OptMDFpathway: Identification of metabolic pathways with maximal thermodynamic driving force and its application for analyzing the endogenous CO2 fixation potential of Escherichia coli

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

Artificial carbon assimilation: From unnatural reactions and pathways to synthetic autotrophic systems

Synthetic biology is being increasingly used to establish novel carbon assimilation pathways and artificial autotrophic strains that can be used in low-carbon biomanufacturing. Currently, artificial pathway design has made significant progress from advocacy to practice within a relatively short span of just over ten years. However, there is still huge scope for exploration of pathway diversity, operational efficiency, and host suitability. The accelerated research process will bring greater opportunities and challenges. In this paper, we provide a comprehensive summary and interpretation of representative one-carbon assimilation pathway designs and artificial autotrophic strain construction work. In addition, we propose some feasible design solutions based on existing research results and patterns to promote the development and application of artificial autotrophy.

Design and thermodynamic analysis of a pathway enabling anaerobic production of poly-3-hydroxybutyrate in Escherichia coli

Utilizing anaerobic metabolisms for the production of biotechnologically relevant products presents potential advantages, such as increased yields and reduced energy dissipation. However, lower energy dissipation may indicate that certain reactions are operating closer to their thermodynamic equilibrium. While stoichiometric analyses and genetic modifications are frequently employed in metabolic engineering, the use of thermodynamic tools to evaluate the feasibility of planned interventions is less documented. In this study, we propose a novel metabolic engineering strategy to achieve an efficient anaerobic production of poly-(R)-3-hydroxybutyrate (PHB) in the model organism Escherichia coli. Our approach involves re-routing of two-thirds of the glycolytic flux through non-oxidative glycolysis and coupling PHB synthesis with NADH re-oxidation. We complemented our stoichiometric analysis with various thermodynamic approaches to assess the feasibility and the bottlenecks in the proposed engineered pathway. According to our calculations, the main thermodynamic bottleneck are the reactions catalyzed by the acetoacetyl-CoA β-ketothiolase (EC 2.3.1.9) and the acetoacetyl-CoA reductase (EC 1.1.1.36). Furthermore, we calculated thermodynamically consistent sets of kinetic parameters to determine the enzyme amounts required for sustaining the conversion fluxes. In the case of the engineered conversion route, the protein pool necessary to sustain the desired fluxes could account for 20% of the whole cell dry weight.

Improving pathway prediction accuracy of constraints-based metabolic network models by treating enzymes as microcompartments

Metabolic network models have become increasingly precise and accurate as the most widespread and practical digital representations of living cells. The prediction functions were significantly expanded by integrating cellular resources and abiotic constraints in recent years. However, if unreasonable modeling methods were adopted due to a lack of consideration of biological knowledge, the conflicts between stoichiometric and other constraints, such as thermodynamic feasibility and enzyme resource availability, would lead to distorted predictions. In this work, we investigated a prediction anomaly of EcoETM, a constraints-based metabolic network model, and introduced the idea of enzyme compartmentalization into the analysis process. Through rational combination of reactions, we avoid the false prediction of pathway feasibility caused by the unrealistic assumption of free intermediate metabolites. This allowed us to correct the pathway structures of l-serine and l-tryptophan. A specific analysis explains the application method of the EcoETM-like model and demonstrates its potential and value in correcting the prediction results in pathway structure by resolving the conflict between different constraints and incorporating the evolved roles of enzymes as reaction compartments. Notably, this work also reveals the trade-off between product yield and thermodynamic feasibility. Our work is of great value for the structural improvement of constraints-based models.

The potential of CO2-based production cycles in biotechnology to fight the climate crisis

Rising CO2 emissions have pushed scientists to develop new technologies for a more sustainable bio-based economy. Microbial conversion of CO2 and CO2-derived carbon substrates into valuable compounds can contribute to carbon neutrality and sustainability. Here, we discuss the potential of C1 carbon sources as raw materials to produce energy, materials, and food and feed using microbial cell factories. We provide an overview of potential microbes, natural and synthetic C1 utilization pathways, and compare their metabolic driving forces. Finally, we sketch a future in which C1 substrates replace traditional feedstocks and we evaluate the costs associated with such an endeavor.

