision Received Date: 06/08/2010
Acceptance Date: 06/08/2010
Electronic publication date: 02/02/2011
Collection year: 2011
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Rapid advances in data processing of genome-wide gene expression have allowed us to get a first glimpse of some fundamental laws and principles involved in the intra-cellular organization as well as to investigate its complex regulatory architecture. However, the identification of commonalities in dynamical processes involved in networks has not followed the same development. In particular, the coupling between dynamics and structural features remains largely uncovered. Here, we review several works that have addressed the issue of uncovering the gene expression dynamics and principles using micro-array time series data at different environmental conditions and disease states as well as the emergence of criticality in gene expression systems by using information theory. Moreover, we also describe the efforts done to explore the question of characterizing gene networks by using transcriptional dynamics information. The combination of the emergent principles uncovered in the transcriptional organization with dynamic information, may lead to reconstruct, characterize and complete gene networks. We also discuss several methods based on simulations of a series of enzyme- catalyzed reaction routes and Markov processes as well as combination of complex network properties with stochastic theory.
