Abstract
BackgroundThe systems-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), which makes assumptions that preclude direct integration of metabolomics data into the underlying models. Finding a way to retain the advantages of FBA’s linear structure while relaxing some of its assumptions could allow us to account for metabolite levels and metabolite-dependent regulation in strain design tools built from FBA, improving the accuracy of predictions made by these tools. We designed, implemented, and characterized a modeling strategy based on Dynamic FBA (DFBA), called Linear Kinetics-Dynamic Flux Balance Analysis (LK-DFBA), to satisfy these specifications. Our strategy adds constraints describing the dynamics and regulation of metabolism that are strictly linear. We evaluated LK-DFBA against alternative modeling frameworks using simulated noisy data from a small in silico model and a larger model of central carbon metabolism in E. coli, and compared each framework’s ability to recapitulate the original system.ResultsIn the smaller model, we found that we could use regression from a dynamic flux estimation (DFE) with an optional non-linear parameter optimization to reproduce metabolite concentration dynamic trends more effectively than an ordinary differential equation model with generalized mass action rate laws when tested under realistic data sampling frequency and noise levels. We observed detrimental effects across all tested modeling approaches when metabolite time course data were missing, but found these effects to be smaller for LK-DFBA in most cases. With the E. coli model, we produced qualitatively reasonable results with similar properties to the smaller model and explored two different parameterization structures that yield trade-offs in computation time and accuracy.ConclusionsLK-DFBA allows for calculation of metabolite concentrations and considers metabolite-dependent regulation while still retaining many computational advantages of FBA. This provides the proof-of-principle for a new metabolic modeling framework with the potential to create genome-scale dynamic models and the potential to be applied in strain engineering tools that currently use FBA.
Highlights
The systems-scale analysis of cellular metabolites, “metabolomics,” provides data ideal for applications in metabolic engineering
As a first demonstration of the proof-of-principle for such an approach, we explored this framework in two model systems of varying scale, generating in silico reference time course data and noisy synthetic time course data by varying the data sampling frequency and the coefficient of variance (CoV) of added noise
Comparing the performance of methods using noisy data in the branched pathway model We generated high-resolution noisy time course data for the modified branched pathway model using the k = 1 parameter set by downsampling the high resolution data to nT = 15, 20, 30, 40, and 50, and adding multiplicative Gaussian noise to data points after the initial time point with CoV = 0.05, 0.15, and 0.25
Summary
The systems-scale analysis of cellular metabolites, “metabolomics,” provides data ideal for applications in metabolic engineering. Many of the computational tools for strain design are built around Flux Balance Analysis (FBA), which makes assumptions that preclude direct integration of metabolomics data into the underlying models. We designed, implemented, and characterized a modeling strategy based on Dynamic FBA (DFBA), called Linear Kinetics-Dynamic Flux Balance Analysis (LK-DFBA), to satisfy these specifications. Metabolism is the biochemical supply chain for all other cellular processes, such as DNA replication, transcription of RNA, and protein synthesis. It is perhaps the most immediate readout available of cellular state. Metabolic modeling and computational strain design tools are valuable methods for metabolic engineers to deal with these interactions, allowing them to more strategically allocate the significant time and resources required to produce an engineered strain in the lab
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