Network-wide thermodynamic constraints shape NAD(P)H cofactor specificity of biochemical reactions

The ubiquitous coexistence of the redox cofactors NADH and NADPH is widely considered to facilitate an efficient operation of cellular redox metabolism. However, it remains unclear what shapes the NAD(P)H specificity of specific redox reactions. Here, we present a computational framework to analyze the effect of redox cofactor swaps on the maximal thermodynamic potential of a metabolic network and use it to investigate key aspects of redox cofactor redundancy in Escherichia coli. As one major result, our analysis suggests that evolved NAD(P)H specificities are largely shaped by metabolic network structure and associated thermodynamic constraints enabling thermodynamic driving forces that are close or even identical to the theoretical optimum and significantly higher compared to random specificities. Furthermore, while redundancy of NAD(P)H is clearly beneficial for thermodynamic driving forces, a third redox cofactor would require a low standard redox potential to be advantageous. Our approach also predicts trends of redox-cofactor concentration ratios and could facilitate the design of optimal redox cofactor specificities.

Optimal evaluation of energy yield and driving force in microbial metabolic pathway variants

This work presents a methodology to evaluate the bioenergetic feasibility of alternative metabolic pathways for a given microbial conversion, optimising their energy yield and driving forces as a function of the concentration of metabolic intermediates. The tool, based on thermodynamic principles and multi-objective optimisation, accounts for pathway variants in terms of different electron carriers, as well as energy conservation (proton translocating) reactions within the pathway. The method also accommodates other constraints, some of them non-linear, such as the balance of conserved moieties. The approach involves the transformation of the maximum energy yield problem into a multi-objective mixed-integer linear optimisation problem which is then subsequently solved using the epsilon-constraint method, highlighting the trade-off between yield and rate in metabolic reactions. The methodology is applied to analyse several pathway alternatives occurring during propionate oxidation in anaerobic fermentation processes, as well as to the reverse TCA cycle pathway occurring during autotrophic microbial CO2 fixation. The results obtained using the developed methodology match previously reported literature and bring about insights into the studied pathways.

Rethinking Biosynthesis of Aclacinomycin A

Aclacinomycin A (ACM-A) is an anthracycline antitumor agent widely used in clinical practice. The current industrial production of ACM-A relies primarily on chemical synthesis and microbial fermentation. However, chemical synthesis involves multiple reactions which give rise to high production costs and environmental pollution. Microbial fermentation is a sustainable strategy, yet the current fermentation yield is too low to satisfy market demand. Hence, strain improvement is highly desirable, and tremendous endeavors have been made to decipher biosynthesis pathways and modify key enzymes. In this review, we comprehensively describe the reported biosynthesis pathways, key enzymes, and, especially, catalytic mechanisms. In addition, we come up with strategies to uncover unknown enzymes and improve the activities of rate-limiting enzymes. Overall, this review aims to provide valuable insights for complete biosynthesis of ACM-A.

etiBsu1209: A comprehensive multiscale metabolic model for Bacillus subtilis

Genome-scale metabolic models (GEMs) have been widely used to guide the computational design of microbial cell factories, and to date, seven GEMs have been reported for Bacillus subtilis, a model gram-positive microorganism widely used in bioproduction of functional nutraceuticals and food ingredients. However, none of them are widely used because they often lead to erroneous predictions due to their low predictive power and lack of information on regulatory mechanisms. In this work, we constructed a new version of GEM for B. subtilis (iBsu1209), which contains 1209 genes, 1595 metabolites, and 1948 reactions. We applied machine learning to fill gaps, which formed a relatively complete metabolic network able to predict with high accuracy (89.3%) the growth of 1209 mutants under 12 different culture conditions. In addition, we developed a visualization and code-free software, Model Tool, for multiconstraints model reconstruction and analysis. We used this software to construct etiBsu1209, a multiscale model that integrates enzymatic constraints, thermodynamic constraints, and transcriptional regulatory networks. Furthermore, we used etiBsu1209 to guide a metabolic engineering strategy (knocking out fabI and yfkN genes) for the overproduction of nutraceutical menaquinone-7, and the titer increased to 153.94 mg/L, 2.2-times that of the parental strain. To the best of our knowledge, etiBsu1209 is the first comprehensive multiscale model for B. subtilis and can serve as a solid basis for rational computational design of B. subtilis cell factories for bioproduction.

