Abstract
The success of genome-scale metabolic modeling is contingent on a model's ability to accurately predict growth and metabolic behaviors. To date, little focus has been directed towards developing systematic methods of proposing, modifying and interrogating an organism's biomass requirements that are used in constraint-based models. To address this gap, the biomass modification and generation (BioMog) framework was created and used to generate lists of biomass components de novo, as well as to modify predefined biomass component lists, for models of Escherichia coli (iJO1366) and of Shewanella oneidensis (iSO783) from high-throughput growth phenotype and fitness datasets. BioMog's de novo biomass component lists included, either implicitly or explicitly, up to seventy percent of the components included in the predefined biomass equations, and the resulting de novo biomass equations outperformed the predefined biomass equations at qualitatively predicting mutant growth phenotypes by up to five percent. Additionally, the BioMog procedure can quantify how many experiments support or refute a particular metabolite's essentiality to a cell, and it facilitates the determination of inconsistent experiments and inaccurate reaction and/or gene to reaction associations. To further interrogate metabolite essentiality, the BioMog framework includes an experiment generation algorithm that allows for the design of experiments to test whether a metabolite is essential. Using BioMog, we correct experimental results relating to the essentiality of thyA gene in E. coli, as well as perform knockout experiments supporting the essentiality of protoheme. With these capabilities, BioMog can be a valuable resource for analyzing growth phenotyping data and component of a model developer's toolbox.
Highlights
The accuracy of constraint-based model predictions and the application of such models in metabolic engineering are contingent on a biologically accurate biomass equation
We have developed the novel biomass modification and generation (BioMog) framework as a means to determine, de novo, biomass components that are consistent with large numbers of high-throughput growth phenotype datasets, which are becomingly increasingly facile and inexpensive to create [4,5]
The BioMog framework uses the concept of blocked metabolites and growth phenotype data to determine potential biomass components that will yield fatal and non-fatal knockout predictions for no growth and growth experimental results, respectively
Summary
The accuracy of constraint-based model predictions (e.g., of growth phenotypes) and the application of such models in metabolic engineering (e.g., strain design for biofuel production) are contingent on a biologically accurate biomass equation. While numerous computational methods exist to curate genome-scale models by interrogating, hypothesizing and refining an organism’s reaction, gene-reaction and/or transcriptional regulatory networks [1,2,3] comparatively little has been done to automate the generation and modification of an organism’s biomass requirements To fill this niche, we have developed the novel biomass modification and generation (BioMog) framework as a means to determine, de novo, biomass components that are consistent with large numbers (containing 1000 s of unique experiments) of high-throughput growth phenotype datasets, which are becomingly increasingly facile and inexpensive to create [4,5]. BioMog can be used to complement existing tools by suggesting other types of model adjustments to improve model predictions
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