Computational methods for integrating metabolomics data with metabolic engineering strain design

The genome-scale analysis of cellular metabolites, “metabolomics”, provides data ideal for applications in metabolic engineering. However, many of the computational tools for strain design are built around Flux Balance Analysis (FBA) and were developed using assumptions that preclude direct integration of metabolomics data into the underlying models. To improve their accuracy, we have focused on developing strategies to account for metabolite levels and metabolite-dependent regulation in these tools and models. We demonstrated the competitiveness of a biologically-inspired “Impulse” function from the transcriptional profiling literature against previously described fitting schemas to show that it may serve as an effective single option for data smoothing in metabolic flux estimation applications. We also developed a resampling-based approach to buffer out sensitivity to specific data sets and allow for more accurate fitting of noisy data. We designed, implemented, and characterized a modeling framework based on dynamic FBA (DFBA) to add strictly linear constraints describing the kinetics and regulation of metabolism. We identified model parameters using both regression from the flux distribution calculated with Dynamic Flux Estimation and global parameter optimization to produce models that performed comparable to or better than Ordinary Differential Equation models fitted by regression to generalized-mass-action rate laws. We demonstrated the efficacy of our framework in a larger, biologically relevant model, assessed the consequences and benefits of two different parameterization structures, and explored the impact of regulatory structure on model behavior to determine its robustness and the viability of using a greedy search method to identify regulatory interactions. The work described has led to the development of a modeling framework that allows widely-used tools for metabolic engineering strain design to directly account for and integrate metabolomics data, metabolite dynamics, and metabolite-dependent regulation.Ph.D

Systematic Applications of Metabolomics in Metabolic Engineering

The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering

Identification of a metabolomic signature associated with feed efficiency in beef cattle

Abstract Background Ruminants play a great role in sustainable livestock since they transform pastures, silage, and crop residues into high-quality human food (i.e. milk and beef). Animals with better ability to convert food into animal protein, measured as a trait called feed efficiency (FE), also produce less manure and greenhouse gas per kilogram of produced meat. Thus, the identification of high feed efficiency cattle is important for sustainable nutritional management. Our aim was to evaluate the potential of serum metabolites to identify FE of beef cattle before they enter the feedlot. Results A total of 3598 and 4210 m/z features was detected in negative and positive ionization modes via liquid chromatography-mass spectrometry. A single feature was different between high and low FE groups. Network analysis (WGCNA) yielded the detection of 19 and 20 network modules of highly correlated features in negative and positive mode respectively, and 1 module of each acquisition mode was associated with RFI (r = 0.55, P < 0.05). Pathway enrichment analysis (Mummichog) yielded the Retinol metabolism pathway associated with feed efficiency in beef cattle in our conditions. Conclusion Altogether, these findings demonstrate the existence of a serum-based metabolomic signature associated with feed efficiency in beef cattle before they enter the feedlot. We are now working to validate the use of metabolites for identification of feed efficient animals for sustainable nutritional management