Enzymatic Conversion of CO2: From Natural to Artificial Utilization

Enzymatic carbon dioxide fixation is one of the most important metabolic reactions as it allows the capture of inorganic carbon from the atmosphere and its conversion into organic biomass. However, due to the often unfavorable thermodynamics and the difficulties associated with the utilization of CO₂, a gaseous substrate that is found in comparatively low concentrations in the atmosphere, such reactions remain challenging for biotechnological applications. Nature has tackled these problems by evolution of dedicated CO₂-fixing enzymes, i.e., carboxylases, and embedding them in complex metabolic pathways. Biotechnology employs such carboxylating and decarboxylating enzymes for the carboxylation of aromatic and aliphatic substrates either by embedding them into more complex reaction cascades or by shifting the reaction equilibrium via reaction engineering. This review aims to provide an overview of natural CO₂-fixing enzymes and their mechanistic similarities. We also discuss biocatalytic applications of carboxylases and decarboxylases for the synthesis of valuable products and provide a separate summary of strategies to improve the efficiency of such processes. We briefly summarize natural CO₂ fixation pathways, provide a roadmap for the design and implementation of artificial carbon fixation pathways, and highlight examples of biocatalytic cascades involving carboxylases. Additionally, we suggest that biochemical utilization of reduced CO₂ derivates, such as formate or methanol, represents a suitable alternative to direct use of CO₂ and provide several examples. Our discussion closes with a techno-economic perspective on enzymatic CO₂ fixation and its potential to reduce CO₂ emissions.

Thermodynamic limitations of PHB production from formate and fructose in Cupriavidus necator

The chemolithotroph Cupriavidus necator H16 is known as a natural producer of the bioplastic-polymer PHB, as well as for its metabolic versatility to utilize different substrates, including formate as the sole carbon and energy source. Depending on the entry point of the substrate, this versatility requires adjustment of the thermodynamic landscape to maintain sufficiently high driving forces for biological processes. Here we employed a model of the core metabolism of C. necator H16 to analyze the thermodynamic driving forces and PHB yields from formate for different metabolic engineering strategies. For this, we enumerated elementary flux modes (EFMs) of the network and evaluated their PHB yields as well as thermodynamics via Max-min driving force (MDF) analysis and random sampling of driving forces. A heterologous ATP:citrate lyase reaction was predicted to increase driving force for producing acetyl-CoA. A heterologous phosphoketolase reaction was predicted to increase maximal PHB yields as well as driving forces. These enzymes were then verified experimentally to enhance PHB titers between 60 and 300% in select conditions. The EFM analysis also revealed that PHB production from formate may be limited by low driving forces through citrate lyase and aconitase, as well as cofactor balancing, and identified additional reactions associated with low and high PHB yield. Proteomics analysis of the engineered strains confirmed an increased abundance of aconitase and cofactor balancing. The findings of this study aid in understanding metabolic adaptation. Furthermore, the outlined approach will be useful in designing metabolic engineering strategies in other non-model bacteria.

