Dataset Information

GrowMatch: an automated method for reconciling in silico/in vivo growth predictions.

ABSTRACT: Genome-scale metabolic reconstructions are typically validated by comparing in silico growth predictions across different mutants utilizing different carbon sources with in vivo growth data. This comparison results in two types of model-prediction inconsistencies; either the model predicts growth when no growth is observed in the experiment (GNG inconsistencies) or the model predicts no growth when the experiment reveals growth (NGG inconsistencies). Here we propose an optimization-based framework, GrowMatch, to automatically reconcile GNG predictions (by suppressing functionalities in the model) and NGG predictions (by adding functionalities to the model). We use GrowMatch to resolve inconsistencies between the predictions of the latest in silico Escherichia coli (iAF1260) model and the in vivo data available in the Keio collection and improved the consistency of in silico with in vivo predictions from 90.6% to 96.7%. Specifically, we were able to suggest consistency-restoring hypotheses for 56/72 GNG mutants and 13/38 NGG mutants. GrowMatch resolved 18 GNG inconsistencies by suggesting suppressions in the mutant metabolic networks. Fifteen inconsistencies were resolved by suppressing isozymes in the metabolic network, and the remaining 23 GNG mutants corresponding to blocked genes were resolved by suitably modifying the biomass equation of iAF1260. GrowMatch suggested consistency-restoring hypotheses for five NGG mutants by adding functionalities to the model whereas the remaining eight inconsistencies were resolved by pinpointing possible alternate genes that carry out the function of the deleted gene. For many cases, GrowMatch identified fairly nonintuitive model modification hypotheses that would have been difficult to pinpoint through inspection alone. In addition, GrowMatch can be used during the construction phase of new, as opposed to existing, genome-scale metabolic models, leading to more expedient and accurate reconstructions.

SUBMITTER: Kumar VS

PROVIDER: S-EPMC2645679 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature

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GrowMatch: an automated method for reconciling in silico/in vivo growth predictions.

Kumar Vinay Satish VS Maranas Costas D CD

PLoS computational biology 20090313 3

Genome-scale metabolic reconstructions are typically validated by comparing in silico growth predictions across different mutants utilizing different carbon sources with in vivo growth data. This comparison results in two types of model-prediction inconsistencies; either the model predicts growth when no growth is observed in the experiment (GNG inconsistencies) or the model predicts no growth when the experiment reveals growth (NGG inconsistencies). Here we propose an optimization-based framewo ...[more]

PMID: 19282964

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Project description:BACKGROUND: Genome scale metabolic reconstructions are developed to efficiently engineer biocatalysts and bioprocesses based on a rational approach. However, in most reconstructions, due to the lack of appropriate measurements, experimentally determined growth parameters are simply taken from literature including other organisms, which reduces the usefulness and suitability of these models. Pseudomonas putida KT2440 is an outstanding biocatalyst given its versatile metabolism, its ability to generate sufficient energy and turnover of NADH and NAD. To apply this strain optimally in industrial production, a previously developed genome-scale metabolic model (iJP815) was experimentally assessed and streamlined to enable accurate predictions of the outcome of metabolic engineering approaches. RESULTS: To substantially improve the accuracy of the genome scale model (iJP815), continuous bioreactor cultures on a mineral medium with glucose as a sole carbon source were carried out at different dilution rates, which covered pulling analysis of the macromolecular composition of the biomass. Besides, the maximum biomass yield (on substrate) of 0.397 gDCW · gglc-1, the maintenance coefficient of 0.037 gglc · gDCW-1 · h-1 and the maximum specific growth rate of 0.59 h-1 were determined. Only the DNA fraction increased with the specific growth rate. This resulted in reliable estimation for the Growth-Associated Maintenance (GAM) of 85 mmolATP · gDCW-1 and the Non Growth-Associated Maintenance (NGAM) of 3.96 mmolATP · gDCW-1 · h-1. Both values were found significantly different from previous assignment as a consequence of a lower yield and higher maintenance coefficient than originally assumed. Contrasting already published 13C flux measurements and the improved model allowed for constraining the solution space, by eliminating futile cycles. Furthermore, the model predictions were compared with transcriptomic data at overall good consistency, which helped to identify missing links. CONCLUSIONS: By careful interpretation of growth stoichiometry and kinetics when grown in the presence of glucose, this work reports on an accurate genome scale metabolic model of Pseudomonas putida, providing a solid basis for its use in designing superior strains for biocatalysis. By consideration of substrate specific variation in stoichiometry and kinetics, it can be extended to other substrates and new mutants.

Project description:The detection of hundreds of thousands of new cases of leprosy every year suggests that transmission of Mycobacterium leprae infection still continues. Unfortunately, tools for identification of asymptomatic disease and/or early-stage M. leprae infection (likely sources of transmission) are lacking. The recent identification of M. leprae-unique genes has allowed the analysis of human T-cell responses to novel M. leprae antigens. Antigens with the most-promising diagnostic potential were tested for their ability to induce cytokine secretion by using peripheral blood mononuclear cells from leprosy patients and controls in five different areas where leprosy is endemic; 246 individuals from Brazil, Nepal, Bangladesh, Pakistan, and Ethiopia were analyzed for gamma interferon responses to five recombinant proteins (ML1989, ML1990, ML2283, ML2346, and ML2567) and 22 synthetic peptides. Of these, the M. leprae-unique protein ML1989 was the most frequently recognized and ML2283 the most specific for M. leprae infection/exposure, as only a limited number of tuberculosis patients responded to this antigen. However, all proteins were recognized by a significant number of controls in areas of endemicity. T-cell responses correlated with in vitro response to M. leprae, suggesting that healthy controls in areas where leprosy is endemic are exposed to M. leprae. Importantly, 50% of the healthy household contacts and 59% of the controls in areas of endemicity had no detectable immunoglobulin M antibodies to M. leprae-specific PGL-I but responded in T-cell assays to >or=1 M. leprae protein. T-cell responses specific for leprosy patients and healthy household contacts were observed for ML2283- and ML0126-derived peptides, indicating that M. leprae peptides hold potential as diagnostic tools. Future work should concentrate on the development of a sensitive and field-friendly assay and identification of additional peptides and proteins that can induce M. leprae-specific T-cell responses.

Dataset Information

GrowMatch: an automated method for reconciling in silico/in vivo growth predictions.

Publications

GrowMatch: an automated method for reconciling in silico/in vivo growth predictions.

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