Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.

Pareto Optimality Explanation of the Glycolytic Alternatives in Nature.

The Entner-Doudoroff (ED) and Embden-Meyerhof-Parnas (EMP) glycolytic pathways are largely conserved across glycolytic species in nature. Is this a coincidence, convergent evolution or there exists a driving force towards either of the two pathway designs? We addressed this question by first employing a variant of the optStoic algorithm to exhaustively identify over 11,916 possible routes between glucose and pyruvate at different pre-determined stoichiometric yields of ATP. Subsequently, we analyzed the thermodynamic feasibility of all the pathways at physiological metabolite concentrations and quantified the protein cost of the feasible solutions. Pareto optimality analysis between energy efficiency and protein cost reveals that the naturally evolved ED and EMP pathways are indeed among the most protein cost-efficient pathways in their respective ATP yield categories and remain thermodynamically feasible across a wide range of ATP/ADP ratios and pathway intermediate metabolite concentration ranges. In contrast, pathways with higher ATP yield (>2) while feasible, are bound within stringent and often extreme operability ranges of cofactor and intermediate metabolite concentrations. The preponderance of EMP and ED is thus consistent with not only optimally balancing energy yield vs. enzyme cost but also with ensuring operability for wide metabolite concentration ranges and ATP/ADP ratios.

Exploring the combinatorial space of complete pathways to chemicals.

Computational pathway design tools often face the challenges of balancing the stoichiometry of co-metabolites and cofactors, and dealing with reaction rule utilization in a single workflow. To this end, we provide an overview of two complementary stoichiometry-based pathway design tools optStoic and novoStoic developed in our group to tackle these challenges. optStoic is designed to determine the stoichiometry of overall conversion first which optimizes a performance criterion (e.g. high carbon/energy efficiency) and ensures a comprehensive search of co-metabolites and cofactors. The procedure then identifies the minimum number of intervening reactions to connect the source and sink metabolites. We also further the pathway design procedure by expanding the search space to include both known and hypothetical reactions, represented by reaction rules, in a new tool termed novoStoic. Reaction rules are derived based on a mixed-integer linear programming (MILP) compatible reaction operator, which allow us to explore natural promiscuous enzymes, engineer candidate enzymes that are not already promiscuous as well as design de novo enzymes. The identified biochemical reaction rules then guide novoStoic to design routes that expand the currently known biotransformation space using a single MILP modeling procedure. We demonstrate the use of the two computational tools in pathway elucidation by designing novel synthetic routes for isobutanol.

Pathway design using de novo steps through uncharted biochemical spaces.

Existing retrosynthesis tools generally traverse production routes from a source to a sink metabolite using known enzymes or de novo steps. Generally, important considerations such as blending known transformations with putative steps, complexity of pathway topology, mass conservation, cofactor balance, thermodynamic feasibility, microbial chassis selection, and cost are largely dealt with in a posteriori fashion. The computational procedure we present here designs bioconversion routes while simultaneously considering any combination of the aforementioned design criteria. First, we track and codify as rules all reaction centers using a prime factorization-based encoding technique (rePrime). Reaction rules and known biotransformations are then simultaneously used by the pathway design algorithm (novoStoic) to trace both metabolites and molecular moieties through balanced bio-conversion strategies. We demonstrate the use of novoStoic in bypassing steps in existing pathways through putative transformations, assembling complex pathways blending both known and putative steps toward pharmaceuticals, and postulating ways to biodegrade xenobiotics.

A review of computational tools for design and reconstruction of metabolic pathways.

Metabolic pathways reflect an organism's chemical repertoire and hence their elucidation and design have been a primary goal in metabolic engineering. Various computational methods have been developed to design novel metabolic pathways while taking into account several prerequisites such as pathway stoichiometry, thermodynamics, host compatibility, and enzyme availability. The choice of the method is often determined by the nature of the metabolites of interest and preferred host organism, along with computational complexity and availability of software tools. In this paper, we review different computational approaches used to design metabolic pathways based on the reaction network representation of the database (i.e., graph or stoichiometric matrix) and the search algorithm (i.e., graph search, flux balance analysis, or retrosynthetic search). We also put forth a systematic workflow that can be implemented in projects requiring pathway design and highlight current limitations and obstacles in computational pathway design.

Metabolic modeling of clostridia: current developments and applications.

Anaerobic Clostridium spp. is an important bioproduction microbial genus that can produce solvents and utilize a broad spectrum of substrates including cellulose and syngas. Genome-scale metabolic (GSM) models are increasingly being put forth for various clostridial strains to explore their respective metabolic capabilities and suitability for various bioconversions. In this study, we have selected representative GSM models for six different clostridia (Clostridium acetobutylicum, C. beijerinckii, C. butyricum, C. cellulolyticum, C. ljungdahlii and C. thermocellum) and performed a detailed model comparison contrasting their metabolic repertoire. We also discuss various applications of these GSM models to guide metabolic engineering interventions as well as assessing cellular physiology.

Advances in de novo strain design using integrated systems and synthetic biology tools.