A review of recent advances in engineering bacteria for enhanced CO2 capture and utilization

Carbon dioxide (CO₂) is emitted into the atmosphere due to some anthropogenic activities, such as the combustion of fossil fuels and industrial output. As a result, fears about catastrophic global warming and climate change have intensified. In the face of these challenges, conventional CO₂ capture technologies are typically ineffective, dangerous, and contribute to secondary pollution in the environment. Biological systems for CO₂ conversion, on the other hand, provide a potential path forward owing to its high application selectivity and adaptability. Moreover, many bacteria can use CO₂ as their only source of carbon and turn it into value-added products. The purpose of this review is to discuss recent significant breakthroughs in engineering bacteria to utilize CO₂ and other one-carbon compounds as substrate. In the same token, the paper also summarizes and presents aspects such as microbial CO₂ fixation pathways, engineered bacteria involved in CO₂ fixation, up-to-date genetic and metabolic engineering approaches for CO₂ fixation, and promising research directions for the production of value-added products from CO₂. This review's findings imply that using biological systems like modified bacteria to manage CO₂ has the added benefit of generating useful industrial byproducts like biofuels, pharmaceutical compounds, and bioplastics. The major downside, from an economic standpoint, thus far has been related to methods of cultivation. However, thanks to genetic engineering approaches, this can be addressed by large production yields. As a result, this review aids in the knowledge of various biological systems that can be used to construct a long-term CO₂ mitigation technology at an industrial scale, in this instance bacteria-based CO₂capture/utilization technology.

Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges

Genome-scale metabolic models (GEMs) are effective tools for metabolic engineering and have been widely used to guide cell metabolic regulation. However, the single gene–protein-reaction data type in GEMs limits the understanding of biological complexity. As a result, multiscale models that add constraints or integrate omics data based on GEMs have been developed to more accurately predict phenotype from genotype. This review summarized the recent advances in the development of multiscale GEMs, including multiconstraint, multiomic, and whole-cell models, and outlined machine learning applications in GEM construction. This review focused on the frameworks, toolkits, and algorithms for constructing multiscale GEMs. The challenges and perspectives of multiscale GEM development are also discussed.

Novel computational and experimental approaches for investigating the thermodynamics of metabolic networks

Thermodynamic analysis of metabolic networks has emerged as a useful new tool for pathway design and metabolic engineering. Understanding the relationship between the thermodynamic driving force of biochemical reactions and metabolic flux has generated new insights regarding the design principles of microbial carbon metabolism. This review summarizes the various lessons that can be obtained from the thermodynamic analysis of metabolic pathways, illustrates concepts of computational thermodynamic tools, and highlights recent applications of thermodynamic analysis to pathway design in industrially relevant microbes.

eQuilibrator 3.0: a database solution for thermodynamic constant estimation

eQuilibrator (equilibrator.weizmann.ac.il) is a database of biochemical equilibrium constants and Gibbs free energies, originally designed as a web-based interface. While the website now counts around 1,000 distinct monthly users, its design could not accommodate larger compound databases and it lacked a scalable Application Programming Interface (API) for integration into other tools developed by the systems biology community. Here, we report on the recent updates to the database as well as the addition of a new Python-based interface to eQuilibrator that adds many new features such as a 100-fold larger compound database, the ability to add novel compounds, improvements in speed and memory use, and correction for Mg²⁺ ion concentrations. Moreover, the new interface can compute the covariance matrix of the uncertainty between estimates, for which we show the advantages and describe the application in metabolic modelling. We foresee that these improvements will make thermodynamic modelling more accessible and facilitate the integration of eQuilibrator into other software platforms.

In-Depth Computational Analysis of Natural and Artificial Carbon Fixation Pathways

