omFBA: Integrate Multi-omics Data to Flux Balance Analysis to Better Understand Cell Metabolism
Weihua Guo, Xueyang Feng*
Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24060
Constraint-based metabolic modeling such as flux balance analysis (FBA) has been widely used to simulate cell metabolism. Thanks to its simplicity and flexibility, numerous algorithms have been developed based on FBA and successfully predicted the phenotypes of various biological systems. However, their phenotype predictions may not always be accurate in FBA because of using the objective function that is assumed for cell metabolism. To overcome this challenge, we have developed a novel algorithm, namely omFBA, to integrate multi-omics data (e.g. transcriptomics, proteomics and metabolomics) into FBA to obtain omics-guided objective functions with high accuracy. In general, we first collected multi-omics data and phenotype data from published database (e.g. GEO database) for different microorganisms such as Saccharomyces cerevisiae. We then developed a “Phenotype Match” algorithm to derive an objective function for FBA that can lead to the most accurate estimation of the known phenotype (e.g. biomass yield). The derived objective function was next correlated with multi-omics data via regression analysis to generate the omics-guided objective functions, which will be further used to accurately simulate cell metabolism. We have applied omFBA in both E. coli and S. cerevisiae and found that the ethanol yield and biomass yield can be accurately predicted in most of the cases tested (~70%) by using transcriptomics data. In addition, omFBA could be incorporated with other existing algorithms easily by using the omics-guided objective functions. Compared to current approaches that attempt to integrate omics data to FBA (e.g. ROOM), omFBA can be more advantages in extendibility and comparability.
See more of this Group/Topical: Topical Conference: Emerging Frontiers in Systems and Synthetic Biology