Literature DB >> 16730024

Uncovering a hidden distributed architecture behind scale-free transcriptional regulatory networks.

S Balaji1, Lakshminarayan M Iyer, L Aravind, M Madan Babu.   

Abstract

Numerous studies in both prokaryotes and eukaryotes have shown that, under standard growth conditions, less than 20% of the protein-coding genes are essential for survival. This suggests that biological systems have evolved to have a high degree of robustness to mutational disruptions that can affect the majority of their genes. This mutational robustness could arise either due to redundancy, i.e. direct backup, or due to distributed architecture, i.e. indirect backup where multiple genes contribute to the functioning of a process in the system. Despite clear evidence for direct backup, the prevalence of indirect backup is poorly understood. In this study, we reveal the existence of a hidden distributed architecture behind the scale-free transcriptional regulatory network of yeast by applying a unique network transformation procedure and show that the network is tolerant even to mutations that disrupt regulatory hubs. Contrary to what is generally accepted, our observation that hubs can be lost or replaced in evolution suggests that this hidden distributed architecture behind scale-free networks protects the overall transcriptional program of the organism from mutations affecting major regulatory hubs. We show that the distributed architecture has been provided by an unexpectedly large number of coordinating partners for any regulatory protein. On the basis of these findings, we propose that the existence of such architecture can allow organisms to explore the adaptive landscape in changing environments by providing the plasticity required to reprogram levels of expression of specific genes that may enhance survival. Thus, an "over-engineered" backup system in the form of distributed architecture is likely to be a major determinant of the "evolvability" of the gene expression in organisms faced with environmental diversity.

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Year:  2006        PMID: 16730024     DOI: 10.1016/j.jmb.2006.04.026

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  33 in total

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2.  Ordered cyclic motifs contribute to dynamic stability in biological and engineered networks.

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3.  Analysis of diverse regulatory networks in a hierarchical context shows consistent tendencies for collaboration in the middle levels.

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4.  Network modelling of gene regulation.

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5.  Exploiting the determinants of stochastic gene expression in Saccharomyces cerevisiae for genome-wide prediction of expression noise.

Authors:  Jingjing Li; Renqiang Min; Franco J Vizeacoumar; Ke Jin; Xiaofeng Xin; Zhaolei Zhang
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6.  Insights from the architecture of the bacterial transcription apparatus.

Authors:  Lakshminarayan M Iyer; L Aravind
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Review 7.  Scale invariance in natural and artificial collective systems: a review.

Authors:  Yara Khaluf; Eliseo Ferrante; Pieter Simoens; Cristián Huepe
Journal:  J R Soc Interface       Date:  2017-11       Impact factor: 4.118

8.  Neutral forces acting on intragenomic variability shape the Escherichia coli regulatory network topology.

Authors:  Troy Ruths; Luay Nakhleh
Journal:  Proc Natl Acad Sci U S A       Date:  2013-04-22       Impact factor: 11.205

9.  Analysis of combinatorial regulation: scaling of partnerships between regulators with the number of governed targets.

Authors:  Nitin Bhardwaj; Matthew B Carson; Alexej Abyzov; Koon-Kiu Yan; Hui Lu; Mark B Gerstein
Journal:  PLoS Comput Biol       Date:  2010-05-27       Impact factor: 4.475

10.  Genomic analysis reveals a tight link between transcription factor dynamics and regulatory network architecture.

Authors:  Raja Jothi; S Balaji; Arthur Wuster; Joshua A Grochow; Jörg Gsponer; Teresa M Przytycka; L Aravind; M Madan Babu
Journal:  Mol Syst Biol       Date:  2009-08-18       Impact factor: 11.429

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