GrowMatch: An Automated Method for Reconciling In Silico/In Vivo Growth Predictions Export

@article{citeulike:5949248, 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 ( i AF1260) 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 i AF1260. 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.}, author = {Kumar, Vinay S. and Maranas, Costas D.}, citeulike-article-id = {5949248}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.1000308}, day = {13}, doi = {10.1371/journal.pcbi.1000308}, journal = {PLoS Comput Biol}, keywords = {bi-level, carbon\_sources, ecoli, escherichia\_coli, fba, keio, metabolic-reconstruction, metabolic\_networks, model\_modification, model\_prediction, optimization}, month = {March}, number = {3}, pages = {e1000308+}, posted-at = {2009-10-16 07:31:42}, priority = {2}, publisher = {Public Library of Science}, title = {GrowMatch: An Automated Method for Reconciling In Silico/In Vivo Growth Predictions}, url = {http://dx.doi.org/10.1371/journal.pcbi.1000308}, volume = {5}, year = {2009} }

TY - JOUR ID - citeulike:5949248 L3 - citeulike-article-id:5949248 N2 - 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 ( i AF1260) 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 i AF1260. 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. IS - 3 JF - PLoS Comput Biol TI - GrowMatch: An Automated Method for Reconciling In Silico/In Vivo Growth Predictions PB - Public Library of Science VL - 5 SP - e1000308 KW - bi-level KW - carbon_sources KW - ecoli KW - escherichia_coli KW - fba KW - keio KW - metabolic-reconstruction KW - metabolic_networks KW - model_modification KW - model_prediction KW - optimization AU - Kumar, Vinay AU - Maranas, Costas PY - 2009/03/13/ UR - http://dx.doi.org/10.1371/journal.pcbi.1000308 ER -