A Hybrid of Metabolic Flux Analysis and Bayesian Factor Modeling for Multiomic Temporal Pathway Activation.

Hdl Handle:
http://hdl.handle.net/10149/601031
Title:
A Hybrid of Metabolic Flux Analysis and Bayesian Factor Modeling for Multiomic Temporal Pathway Activation.
Authors:
Angione, C. (Claudio); Pratanwanich, N. (Naruemon); Lió, P. (Pietro)
Affiliation:
Computer Laboratory, University of Cambridge
Citation:
Angione, C., Pratanwanich, N. and Lió, P. (2015) 'A hybrid of metabolic flux analysis and Bayesian factor modeling for multi-omics temporal pathway activation' ACS Synthetic Biology; 4(8): 880-889
Publisher:
ACS Publications
Journal:
ACS synthetic biology
Issue Date:
21-Aug-2015
URI:
http://hdl.handle.net/10149/601031
DOI:
10.1021/sb5003407
PubMed ID:
25856685
Additional Links:
http://pubs.acs.org/doi/abs/10.1021/sb5003407
Abstract:
The growing availability of multiomic data provides a highly comprehensive view of cellular processes at the levels of mRNA, proteins, metabolites, and reaction fluxes. However, due to probabilistic interactions between components depending on the environment and on the time course, casual, sometimes rare interactions may cause important effects in the cellular physiology. To date, interactions at the pathway level cannot be measured directly, and methodologies to predict pathway cross-correlations from reaction fluxes are still missing. Here, we develop a multiomic approach of flux-balance analysis combined with Bayesian factor modeling with the aim of detecting pathway cross-correlations and predicting metabolic pathway activation profiles. Starting from gene expression profiles measured in various environmental conditions, we associate a flux rate profile with each condition. We then infer pathway cross-correlations and identify the degrees of pathway activation with respect to the conditions and time course using Bayesian factor modeling. We test our framework on the most recent metabolic reconstruction of Escherichia coli in both static and dynamic environments, thus predicting the functionality of particular groups of reactions and how it varies over time. In a dynamic environment, our method can be readily used to characterize the temporal progression of pathway activation in response to given stimuli.
Type:
Article
Language:
en
ISSN:
2161-5063
Rights:
Following 18 month embargo author can archive post-print (ie final draft post-refereeing). For full details see http://www.sherpa.ac.uk/romeo/issn/2161-5063/ [Accessed: 09/03/2016]

Full metadata record

DC FieldValue Language
dc.contributor.authorAngione, C. (Claudio)en
dc.contributor.authorPratanwanich, N. (Naruemon)en
dc.contributor.authorLió, P. (Pietro)en
dc.date.accessioned2016-03-09T16:24:14Zen
dc.date.available2016-03-09T16:24:14Zen
dc.date.issued2015-08-21en
dc.identifier.citationACS synthetic biology; 4 (8): 880-9en
dc.identifier.issn2161-5063en
dc.identifier.pmid25856685en
dc.identifier.doi10.1021/sb5003407en
dc.identifier.urihttp://hdl.handle.net/10149/601031en
dc.description.abstractThe growing availability of multiomic data provides a highly comprehensive view of cellular processes at the levels of mRNA, proteins, metabolites, and reaction fluxes. However, due to probabilistic interactions between components depending on the environment and on the time course, casual, sometimes rare interactions may cause important effects in the cellular physiology. To date, interactions at the pathway level cannot be measured directly, and methodologies to predict pathway cross-correlations from reaction fluxes are still missing. Here, we develop a multiomic approach of flux-balance analysis combined with Bayesian factor modeling with the aim of detecting pathway cross-correlations and predicting metabolic pathway activation profiles. Starting from gene expression profiles measured in various environmental conditions, we associate a flux rate profile with each condition. We then infer pathway cross-correlations and identify the degrees of pathway activation with respect to the conditions and time course using Bayesian factor modeling. We test our framework on the most recent metabolic reconstruction of Escherichia coli in both static and dynamic environments, thus predicting the functionality of particular groups of reactions and how it varies over time. In a dynamic environment, our method can be readily used to characterize the temporal progression of pathway activation in response to given stimuli.en
dc.language.isoenen
dc.publisherACS Publicationsen
dc.relation.urlhttp://pubs.acs.org/doi/abs/10.1021/sb5003407en
dc.rightsFollowing 18 month embargo author can archive post-print (ie final draft post-refereeing). For full details see http://www.sherpa.ac.uk/romeo/issn/2161-5063/ [Accessed: 09/03/2016]en
dc.titleA Hybrid of Metabolic Flux Analysis and Bayesian Factor Modeling for Multiomic Temporal Pathway Activation.en
dc.typeArticleen
dc.contributor.departmentComputer Laboratory, University of Cambridgeen
dc.identifier.journalACS synthetic biologyen
or.citation.harvardAngione, C., Pratanwanich, N. and Lió, P. (2015) 'A hybrid of metabolic flux analysis and Bayesian factor modeling for multi-omics temporal pathway activation' ACS Synthetic Biology; 4(8): 880-889en
dc.eprint.versionPost-printen
dc.embargo18 monthsen

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