OptStrain: a computational framework for redesign of microbial production systems

Abstract

This paper introduces the hierarchical computational framework OptStrain aimed at guiding pathway modifications, through reaction additions and deletions, of microbial networks for the overproduction of targeted compounds. These compounds may range from electrons or hydrogen in biofuel cell and environmental applications to complex drug precursor molecules. A comprehensive database of biotransformations, referred to as the Universal database (with >5700 reactions), is compiled and regularly updated by downloading and curating reactions from multiple biopathway database sources. Combinatorial optimization is then used to elucidate the set(s) of non-native functionalities, extracted from this Universal database, to add to the examined production host for enabling the desired product formation. Subsequently, competing functionalities that divert flux away from the targeted product are identified and removed to ensure higher product yields coupled with growth. This work represents an advancement over earlier efforts by establishing an integrated computational framework capable of constructing stoichiometrically balanced pathways, imposing maximum product yield requirements, pinpointing the optimal substrate(s), and evaluating different microbial hosts. The range and utility of OptStrain are demonstrated by addressing two very different product molecules. The hydrogen case study pinpoints reaction elimination strategies for improving hydrogen yields using two different substrates for three separate production hosts. In contrast, the vanillin study primarily showcases which non-native pathways need to be added into Escherichia coli. In summary, OptStrain provides a useful tool to aid microbial strain design and, more importantly, it establishes an integrated framework to accommodate future modeling developments.

Figure 1.

Pictorial representation of the OptStrain procedure. Step 1 involves the curation of database(s) of reactions to compile the Universal database, which comprises only elementally balanced reactions. Step 2 identifies a maximum-yield path enabling the desired biotransformation from a substrate (e.g., glucose, methanol, xylose) to product (e.g., hydrogen, vanillin) without any consideration for the origin of reactions. Note that the white arrows represent native reactions of the host and the yellow arrows denote non-native reactions. Step 3 minimizes the reliance on non-native reactions, and Step 4 incorporates the non-native functionalities into the microbial host's stoichiometric model and applies the OptKnock procedure to identify and eliminate reactions competing with the targeted product. The red ×s pinpoint the deleted reactions.

Figure 2.

Maximum hydrogen yield on a weight basis for different substrates.

Figure 3.

Hydrogen production envelopes as a function of the biomass production rate of the wild-type E. coli network under aerobic and anaerobic conditions as well as the two-reaction and three-reaction deletion mutant networks. The basis glucose uptake rate is fixed at 10 mmol/gDW per hour. These curves are constructed by finding the maximum and minimum hydrogen production rates at different rates of biomass formation. Point A denotes the required theoretical hydrogen production rate at the maximum biomass formation rate of the wild-type network under anaerobic conditions. Points B and C identify the theoretical hydrogen production rates at maximum growth for the two mutant networks, respectively, after fixing the corresponding carbon dioxide transport rates at the values suggested by OptKnock.

Figure 4.

Calculated flux distributions at the maximum growth rates in the (A) two and (B) three deletion E. coli mutant networks for overproducing hydrogen. A basis glucose uptake rate of 10 mmol/gDW per hour was assumed.

Figure 5.

Calculated flux distributions at the maximum growth rates for the wild-type (light gray) and the two-reaction deletion mutant (dark gray) C. acetobutylicum networks. The ×s denote reactions that were selected for elimination in the mutant network. The wild-type network flux values are for the minimum hydrogen production scenario, corresponding to point A in Figure 6.

Figure 6.

Hydrogen formation limits of the wild-type (solid) and mutant (dotted) Clostridium acetobutylicum metabolic network for a basis glucose uptake rate of 1 mmol/gDW per hour. Line AB denotes different alternate yield solutions that are available to the wild-type network at maximum biomass production rates. Point C pinpoints the hydrogen yield of the mutant network at maximum growth. This can be contrasted with the reported experimental hydrogen yield (2 mol/mol glucose) in C. acetobutylicum (Nandi and Sengupta 1998).

Figure 7.

Calculated flux distributions at the maximum growth rates in the (A) one, (B) two, and (C) four deletion E. coli mutant networks for overproducing vanillin. Non-native reactions are denoted by the thicker gray arrows. A basic glucose uptake rate of 10 mmol/gDW per hour was assumed.

Figure 8.

Vanillin production envelope of the augmented E. coli metabolic network for a basic 10 mmol/gDW per hour uptake rate of glucose. Points A, B, and C denote the maximum growth points associated with the one, two, and four reaction deletion mutant networks, respectively. Note that the level “augmented” refers to the E. coli wild-type network augmented with the three non-native reactions. An anaerobic mode of growth is suggested in all cases.