%0 Journal Article %1 Khandelwal2013Community %A Khandelwal, Ruchir A. %A Olivier, Brett G. %A Röling, Wilfred F. M. %A Teusink, Bas %A Bruggeman, Frank J. %D 2013 %I Public Library of Science %J PLoS ONE %K communities consortia flux-analysis %N 5 %P e64567+ %R 10.1371/journal.pone.0064567 %T Community Flux Balance Analysis for Microbial Consortia at Balanced Growth %U http://dx.doi.org/10.1371/journal.pone.0064567 %V 8 %X A central focus in studies of microbial communities is the elucidation of the relationships between genotype, phenotype, and dynamic community structure. Here, we present a new computational method called community flux balance analysis (cFBA) to study the metabolic behavior of microbial communities. cFBA integrates the comprehensive metabolic capacities of individual microorganisms in terms of (genome-scale) stoichiometric models of metabolism, and the metabolic interactions between species in the community and abiotic processes. In addition, cFBA considers constraints deriving from reaction stoichiometry, reaction thermodynamics, and the ecosystem. cFBA predicts for communities at balanced growth the maximal community growth rate, the required rates of metabolic reactions within and between microbes and the relative species abundances. In order to predict species abundances and metabolic activities at the optimal community growth rate, a nonlinear optimization problem needs to be solved. We outline the methodology of cFBA and illustrate the approach with two examples of microbial communities. These examples illustrate two useful applications of cFBA. Firstly, cFBA can be used to study how specific biochemical limitations in reaction capacities cause different types of metabolic limitations that microbial consortia can encounter. In silico variations of those maximal capacities allow for a global view of the consortium responses to various metabolic and environmental constraints. Secondly, cFBA is very useful for comparing the performance of different metabolic cross-feeding strategies to either find one that agrees with experimental data or one that is most efficient for the community of microorganisms.

@article{Khandelwal2013Community, abstract = {A central focus in studies of microbial communities is the elucidation of the relationships between genotype, phenotype, and dynamic community structure. Here, we present a new computational method called community flux balance analysis ({cFBA}) to study the metabolic behavior of microbial communities. {cFBA} integrates the comprehensive metabolic capacities of individual microorganisms in terms of (genome-scale) stoichiometric models of metabolism, and the metabolic interactions between species in the community and abiotic processes. In addition, {cFBA} considers constraints deriving from reaction stoichiometry, reaction thermodynamics, and the ecosystem. {cFBA} predicts for communities at balanced growth the maximal community growth rate, the required rates of metabolic reactions within and between microbes and the relative species abundances. In order to predict species abundances and metabolic activities at the optimal community growth rate, a nonlinear optimization problem needs to be solved. We outline the methodology of {cFBA} and illustrate the approach with two examples of microbial communities. These examples illustrate two useful applications of {cFBA}. Firstly, {cFBA} can be used to study how specific biochemical limitations in reaction capacities cause different types of metabolic limitations that microbial consortia can encounter. In silico variations of those maximal capacities allow for a global view of the consortium responses to various metabolic and environmental constraints. Secondly, {cFBA} is very useful for comparing the performance of different metabolic cross-feeding strategies to either find one that agrees with experimental data or one that is most efficient for the community of microorganisms.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Khandelwal, Ruchir A. and Olivier, Brett G. and R\"{o}ling, Wilfred F. M. and Teusink, Bas and Bruggeman, Frank J.}, biburl = {https://www.bibsonomy.org/bibtex/20550e477848f73b94f64394b16404808/karthikraman}, citeulike-article-id = {12758847}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pone.0064567}, day = 31, doi = {10.1371/journal.pone.0064567}, interhash = {226ab14577c35181062b047e79c77ab0}, intrahash = {0550e477848f73b94f64394b16404808}, journal = {PLoS ONE}, keywords = {communities consortia flux-analysis}, month = may, number = 5, pages = {e64567+}, posted-at = {2013-11-06 08:16:43}, priority = {2}, publisher = {Public Library of Science}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {Community Flux Balance Analysis for Microbial Consortia at Balanced Growth}, url = {http://dx.doi.org/10.1371/journal.pone.0064567}, volume = 8, year = 2013 }