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Reconstructing Signaling Networks in Java Generate 3 of 9 in Java Reconstructing Signaling Networks

5.2 Reconstructing Signaling Networks using j2ee tobuild ansi/aim code 39 on asp.net web,windows application Scan GS1 BarCodes pairs. Of course, if jboss Code 3 of 9 there are more than two components needed to induce a speci c event, the combinatorial possibilities grow to correspondingly greater numbers. Similarly, a small number of expressed receptors, used in combination, can allow for the discrimination of a very large number of environmental stimuli.

For example, if we assume that 1% of the estimated 1,543 receptors in the human genome [236] are expressed in a given cell type, then that cell type could respond to 32,768 (= 215 ) different ligand combinations. These back of the envelope calculations emphasize that a small number of transcription factors and signaling receptor proteins operating in a combinatorial manner can allow for diversity of function in signaling networks. A recent study analyzed the repertoire of GPCRs in the human genome and identi ed 367 GPCRs [235].

The expression pro les of 100 GPCRs in the mouse genome for 26 different tissues indicated that most of the receptors were expressed in a variety of tissues but that each tissue had a unique pro le of receptors. These results further support the existence of combinatorial control of signaling networks. Elements of reconstruction Signaling network reconstruction has been approached in three different ways (see Figure 3.

8). r The rst approach consists of reconstructions of, preferably highly connected, nodes. This approach involves the delineation of all the compounds and reactions associated with a given network component (i.

e., a protein, an ion, or a metabolite). For example, much work has been done with calcium that plays a key role in many signaling processes.

r The second approach consists of identifying signaling modules. Such modules involve grouping components that function together under certain conditions. Such grouping can be based on intuitive reasoning or on unbiased assessment of network properties (see 9).

These modules allow for detailed analyses of kinetics of various concentrations and help to understand processes like feedback mechanisms. For example, much successful work has been done with the epidermal growth factor receptor and associated mitrogen-activated protein (MAP) kinases [197, 248]. Analyses of other growth factor receptor signaling have also been performed [209, 164].

r The third approach of reconstructed networks involves pathways that connect signaling inputs to signaling outputs. For example, the delineation of all the steps from the binding of a growth factor to its. Signaling Networks Table 5.2: Online sources on signaling networks. BioCarta Transpath ( now commercialized) Alliance for Cellular Signaling Cell Signaling Networks Database SPAD database Science Magazine s STKE database Database of Quantitative Cellular Signaling (DOQCS) http://www.biocarta.com/genes/ allpathways.

asp http://transpath.gbf.de http://www.

signaling-gateway.org http://geo.nihs.

go.jp/csndb http://www.grt.

kyushu-u.ac.jp/spad/ http://stke.

sciencemag.org http://doqcs.ncbs.

res.in. receptor to the subs 3 of 9 for Java equent activation of a transcription factor that induces the expression of target genes. The reconstruction and analysis of the pheromone-activated MAP kinase pathway in yeast has demonstrated the utility of such an approach [227]. The reconstruction and analysis of this signaling pathway resulted in an hypothesized mechanism by which the MAP kinase Fus3p was dephosphorylated and localized at particular steps in the signaling pathway.

Level of detail in a reconstruction An important consideration in the reconstruction of a signaling network is the desired level of detail. The level of detail can be as coarse as a delineation of associations between network components or as re ned as a precise mechanistic description of the chemical reactions that occur. A reconstruction of associations can involve a description of a simple connectivity (e.

g., ligand A transcription factor B; ligand A is functionally connected to transcription factor B) or a more involved set of relationships that shows more intermediates between a signaling input and a signaling output (e.g.

, ligand A protein B protein C transcription factor D) (e.g., [212]).

Network reconstructions consisting of associations between components are amenable to multiple types of structural analyses (to be discussed). More detailed causal relationships account for cause and effect relationships (e.g.

, ligand A protein B; ligand A activates protein B) (e.g., [197]).

Kinetic relationships build off of these causal relationships, assigning scaling factors and time constants between different properties of interest. At an even more re ned level of detail are mechanistic reconstructions. These reconstructions account for stoichiometric relationships between signaling components (e.

g., ligand A binds to receptor B and receptor B then dimerizes) and thus can be represented with stoichiometric matrices. This level of detail allows for an accounting of all network.

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