Statistics-based model for prediction of chemical biosynthesis yield from Saccharomyces cerevisiae

Abstract

BackgroundThe robustness of Saccharomyces cerevisiae in facilitating industrial-scale production of ethanol extends its utilization as a platform to synthesize other metabolites. Metabolic engineering strategies, typically via pathway overexpression and deletion, continue to play a key role for optimizing the conversion efficiency of substrates into the desired products. However, chemical production titer or yield remains difficult to predict based on reaction stoichiometry and mass balance. We sampled a large space of data of chemical production from S. cerevisiae, and developed a statistics-based model to calculate production yield using input variables that represent the number of enzymatic steps in the key biosynthetic pathway of interest, metabolic modifications, cultivation modes, nutrition and oxygen availability.ResultsBased on the production data of about 40 chemicals produced from S. cerevisiae, metabolic engineering methods, nutrient supplementation, and fermentation conditions described therein, we generated mathematical models with numerical and categorical variables to predict production yield. Statistically, the models showed that: 1. Chemical production from central metabolic precursors decreased exponentially with increasing number of enzymatic steps for biosynthesis (>30% loss of yield per enzymatic step, P-value = 0); 2. Categorical variables of gene overexpression and knockout improved product yield by 2~4 folds (P-value < 0.1); 3. Addition of notable amount of intermediate precursors or nutrients improved product yield by over five folds (P-value < 0.05); 4. Performing the cultivation in a well-controlled bioreactor enhanced the yield of product by three folds (P-value < 0.05); 5. Contribution of oxygen to product yield was not statistically significant. Yield calculations for various chemicals using the linear model were in fairly good agreement with the experimental values. The model generally underestimated the ethanol production as compared to other chemicals, which supported the notion that the metabolism of Saccharomyces cerevisiae has historically evolved for robust alcohol fermentation.ConclusionsWe generated simple mathematical models for first-order approximation of chemical production yield from S. cerevisiae. These linear models provide empirical insights to the effects of strain engineering and cultivation conditions toward biosynthetic efficiency. These models may not only provide guidelines for metabolic engineers to synthesize desired products, but also be useful to compare the biosynthesis performance among different research papers.

Metabolic Burden: Cornerstones in Synthetic Biology and Metabolic Engineering Applications

“…To demonstrate the highly nonlinear impact of energy burden on biosynthesis, flux balance analysis (FBA) was used with a constraint-based model to simulate the adverse impacts of ATP metabolism on E. coli fatty acid, isobutanol and acetate yields ( Figure 1) [19]. The simulations showed that native cell metabolism can withstand a certain amount of ATP loss without having apparent biosynthesis deficiency (i.e., forming a -plateau‖).…”

“…These multi-scale models can offer knowledge of intracellular function and overall fermentation performance. For example, dynamic FBA (a combination of kinetic models and FBA) simulates the industrial fermentation of E. coli to produce recombinant proteins [19]. The hybrid of constrained FBA models with agent-based model successfully simulate highly heterogeneous systems (e.g., biofilm) [104].…”

Metabolic Engineering of Synechocystis sp. Strain PCC 6803 for Isobutanol Production

“…Among the next-generation biofuels synthesized from pyruvate, IB possesses fewer reaction steps (5 reaction steps from pyruvate to IB) than the synthesis of 1-butanol or biodiesel. IB is less toxic to microbes (5), so it may achieve a higher product titer and yield (6,7). For example, a maximum titer of 50.8 g/liter of IB can be achieved in an engineered Escherichia coli strain (8).…”

Metabolic analyses elucidate non-trivial gene targets for amplifying dihydroartemisinic acid production in yeast

“…Thus, while FBA is valuable in identifying non-trivial gene targets, it is ambitious to expect the real engineered strain to exhibit yields close to the FBA-predicted yields. Additionally, a recent statistical analysis of previous studies on the production of chemicals by engineered S. cerevisiae found that genetic intervention improved product yield only by two- to fourfold in a majority of the 40 studies examined, and that chemicals requiring multiple enzymatic steps were associated with lower yields (Varman et al, 2011). Along the same lines, implementation of the best gene knockout strategy suggested by our study may result in a DHA yield improvement tangibly lower than the predicted 70-fold increase.…”