Dataset Information

Maldonado2017 - Integration of Genome Scale Metabolic Networks and Gene Regulation of Metabolic Enzymes With Physiologically Based Pharmacokinetics

ABSTRACT: Example models of ongoing work on integration of PBPK, GSMNs, and metabolic gene regulation.

SUBMITTER: Krishna Kumar Tiwari

PROVIDER: MODEL2111050001 | BioModels | 2021-11-05

REPOSITORIES: BioModels

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Dataset's files

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			Action	DRS
	MODEL2111050001?filename=CLEAN.bat	Other
	MODEL2111050001?filename=COMPILE.bat	Other
	MODEL2111050001?filename=HepatoNet1_EMM_14122016.sfba	Other
	MODEL2111050001?filename=PBPK_GSMN_NR.ctrl.txt	Txt
	MODEL2111050001?filename=PBPK_GSMN_NR.qsspn	Other

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Publications

Integration of Genome Scale Metabolic Networks and Gene Regulation of Metabolic Enzymes With Physiologically Based Pharmacokinetics.

Maldonado Elaina M EM Leoncikas Vytautas V Fisher Ciarán P CP Moore J Bernadette JB Plant Nick J NJ Kierzek Andrzej M AM

CPT: pharmacometrics & systems pharmacology 20170908 11

The scope of physiologically based pharmacokinetic (PBPK) modeling can be expanded by assimilation of the mechanistic models of intracellular processes from systems biology field. The genome scale metabolic networks (GSMNs) represent a whole set of metabolic enzymes expressed in human tissues. Dynamic models of the gene regulation of key drug metabolism enzymes are available. Here, we introduce GSMNs and review ongoing work on integration of PBPK, GSMNs, and metabolic gene regulation. We demonst ...[more]

PMID: 28782239

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Project description:Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface.