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Reconstruction of metabolic pathways by combining probabilistic graphical model-based and knowledge-based methods.

Qi Q, Li J, Cheng J - BMC Proc (2014)

Bottom Line: Using individual reactions and interactions extracted from different pathways of many organisms to guide pathway construction is new and improves both the coverage and accuracy of metabolic pathway construction.We applied this probabilistic knowledge-based approach to construct the metabolic networks from yeast gene expression data and compared its results with 62 known metabolic networks in the KEGG database.The experiment showed that the method improved the coverage of metabolic network construction over the traditional reference pathway mapping method and was more accurate than pure ab initio methods.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, University of Missouri, Columbia, MO 65201, USA.

ABSTRACT
Automatic reconstruction of metabolic pathways for an organism from genomics and transcriptomics data has been a challenging and important problem in bioinformatics. Traditionally, known reference pathways can be mapped into an organism-specific ones based on its genome annotation and protein homology. However, this simple knowledge-based mapping method might produce incomplete pathways and generally cannot predict unknown new relations and reactions. In contrast, ab initio metabolic network construction methods can predict novel reactions and interactions, but its accuracy tends to be low leading to a lot of false positives. Here we combine existing pathway knowledge and a new ab initio Bayesian probabilistic graphical model together in a novel fashion to improve automatic reconstruction of metabolic networks. Specifically, we built a knowledge database containing known, individual gene / protein interactions and metabolic reactions extracted from existing reference pathways. Known reactions and interactions were then used as constraints for Bayesian network learning methods to predict metabolic pathways. Using individual reactions and interactions extracted from different pathways of many organisms to guide pathway construction is new and improves both the coverage and accuracy of metabolic pathway construction. We applied this probabilistic knowledge-based approach to construct the metabolic networks from yeast gene expression data and compared its results with 62 known metabolic networks in the KEGG database. The experiment showed that the method improved the coverage of metabolic network construction over the traditional reference pathway mapping method and was more accurate than pure ab initio methods.

No MeSH data available.


Resulted network of probabilistic pathway inference without knowledge constraints for the targeted citrate cycle metabolic pathway network. Red edges denote correct relationships.
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Figure 5: Resulted network of probabilistic pathway inference without knowledge constraints for the targeted citrate cycle metabolic pathway network. Red edges denote correct relationships.

Mentions: The predicted pathway networks were comprised of directly connected nodes (genes), which represent the network structures of gene-product relationships underlying the metabolic pathways. The predicted results were compared with the 62 target yeast metabolic pathways obtained in the KEGG database. The comparison was conducted on relational edges presented both in target and predicted metabolic network structures. For example, given the underlying relation network of KEGG's Citrate cycle metabolism as target network shown in Figure 3. Figure 4, Figure 5 and 6, respectively, show the resulted networks from the mapping-based method, the probabilistic inference method without knowledge constraints, and the one with knowledge constraints. The red-marked edges highlight the correct relations existing in a target network. Figure 4 shows that the simple mapping method recovered a portion of correct relations. Without knowledge constraints, the predicted network in Figure 5 has only a few correctly predicted edges and a large number of falsely predicted ones. The probabilistic inference method with knowledge constraints predicts more correct relation edges than the other two methods (see Figure 6). The predicted relation network was then processed to generate the metabolic network as shown in Figure 7, by adding on relevant chemical compounds from the knowledgebase.


Reconstruction of metabolic pathways by combining probabilistic graphical model-based and knowledge-based methods.

Qi Q, Li J, Cheng J - BMC Proc (2014)

Resulted network of probabilistic pathway inference without knowledge constraints for the targeted citrate cycle metabolic pathway network. Red edges denote correct relationships.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4202177&req=5

Figure 5: Resulted network of probabilistic pathway inference without knowledge constraints for the targeted citrate cycle metabolic pathway network. Red edges denote correct relationships.
Mentions: The predicted pathway networks were comprised of directly connected nodes (genes), which represent the network structures of gene-product relationships underlying the metabolic pathways. The predicted results were compared with the 62 target yeast metabolic pathways obtained in the KEGG database. The comparison was conducted on relational edges presented both in target and predicted metabolic network structures. For example, given the underlying relation network of KEGG's Citrate cycle metabolism as target network shown in Figure 3. Figure 4, Figure 5 and 6, respectively, show the resulted networks from the mapping-based method, the probabilistic inference method without knowledge constraints, and the one with knowledge constraints. The red-marked edges highlight the correct relations existing in a target network. Figure 4 shows that the simple mapping method recovered a portion of correct relations. Without knowledge constraints, the predicted network in Figure 5 has only a few correctly predicted edges and a large number of falsely predicted ones. The probabilistic inference method with knowledge constraints predicts more correct relation edges than the other two methods (see Figure 6). The predicted relation network was then processed to generate the metabolic network as shown in Figure 7, by adding on relevant chemical compounds from the knowledgebase.

Bottom Line: Using individual reactions and interactions extracted from different pathways of many organisms to guide pathway construction is new and improves both the coverage and accuracy of metabolic pathway construction.We applied this probabilistic knowledge-based approach to construct the metabolic networks from yeast gene expression data and compared its results with 62 known metabolic networks in the KEGG database.The experiment showed that the method improved the coverage of metabolic network construction over the traditional reference pathway mapping method and was more accurate than pure ab initio methods.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, University of Missouri, Columbia, MO 65201, USA.

ABSTRACT
Automatic reconstruction of metabolic pathways for an organism from genomics and transcriptomics data has been a challenging and important problem in bioinformatics. Traditionally, known reference pathways can be mapped into an organism-specific ones based on its genome annotation and protein homology. However, this simple knowledge-based mapping method might produce incomplete pathways and generally cannot predict unknown new relations and reactions. In contrast, ab initio metabolic network construction methods can predict novel reactions and interactions, but its accuracy tends to be low leading to a lot of false positives. Here we combine existing pathway knowledge and a new ab initio Bayesian probabilistic graphical model together in a novel fashion to improve automatic reconstruction of metabolic networks. Specifically, we built a knowledge database containing known, individual gene / protein interactions and metabolic reactions extracted from existing reference pathways. Known reactions and interactions were then used as constraints for Bayesian network learning methods to predict metabolic pathways. Using individual reactions and interactions extracted from different pathways of many organisms to guide pathway construction is new and improves both the coverage and accuracy of metabolic pathway construction. We applied this probabilistic knowledge-based approach to construct the metabolic networks from yeast gene expression data and compared its results with 62 known metabolic networks in the KEGG database. The experiment showed that the method improved the coverage of metabolic network construction over the traditional reference pathway mapping method and was more accurate than pure ab initio methods.

No MeSH data available.