Regulatory gene networks and the properties of the developmental process

Davidson, Eric H.; McClay, David R.; Hood, Leroy

doi:10.1073/pnas.0437746100

Cited by 205 publications

(130 citation statements)

References 38 publications

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“…The structure of resulting gene networks is relevant to the functioning of the cell, for example, in development (Davidson et al, 2003). Network analyses have shown a correlation between, on the one hand, the essentiality of a gene and, on the other hand, either the number of connections that the gene has (Jeong et al, 2001;Han et al, 2004;Carter et al, 2004) or the topology of the metabolic network (Stelling et al, 2002;Forster et al, 2003).…”

Section: Resultsmentioning

confidence: 99%

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A General Framework for Weighted Gene Co-Expression Network Analysis

Zhang¹,

Horvath²

2005

Statistical Applications in Genetics and Molecular Biology

4,812

4,830

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Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for 'soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion).We generalize the following important network concepts to the case of weighted networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a gene. Second, we provide theoretical and empirical evidence that the 'weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its 'unweighted' counterpart. Third, we generalize the clustering coefficient to weighted networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding.We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.

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Section: Resultsmentioning

confidence: 99%

“…Furthermore, networks have been found useful to interpret synthetic lethal knockouts (Brummelkamp and Bernards, 2003;Sonoda, 2003). Network modules implement the classic idea that a cell can be divided into functional modules (Snel et al, 2002;Yanai and DeLisi, 2002;Davidson et al, 2003).…”

Section: Resultsmentioning

confidence: 99%

A General Framework for Weighted Gene Co-Expression Network Analysis

Zhang¹,

Horvath²

2005

Statistical Applications in Genetics and Molecular Biology

4,812

4,830

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“…How is it organized? Sophisticated electronic circuit type diagrams of a cell's molecular interactions provide limited insight (15,16). If the cell's protein interrelationships are drawn more like food webs in ecology, we are struck by their similarity (11,17).…”

Section: The Cell's Networkmentioning

confidence: 99%

Exploring the cell's network with molecular imaging

Enzmann

2006

Magnetic Resonance Imaging

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Molecular imaging is already a powerful tool for investigating molecular interactions within the cell. Interpreting molecular imaging findings will, however, take us into the more unfamiliar, nonlinear realm of networks. The network class of interest is the "scale-free" network, which characterizes not only the cell, but also surprisingly, other real work networks such as the world wide web. This network topology yields insights in how the cell is functionally organized via motifs, modules, and different types of hubs. Additional organizational information is gained from the cell's evolutionary history. Interpretation of molecular images will be deepened by a both qualitative and quantitative knowledge of the cell's network. Importantly, cell network behavior can be independent of molecular detail. For this reason, the same molecule can serve different functions in different cells or even within the same cell. Since a scalefree network's behavior is likely to be nonlinear and exhibit emergent behavior, a degree of caution is prudent in assigning cause and effect to molecular imaging findings in our effort to reengineer some of the cell's functions. Molecular imagers will need to be cognizant of the level of organization in the cell's network they are interrogating.

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“…The causality manifested by this system is cybernetic: Regulatory states specifying cell fate are maintained in mutually exclusive domains by a complex system of positive and negative feedback [35]. The oral ectoderm regulatory state is established by the positive feedback community effect of Nodal, which is localized by negative feedback from Lefty; and this regulatory state initiates aboral ectoderm specification by activating bmp2/4 within prospective oral ectoderm.…”

Section: A Developmental Paradigm: Sea Urchin Embryogenesismentioning

confidence: 99%

Information as a Manifestation of Development

Coffman

2011

Information

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Information manifests a reduction in uncertainty or indeterminacy. As such it can emerge in two ways: by measurement, which involves the intentional choices of an observer; or more generally, by development, which involves systemically mutual ('self-organizing') processes that break symmetry. The developmental emergence of information is most obvious in ontogeny, but pertains as well to the evolution of ecosystems and abiotic dissipative structures. In this review, a seminal, well-characterized ontogenetic paradigm-the sea urchin embryo-is used to show how cybernetic causality engenders the developmental emergence of biological information at multiple hierarchical levels of organization. The relevance of information theory to developmental genomics is also discussed.

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Regulatory gene networks and the properties of the developmental process

Cited by 205 publications

References 38 publications

A General Framework for Weighted Gene Co-Expression Network Analysis

A General Framework for Weighted Gene Co-Expression Network Analysis

Exploring the cell's network with molecular imaging

Information as a Manifestation of Development

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