Computational methods for integrating metabolomics data with metabolic engineering strain design

Abstract

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.