BMC Bioinformatics
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Automatically built pathway. Nodes and links in common with manually curated IL-1 pathway shown on Figure 3 are highlighted in blue. Note that the set of proteins unique to automatically built pathway represents a classical MAP kinase cascade. It has been suggested only recently that the IL-1 receptor appears to activate a MAP kinase cascade by interaction with other members of the Toll-like receptor superfamily [17]. Obviously, older review articles used for construction of the manually curated IL-1 pathway did not mention this information. For graph legend see figure 3.
- The example of an automatically built pathway for IL1 and its receptor is shown on Figure 4.
BMC Bioinformatics
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Network models produced by NetSearch. Pathways predicted by NetSearch for (A, B) pheromone response, (C) cell wall integrity, and (D) filamentation pathways, with the starting membrane protein for path drawing (blue), intermediate proteins (green) and transcription factor (red). In each case, the fifteen highest ranked paths between common endpoints were combined to form the signaling network. For the cell wall integrity pathway, the sensor proteins that initiate signal transduction Wsc/1/2/3p and Mid2p did not have any productive interactions. For this pathway, we began our searches at Rho1p and searched for a path length of seven. The size of each vertex is proportional to the sum of the scores of the paths in which it was included. Network graphs were produced with PAJEK graph drawing software [44], http://vlado.fmf.uni-lj.si/pub/networks/pajek.
- The signaling network models generated by NetSearch for the pheromone response, cell wall integrity and filamentation pathways are depicted in Fig. 4.
- Of the three network models, the one generated for the pheromone response pathway originating at Ste3p (Fig. 4A) exhibited the highest co-clustering scores.
- Fig. 4B depicts the pheromone response network at several different score cutoffs, and demonstrates how higher co-clustering score cutoffs reduces the complexity of the protein-interaction map.
- The top graph in Fig. 4B shows the network constructed from all 354 paths (with each protein arranged on the perimeter of an ellipse for clarity).
- On the bottom of Fig. 4B, only the highest scoring paths (those used to construct the network in Fig. 4A) with 19 proteins, are depicted.
- The network model generated for the cell wall integrity pathway is depicted in Fig. 4C. Membrane proteins in particular may fail to produce interactions when forced into the nucleus by the requirements of the standard two-hybrid technique.
- The network model for filamentous growth (Fig. 4D) involves 21 proteins, 20 of which are known to play a role in filamentous growth, or have functions consistent with that role, with the exception of Fus1p.
- This is the approach we have followed in constructing the networks depicted in Fig. 4.
- But when comparing the two network predictions depicted in Fig. 4, one notes many differences, all of which may help ensure specificity.
- Based on the interaction data available, the networks depicted in Fig. 4 are static, with all interactions given equal weight, and without information on the direction of information transfer.
BMC Bioinformatics
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Schematic representation of the AUTOGRAPH-method. The method consists of two parts, A and B. Part A includes the orthology prediction between genes of the query genome and the reference genomes from which there is a manually curated metabolic network available. In part B the gene-reaction associations are extracted from the manually curated networks. Subsequently, orthology will be combined with the gene-reaction association data. This allows reaction transfer to genes of the query genome.
- We applied the AUTOGRAPH-method (see Figure 1 and methods for details) to the genome of Lactococcus lactis IL1403 [32] using three manually curated metabolic networks as input, i.e. a network from Escherichia coli K12 [22], Lactobacillus plantarum WCFS1 [manuscript in preparation, see also [17]] and Bacillus subtilis [33].
- The developed method is called AUTOGRAPH (AUtomatic Transfer by Orthology of Gene Reaction Associations for Pathway Heuristics, see Figure 1) and is outlined in detail below.
- The AUTOGRAPH-method combines manually curated genome-scale metabolic networks and orthology to predict a network for a query species (Figure 1).
Theoretical Biology and Medical Modelling
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Alternative Th network. T helper pathway published in [69], reinterpreted as a signaling network.
- To circumvent this problem, we chose four pathways with low numbers of regulatory ambiguities and translated them as signaling networks (Figures 4 through 7).
- The alternative networks (Figures 4 through 7) were taken from previously published attempts to discover the molecular basis of this differentiation process.
The Journal of biological chemistry.
