Dataset Information

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

ABSTRACT: Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs) for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA) and multi-objective flux variability analysis (MO-FVA). Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity) that take place at the ecosystem scale.

SUBMITTER: Budinich M

PROVIDER: S-EPMC5302800 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

Budinich Marko M Bourdon Jérémie J Larhlimi Abdelhalim A Eveillard Damien D

PloS one 20170210 2

Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs) for ...[more]

PMID: 28187207

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Project description:BackgroundGeobacter metallireducens was the first organism that can be grown in pure culture to completely oxidize organic compounds with Fe(III) oxide serving as electron acceptor. Geobacter species, including G. sulfurreducens and G. metallireducens, are used for bioremediation and electricity generation from waste organic matter and renewable biomass. The constraint-based modeling approach enables the development of genome-scale in silico models that can predict the behavior of complex biological systems and their responses to the environments. Such a modeling approach was applied to provide physiological and ecological insights on the metabolism of G. metallireducens.ResultsThe genome-scale metabolic model of G. metallireducens was constructed to include 747 genes and 697 reactions. Compared to the G. sulfurreducens model, the G. metallireducens metabolic model contains 118 unique reactions that reflect many of G. metallireducens' specific metabolic capabilities. Detailed examination of the G. metallireducens model suggests that its central metabolism contains several energy-inefficient reactions that are not present in the G. sulfurreducens model. Experimental biomass yield of G. metallireducens growing on pyruvate was lower than the predicted optimal biomass yield. Microarray data of G. metallireducens growing with benzoate and acetate indicated that genes encoding these energy-inefficient reactions were up-regulated by benzoate. These results suggested that the energy-inefficient reactions were likely turned off during G. metallireducens growth with acetate for optimal biomass yield, but were up-regulated during growth with complex electron donors such as benzoate for rapid energy generation. Furthermore, several computational modeling approaches were applied to accelerate G. metallireducens research. For example, growth of G. metallireducens with different electron donors and electron acceptors were studied using the genome-scale metabolic model, which provided a fast and cost-effective way to understand the metabolism of G. metallireducens.ConclusionWe have developed a genome-scale metabolic model for G. metallireducens that features both metabolic similarities and differences to the published model for its close relative, G. sulfurreducens. Together these metabolic models provide an important resource for improving strategies on bioremediation and bioenergy generation.

Project description:BackgroundGenome-scale models of metabolism have only been analyzed with the constraint-based modelling philosophy. Some gene deletion studies on in silico organism models at genome-scale have been made, but most of them were from the aspects of distinguishing lethal and non-lethal genes or growth rate. The impact of gene deletion on flux redistribution, the functions and characters of key genes, and the performance of different reactions in entire gene deletion still lack research.ResultsThree main researches have been done into the metabolism of E. coli in gene deletion. The first work was about finding key genes and subsystems: First, by calculating the deletion impact p of whole 1261 genes, one by one, on the metabolic flux redistribution of E. coli_iAF1260, we can find that p is more detailed in describing the change of organism's metabolism. Next, we sought out 195 important (high-p) genes, and they are more than essential genes (growth rate f becomes zero if deleting). So we speculated that under some circumstances and when an important gene is deleted, a big change in the metabolic system of E. coli has taken place and E. coli may use other reaction ways to strive to live. Further, by determining the functional subsystems to which 195 key genes belong, we found that their distribution to subsystems was not even and most of them were related to just three subsystems and that all of the 8 important but not essential genes appear just in "Oxidative Phosphorylation". Our second work was about p's three characters: We analyzed the correlation between p and d (connection degree of one gene) and the correlation between p and vgene (flux sum controlled by one gene), and found that both of them are not of linear correlation, but the correlation between p and f is of highly linear correlation. The third work was about highly-affected reactions: We found 16 reactions with more than 2000 Rg value (measuring the impact that a reaction is gotten in the whole 1261 gene deletion). We speculated that highly-affected reactions involve in the metabolism of basic biomasses.ConclusionTo sum up, these results we obtained have biological significances and our researches will shed new light on the future researches.

Dataset Information

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

Publications

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

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