In the recent years, engineering new-to-nature CO2- and C1-fixing metabolic pathways made a leap forward. New, artificial pathways promise higher yields and activity than natural ones like the Calvin-Benson-Bassham (CBB) cycle. The question remains how to best predict their in vivo performance and what actually makes one pathway "better" than another. In this context, we explore aerobic carbon fixation pathways by a computational approach and compare them based on their specific activity and yield on methanol, formate, and CO2/H2 considering the kinetics and thermodynamics of the reactions. Besides pathways found in nature or implemented in the laboratory, this included two completely new cycles with favorable features: the reductive citramalyl-CoA cycle and the 2-hydroxyglutarate-reverse tricarboxylic acid cycle. A comprehensive kinetic data set was collected for all enzymes of all pathways, and missing kinetic data were sampled with the Parameter Balancing algorithm. Kinetic and thermodynamic data were fed to the Enzyme Cost Minimization algorithm to check for respective inconsistencies and calculate pathway-specific activities. The specific activities of the reductive glycine pathway, the CETCH cycle, and the new reductive citramalyl-CoA cycle were predicted to match the best natural cycles with superior product-substrate yield. However, the CBB cycle performed better in terms of activity compared to the alternative pathways than previously thought. We make an argument that stoichiometric yield is likely not the most important design criterion of the CBB cycle. Still, alternative carbon fixation pathways were paretooptimal for specific activity and product-substrate yield in simulations with C1 substrates and CO2/H2 and therefore hold great potential for future applications in Industrial Biotechnology and Synthetic Biology.

Enhancing in silico strain design predictions through next generation metabolic modeling approaches

The reconstruction and analysis of metabolic models has garnered increasing attention due to the multitude of applications in which these have proven to be practical. The growing number of generated metabolic models has been accompanied by an exponentially expanding arsenal of tools used to analyze them. In this work, we discussed the biological relevance of a number of promising modeling frameworks, focusing on the questions and hypotheses each method is equipped to address. To this end, we critically analyzed the steady-state modeling approaches focusing on resource allocation and incorporation of thermodynamic considerations which produce promising results and aid in the generation and experimental validation of numerous predictions. For smaller networks involving more complex regulation, we addressed kinetic modeling techniques which show encouraging results in addressing questions outside the scope of steady-state modeling. Finally, we discussed the potential application of the discussed frameworks within the field of strain design. Adoption of such methodologies is believed to significantly enhance the accuracy of in silico predictions and hence decrease the number of design-build-test cycles required.

The view of microbes as energy converters illustrates the trade-off between growth rate and yield

The application of thermodynamics to microbial growth has a long tradition that originated in the middle of the 20th century. This approach reflects the view that self-replication is a thermodynamic process that is not fundamentally different from mechanical thermodynamics. The key distinction is that a free energy gradient is not converted into mechanical (or any other form of) energy but rather into new biomass. As such, microbes can be viewed as energy converters that convert a part of the energy contained in environmental nutrients into chemical energy that drives self-replication. Before the advent of high-throughput sequencing technologies, only the most central metabolic pathways were known. However, precise measurement techniques allowed for the quantification of exchanged extracellular nutrients and heat of growing microbes with their environment. These data, together with the absence of knowledge of metabolic details, drove the development of so-called black-box models, which only consider the observable interactions of a cell with its environment and neglect all details of how exactly inputs are converted into outputs. Now, genome sequencing and genome-scale metabolic models (GEMs) provide us with unprecedented detail about metabolic processes inside the cell. However, mostly due to computational complexity issues, the derived modelling approaches make surprisingly little use of thermodynamic concepts. Here, we review classical black-box models and modern approaches that integrate thermodynamics into GEMs. We also illustrate how the description of microbial growth as an energy converter can help to understand and quantify the trade-off between microbial growth rate and yield.

Integrating thermodynamic and enzymatic constraints into genome-scale metabolic models

Stoichiometric genome-scale metabolic network models (GEMs) have been widely used to predict metabolic phenotypes. In addition to stoichiometric ratios, other constraints such as enzyme availability and thermodynamic feasibility can also limit the phenotype solution space. Extended GEM models considering either enzymatic or thermodynamic constraints have been shown to improve prediction accuracy. In this paper, we propose a novel method that integrates both enzymatic and thermodynamic constraints in a single Pyomo modeling framework (ETGEMs). We applied this method to construct the EcoETM (E. coli metabolic model with enzymatic and thermodynamic constraints). Using this model, we calculated the optimal pathways for cellular growth and the production of 22 metabolites. When comparing the results with those of iML1515 and models with one of the two constraints, we observed that many thermodynamically unfavorable and/or high enzyme cost pathways were excluded from EcoETM. For example, the synthesis pathway of carbamoyl-phosphate (Cbp) from iML1515 is both thermodynamically unfavorable and enzymatically costly. After introducing the new constraints, the production pathways and yields of several Cbp-derived products (e.g. L-arginine, orotate) calculated using EcoETM were more realistic. The results of this study demonstrate the great application potential of metabolic models with multiple constraints for pathway analysis and phenotype prediction.