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FIG. 6. Ingenuity Pathway Analysis identifies networks of genes regulated during S phase in the liver in vivo. The network is displayed graphically as nodes (genes/gene products) and edges (the biological relationships between the nodes). For the explanation of the symbols and letters, see the legend to Fig. 4. This network was produced by combining the two highest scoring networks with a total of 31 differentially expressed focus genes and a highly significant score of 58. Uncommon gene symbols shown are AURKB, aurora kinase B; BIRC5, survivin; CCNE1, cyclin E1; CCNE2, cyclin E2; CDKN1A, p21; CDKN1B, p27Kip1; CDKN1C, P57KIP2; CEBPB, CCAAT/enhancer binding protein {{beta}}; DCTN1, dynactin 1; DCTN2, dynactin 2; DDIT3, GADD153; TFDP1, DP1; and YWHAQ, 14-3-3 homolog.
- Analysis of genes differentially expressed at this point identified several regulatory networks, including pathways involved in DNA replication (TOP2A (38), RPA1 (39), PCNA (40)), mitotic spindle assembly (MAPRE (41), DCTN2 (42), CDC2 (43), RACGAP1 (44, 45)), and mitotic checkpoint control (YWHAQ (46), MAD2L1 (47), AURKB (48), CKS1B (49), BIRC5 (50), NPM1 (51)) (Fig. 6).
BMC Bioinformatics
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Glycolisis/Gluconeogenesis model pathway. The studied metabolic module is reported in the figure (KEGG data base at (26)) with the indication of the involved enzymes, the ellipse marks the portion of the metabolic module at the basis of the difference marked by the first principal component. The bolded enzymes are shared by all the 25 organisms data sets.
- The first analysed case was relative to glycolisis/gluconeogenesis module, the general scheme of the metabolic network is reported in Fig. 2, the bolded enzymes are the one common to all the analysed microrganisms.
- By superimposing the different metabolic networks, we observed that the main difference between the networks posited at the two opposite first component ends, is the presence (absence) of the lateral branching of the Glycolisis/Gluconeogenesis module deputed to the utilization of Arbutin and Salicin as substrates for Glycolisis (ellipse of Fig. 2).
- The 25 analysed organisms share 11 common enzymes of the Glycolisis/Gluconeogenesis pathway (bolded in Fig. 2) thus, each of these enzymes allows for a specific sequence space to be constructed and compared with the metabolic network space.
Theoretical Biology and Medical Modelling
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Alternative Th network. T helper pathway published in [71], reinterpreted as a signaling network.
- To circumvent this problem, we chose four pathways with low numbers of regulatory ambiguities and translated them as signaling networks (Figures 4 through 7).
- Indeed, the network in Figure 6 comes from a relatively recent review, while that in Figure 7 is rather complex and contains five more nodes than our own proposed network (Figure 2).
- The alternative networks (Figures 4 through 7) were taken from previously published attempts to discover the molecular basis of this differentiation process.
Genome Biology
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LinearActivePaths analysis finds that virtually all genes in active metabolic networks confer sensitivity to arsenic when deleted. (a) Serine, threonine, glutamate amino-acid synthetic pathways; (b) the shikimate pathway. The paths that compose these networks all have individual p-values of < 0.05. The coloration for these figures is based on red for any gene ranked in the top 50 significant genes, yellow for 51-100, and green for >101.
- The first significant pathway was amino acid synthesis/degradation with the terminal products being L-threonine and L-homoserine, beginning with precursors such as L-arginine, fumarate and oxaloacetate (Figure 5a).
- The second network indicated the importance of the shikimate pathway, which is essential for the production of aromatic compounds in plants, bacteria and fungi (Figure 5b).
- These downstream pathways are important for the conversion to glutathione, necessary for the cell's defense from arsenic (Figures 4, 5a, 6 and Table 1).
Physiological genomics.
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Fig. 1. Signaling and transcriptional codes in development. Intercellular signals A, B, and C here comprise a "signaling code" received by a cell, initiating signal transduction events that lead to the binding of pathway-specific downstream transcription factors (A', B', C') to a cis-regulatory module. Also binding to the regulatory DNA are tissue-specific (i.e., not general nuclear effectors of the signaling pathways) transcription factors (TFs) D and E. The resulting A'-B'-C'-D-E "transcriptional code" acts on the regulatory module to activate gene transcription. For simplicity, cross talk between the pathways has not been illustrated, but such interactions are a common component of genetic networks (for instance, see Fig. 3C).
- The linchpins of the regulatory networks are the cis-regulatory elements that directly control gene expression through interpretation of the transcriptional code and thus act as sites of integration for the combinatorial action of multiple signal transduction pathways and tissue-specific selector proteins (Fig. 1).