Modeling the differences in biochemical capabilities of pseudomonas species by flux balance analysis: how good are genome-scale metabolic networks at predicting the differences?
To date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capabilities of closely related species using their metabolic models. The accuracy of a model is highly dependent on the reconstruction process, as some errors may be included in the model during reconstruction. In this study, we investigated the ability of three Pseudomonas metabolic models to predict the biochemical differences, namely, iMO1086, iJP962, and iSB1139, which are related to P. aeruginosa PAO1, P. putida KT2440, and P. fluorescens SBW25, respectively. We did a comprehensive literature search for previous works containing biochemically distinguishable traits over these species. Amongst more than 1700 articles, we chose a subset of them which included experimental results suitable for in silico simulation. By simulating the conditions provided in the actual biological experiment, we performed case-dependent tests to compare the in silico results to the biological ones. We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139.
Department of Biotechnology, College of Science, University of Tehran, Tehran 1417614411, Iran.
Parizad
P
Babaei
Department of Biotechnology, College of Science, University of Tehran, Tehran 1417614411, Iran.
Tahereh
0000-0001-6809-0741
T
Ghasemi-Kahrizsangi
Department of Biotechnology, College of Science, University of Tehran, Tehran 1417614411, Iran.
Sayed-Amir
0000-0001-9801-7449
SA
Marashi
D005544:Forecasting; D016680:Genome, Bacterial / Q000502:physiology*; D064688:Metabolic Flux Analysis / Q000379:methods; D053858:Metabolic Networks and Pathways / Q000502:physiology*; D008954:Models, Biological; D011549:Pseudomonas / Q000235:genetics / Q000378:metabolism*