Attractors and Democratic Dynamics

Bar‐Yam, Yaneer; Harmon, Dion; Bivort, Benjamin de

doi:10.1126/science.1163225

Cited by 79 publications

(62 citation statements)

References 15 publications

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“…In this study, we build upon the idea that depending upon the layout of regulatory edges, there are two aspects of regulation (12). In an autocracy, few top regulators influence their own set of targets directly or through a chain of influence (Fig.…”

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confidence: 99%

“…Whereas an autocracy organizes into a neat hierarchy with well-defined levels but lacks comanagement or co-control, a democracy displays much more comanagement without welldefined levels or a clean hierarchy. It should be noted here that terms like democracy and autocracy used in this study do not exactly match the political science notions; they are defined based on analogies from Bar-Yam et al (12).…”

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confidence: 99%

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

Bhardwaj

Yan

Gerstein

2010

Proc. Natl. Acad. Sci. U.S.A.

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Gene regulatory networks have been shown to share some common aspects with commonplace social governance structures. Thus, we can get some intuition into their organization by arranging them into well-known hierarchical layouts. These hierarchies, in turn, can be placed between the extremes of autocracies, with welldefined levels and clear chains of command, and democracies, without such defined levels and with more co-regulatory partnerships between regulators. In general, the presence of partnerships decreases the variation in information flow amongst nodes within a level, more evenly distributing stress. Here we study various regulatory networks (transcriptional, modification, and phosphorylation) for five diverse species, Escherichia coli to human. We specify three levels of regulators-top, middle, and bottom-which collectively govern the non-regulator targets lying in the lowest fourth level. We define quantities for nodes, levels, and entire networks that measure their degree of collaboration and autocratic vs. democratic character. We show individual regulators have a range of partnership tendencies: Some regulate their targets in combination with other regulators in local instantiations of democratic structure, whereas others regulate mostly in isolation, in more autocratic fashion. Overall, we show that in all networks studied the middle level has the highest collaborative propensity and coregulatory partnerships occur most frequently amongst midlevel regulators, an observation that has parallels in corporate settings where middle managers must interact most to ensure organizational effectiveness. There is, however, one notable difference between networks in different species: The amount of collaborative regulation and democratic character increases markedly with overall genomic complexity.coregulatory partnerships | hierarchy | middle managers | autocracy | democracy

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confidence: 99%

mentioning

confidence: 99%

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

Bhardwaj

Yan

Gerstein

2010

Proc. Natl. Acad. Sci. U.S.A.

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“…Qualitatively, such networks can be classified as "autocratic", "intermediate", and "democratic" [19]. Focusing on the former class, we notice that in this case there are at least two general principles that allow us to understand what often happens [20].…”

Section: Modelmentioning

confidence: 99%

“…The topology of the genetic networks containing mRNAS and proteins can be fairly complex [17][18][19][20]. Qualitatively, such networks can be classified as "autocratic", "intermediate", and "democratic" [19].…”

Section: Modelmentioning

confidence: 99%

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Non-coding RNAs and a layered architecture of genetic networks

Zhdanov¹

2010

Open Physics

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Abstract:In eukaryotic cells, protein-coding sequences constitute a relatively small part of the genome. The rest of the genome is transcribed to non-coding RNAs (ncRNAs). Such RNAs form the cornerstone of a regulatory network that operates in parallel with the protein network. Their biological functions are based primarily on the ability to pair with and deactivate target messenger RNAs (mRNAs). To clarify the likely role of ncRNAs in complex genetic networks, we present and comprehensively analyze a kinetic model of one of the key counterparts of the network architectures. Specifically, the genes transcribed to ncRNAs are considered to interplay with a hierarchical two-layer set of genes transcribed to mRNAs. The genes forming the bottom layer are regulated from the top and negatively self-regulated. If the former regulation is positive, the dependence of the RNA populations on the governing parameters is found to be often nonmonotonous. Specifically, the model predicts bistability. If the regulation is negative, the dependence of the RNA populations on the governing parameters is monotonous. In particular, the population of the mRNAs, corresponding to the genes forming the bottom layer, is nearly constant.

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Tutorial on biological networks

Vital‐Lopez

Memišević

Dutta

2012

WIREs Data Min & Knowl

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Understanding how the functioning of a biological system emerges from the interactions among its components is a long-standing goal of network science. Fomented by developments in high-throughput technologies to characterize biomolecules and their interactions, network science has emerged as one of the fastest growing areas in computational and systems biology research. Although the number of research and review articles on different aspects of network science is increasing, updated resources that provide a broad, yet concise, review of this area in the context of systems biology are few. The objective of this article is to provide an overview of the research on biological networks to a general audience, who have some knowledge of biology and statistics, but are not necessarily familiar with this research field. Based on the different aspects of network science research, the article is broadly divided into four sections: (1) network construction, (2) topological analysis, (3) network and data integration, and (4) visualization tools. We specifically focused on the most widely studied types of biological networks, which are, metabolic, gene regulatory, protein-protein interaction, genetic interaction, and signaling networks. In future, with further developments on experimental and computational methods, we expect that the analysis of biological networks will assume a leading role in basic and translational research.

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Attractors and Democratic Dynamics

Cited by 79 publications

References 15 publications

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

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

Non-coding RNAs and a layered architecture of genetic networks

Tutorial on biological networks

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