Recent efforts in expanding the range of biofuel and biorenewable molecules using microbial production hosts have focused on the introduction of non-native pathways in model organisms and the bio-prospecting of non-model organisms with desirable features. Current challenges lie in the assembly and coordinated expression of the (non-)native pathways and the elimination of competing pathways and undesirable regulation. Several systems and synthetic biology approaches providing contrasting top-down and bottom-up strategies, respectively, have been developed. In this review, we discuss recent advances in both in silico and experimental approaches for metabolic pathway design and engineering, with a critical assessment of their merits and remaining challenges.

Rational design of a synthetic Entner-Doudoroff pathway for improved and controllable NADPH regeneration.

NADPH is an essential cofactor for the biosynthesis of several high-value chemicals, including isoprenoids, fatty acid-based fuels, and biopolymers. Tunable control over all potentially rate-limiting steps, including the NADPH regeneration rate, is crucial to maximizing production titers. We have rationally engineered a synthetic version of the Entner-Doudoroff pathway from Zymomonas mobilis that increased the NADPH regeneration rate in Escherichia coli MG1655 by 25-fold. To do this, we combined systematic design rules, biophysical models, and computational optimization to design synthetic bacterial operons expressing the 5-enzyme pathway, while eliminating undesired genetic elements for maximum expression control. NADPH regeneration rates from genome-integrated pathways were estimated using a NADPH-binding fluorescent reporter and by the productivity of a NADPH-dependent terpenoid biosynthesis pathway. We designed and constructed improved pathway variants by employing the RBS Library Calculator to efficiently search the 5-dimensional enzyme expression space and by performing 40 cycles of MAGE for site-directed genome mutagenesis. 624 pathway variants were screened using a NADPH-dependent blue fluorescent protein, and 22 were further characterized to determine the relationship between enzyme expression levels and NADPH regeneration rates. The best variant exhibited 25-fold higher normalized mBFP levels when compared to wild-type strain. Combining the synthetic Entner-Doudoroff pathway with an optimized terpenoid pathway further increased the terpenoid titer by 97%.

Production of 2,3-butanediol in Saccharomyces cerevisiae by in silico aided metabolic engineering.

BACKGROUND: 2,3-Butanediol is a chemical compound of increasing interest due to its wide applications. It can be synthesized via mixed acid fermentation of pathogenic bacteria such as Enterobacter aerogenes and Klebsiella oxytoca. The non-pathogenic Saccharomyces cerevisiae possesses three different 2,3-butanediol biosynthetic pathways, but produces minute amount of 2,3-butanediol. Hence, we attempted to engineer S. cerevisiae strain to enhance 2,3-butanediol production. RESULTS: We first identified gene deletion strategy by performing in silico genome-scale metabolic analysis. Based on the best in silico strategy, in which disruption of alcohol dehydrogenase (ADH) pathway is required, we then constructed gene deletion mutant strains and performed batch cultivation of the strains. Deletion of three ADH genes, ADH1, ADH3 and ADH5, increased 2,3-butanediol production by 55-fold under microaerobic condition. However, overproduction of glycerol was observed in this triple deletion strain. Additional rational design to reduce glycerol production by GPD2 deletion altered the carbon fluxes back to ethanol and significantly reduced 2,3-butanediol production. Deletion of ALD6 reduced acetate production in strains lacking major ADH isozymes, but it did not favor 2,3-butanediol production. Finally, we introduced 2,3-butanediol biosynthetic pathway from Bacillus subtilis and E. aerogenes to the engineered strain and successfully increased titer and yield. Highest 2,3-butanediol titer (2.29 . l-1) and yield (0.113 g . g-1) were achieved by Δadh1 Δadh3 Δadh5 strain under anaerobic condition. CONCLUSIONS: With the aid of in silico metabolic engineering, we have successfully designed and constructed S. cerevisiae strains with improved 2,3-butanediol production.

Deletion of lactate dehydrogenase in Enterobacter aerogenes to enhance 2,3-butanediol production.

2,3-Butanediol is an important bio-based chemical product, because it can be converted into several C4 industrial chemicals. In this study, a lactate dehydrogenase-deleted mutant was constructed to improve 2,3-butanediol productivity in Enterobacter aerogenes. To delete the gene encoding lactate dehydrogenase, λ Red recombination method was successfully adapted for E. aerogenes. The resulting strain produced a very small amount of lactate and 16.7% more 2,3-butanediol than that of the wild-type strain in batch fermentation. The mutant and its parental strain were then cultured with six different carbon sources, and the mutant showed higher carbon source consumption and microbial growth rates in all media. The 2,3-butanediol titer reached 69.5 g/l in 54 h during fed-batch fermentation with the mutant,which was 27.4% higher than that with the parental strain.With further optimization of the medium and aeration conditions,118.05 g/l 2,3-butanediol was produced in 54 h during fed-batch fermentation with the mutant. This is by far the highest titer of 2,3-butanediol with E. aerogenes achieved by metabolic pathway engineering.