shaw secure not updating - Bayesian methods for elucidating genetic regulatory networks

We can reduce the complexity of a network further by considering its motifs (Fig. Network motifs, by their intrinsic behavior, help us understand how networks oversee different tasks, and different motifs predominate depending on the type of network or module (Yeger-Lotem et al. For example, a transcriptional regulatory module dominated by single-input motifs has a simple structure and is expected to have an “all-or-none” response, whereas a module or subnetwork in which multiple-input motifs predominate will be expected to have a more subtle and gradated response.Networks characterized by multiple feed-forward loops tend to be stable rather than transient (Yeger-Lotem et al. It is necessary to understand both the topology of a network (interconnectivity of nodes) and how this topology changes with time or environmental conditions, since not all nodes are active at any given time.Nodes represent proteins, genes, or enzymatic substrates that translate extracellular signals from the environment.

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One is that there might be a size limit beyond which a hub, while still being functional, renders the whole network too sensitive to directed inactivation.

A more pragmatic explanation is that, since a cell carries out many distinct biological processes, it may need a certain level of compartmentalization that cannot be achieved if everything is directly connected.

This presumably facilitates the efficient propagation and integration of signals.

One other notable characteristic of biological networks is the relative paucity of hubs that connect directly to one another.

We focus here on transcriptional regulatory networks for two reasons.

First, this area has received much attention in the past decade, due in large part to the development of high-throughput genomic approaches and an array of computational tools.The nodes may represent the set of genes that share a common regulatory TF or that are expressed under the same specific set of conditions. Network motifs describe how single nodes connect with their neighbors.Studies of the regulatory networks governing cell cycle progression and myogenesis provide examples (see below). Examples include the single-input motif, which describes the connection between a target gene and its sole transcriptional regulator; the multiple-input motif, in which a target gene is regulated by a group of factors; and the feed-forward loop, in which the product of one TF regulates the expression of a second TF, and both factors together regulate the expression of a third gene.They are characterized by a multistage architecture of their regulatory network.The TF hubs that regulate them have a relatively small number of targets, which often tend to be other TFs, and this tendency generates high local interconnectivity.2000, 2001; Newman 2003; Barabasi and Oltvai 2004).

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