Designing microbial communities to maximize the thermodynamic driving force for the production of chemicals

Microbial communities have become a major research focus due to their importance for biogeochemical cycles, biomedicine and biotechnological applications. While some biotechnological applications, such as anaerobic digestion, make use of naturally arising microbial communities, the rational design of microbial consortia for bio-based production processes has recently gained much interest. One class of synthetic microbial consortia is based on specifically designed strains of one species. A common design principle for these consortia is based on division of labor, where the entire production pathway is divided between the different strains to reduce the metabolic burden caused by product synthesis. We first show that classical division of labor does not automatically reduce the metabolic burden when metabolic flux per biomass is analyzed. We then present ASTHERISC (Algorithmic Search of THERmodynamic advantages in Single-species Communities), a new computational approach for designing multi-strain communities of a single-species with the aim to divide a production pathway between different strains such that the thermodynamic driving force for product synthesis is maximized. ASTHERISC exploits the fact that compartmentalization of segments of a product pathway in different strains can circumvent thermodynamic bottlenecks arising when operation of one reaction requires a metabolite with high and operation of another reaction the same metabolite with low concentration. We implemented the ASTHERISC algorithm in a dedicated program package and applied it on E. coli core and genome-scale models with different settings, for example, regarding number of strains or demanded product yield. These calculations showed that, for each scenario, many target metabolites (products) exist where a multi-strain community can provide a thermodynamic advantage compared to a single strain solution. In some cases, a production with sufficiently high yield is thermodynamically only feasible with a community. In summary, the developed ASTHERISC approach provides a promising new principle for designing microbial communities for the bio-based production of chemicals.

Communities of microbes are ubiquitous in nature and also of high relevance for industrial applications, e.g. for the production of biogas. The development and use of non-natural communities for biotechnological applications has become an important subject of research. In this work, we present a new computational method to design synthetic communities with improved capabilities for the synthesis of desired target metabolites. Our method takes a constraint-based metabolic model of an organism as input and searches for a suitable partitioning of the product pathway via different strains of the organism such that the thermodynamic driving force for product synthesis is maximized. Essentially, this approach exploits the fact that having multiple strains allows adjustment of different metabolite concentrations in the different strains by which the thermodynamic driving force for product synthesis can often be increased. We tested this approach with a core and with a genome-scale metabolic network model of Escherichia coli. We found that, for dozens of metabolites, there exist communities with specifically designed strains of E. coli where the maximal thermodynamic driving force can be increased compared to a single E. coli strain. In summary, our presented method provides a new approach, together with a new design principle, for the computational design of microbial communities.

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.

Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms

Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.

Awakening a latent carbon fixation cycle in Escherichia coli

Carbon fixation is one of the most important biochemical processes. Most natural carbon fixation pathways are thought to have emerged from enzymes that originally performed other metabolic tasks. Can we recreate the emergence of a carbon fixation pathway in a heterotrophic host by recruiting only endogenous enzymes? In this study, we address this question by systematically analyzing possible carbon fixation pathways composed only of Escherichia coli native enzymes. We identify the GED (Gnd-Entner-Doudoroff) cycle as the simplest pathway that can operate with high thermodynamic driving force. This autocatalytic route is based on reductive carboxylation of ribulose 5-phosphate (Ru5P) by 6-phosphogluconate dehydrogenase (Gnd), followed by reactions of the Entner-Doudoroff pathway, gluconeogenesis, and the pentose phosphate pathway. We demonstrate the in vivo feasibility of this new-to-nature pathway by constructing E. coli gene deletion strains whose growth on pentose sugars depends on the GED shunt, a linear variant of the GED cycle which does not require the regeneration of Ru5P. Several metabolic adaptations, most importantly the increased production of NADPH, assist in establishing sufficiently high flux to sustain this growth. Our study exemplifies a trajectory for the emergence of carbon fixation in a heterotrophic organism and demonstrates a synthetic pathway of biotechnological interest.

Bacterial synthesis of C3-C5 diols via extending amino acid catabolism

Amino acids are naturally occurring and structurally diverse metabolites in biological system, whose potentials for chemical expansion, however, have not been fully explored. Here, we devise a metabolic platform capable of producing industrially important C3-C5 diols from amino acids. The presented platform combines the natural catabolism of charged amino acids with a catalytically efficient and thermodynamically favorable diol formation pathway, created by expanding the substrate scope of the carboxylic acid reductase toward noncognate ω-hydroxylic acids. Using the established platform as gateways, seven different diol-convertible amino acids are converted to diols including 1,3-propanediol, 1,4-butanediol, and 1,5-pentanediol. Particularly, we afford to optimize the production of 1,4-butanediol and demonstrate the de novo production of 1,5-pentanediol from glucose, with titers reaching 1.41 and 0.97 g l^-1, respectively. Our work presents a metabolic platform that enriches the pathway repertoire for nonnatural diols with feedstock flexibility to both sugar and protein hydrolysates.

Thermodynamic Favorability of End Products of Anaerobic Glucose Metabolism

The eQuilibrator component contribution method allows calculation of the overall Gibbs energy changes for conversion of glucose to a wide range of final products in the absence of other oxidants. Values are presented for all possible combinations of products with up to three carbons and selected others. The most negative Gibbs energy change is for the formation of graphite and water (-499 kJ mol-1) followed by CH4 and CO2 (-430 kJ mol-1), the observed final products of anaerobic digestion. Other favored products (with various combinations having Gibbs energy changes between -300 and -367 kJ mol-1) are short-chain alkanes, fatty acids, dicarboxylic acids, and even hexane and benzene. The most familiar products, lactate and ethanol + CO2, are less favored (Gibbs energy changes of -206 and -265 kJ mol-1 respectively). The values presented offer an interesting perspective on observed metabolism and its evolutionary origins as well as on cells engineered for biotechnological purposes.

Characterizing and ranking computed metabolic engineering strategies

MOTIVATION

The computer-aided design of metabolic intervention strategies has become a key component of an integrated metabolic engineering approach and a broad range of methods and algorithms has been developed for this task. Many of these algorithms enforce coupling of growth with product synthesis and may return thousands of possible intervention strategies from which the most suitable strategy must then be selected.

RESULTS

This work focuses on how to evaluate and rank, in a meaningful way, a given pool of computed metabolic engineering strategies for growth-coupled product synthesis. Apart from straightforward criteria, such as a preferably small number of necessary interventions, a reasonable growth rate and a high product yield, we present several new criteria useful to pick the most suitable intervention strategy. Among others, we investigate the robustness of the intervention strategies by searching for metabolites that may disrupt growth coupling when accumulated or secreted and by checking whether the interventions interrupt pathways at their origin (preferable) or at downstream steps. We also assess thermodynamic properties of the pathway(s) favored by the intervention strategy. Furthermore, strategies that have a significant overlap with alternative solutions are ranked higher because they provide flexibility in implementation. We also introduce the notion of equivalence classes for grouping intervention strategies with identical solution spaces. Our ranking procedure involves in total ten criteria and we demonstrate its applicability by assessing knockout-based intervention strategies computed in a genome-scale model of E.coli for the growth-coupled synthesis of l-methionine and of the heterologous product 1,4-butanediol.

AVAILABILITY AND IMPLEMENTATION

The MATLAB scripts that were used to characterize and rank the example intervention strategies are available at http://www2.mpi-magdeburg.mpg.de/projects/cna/etcdownloads.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

MEMO: A Method for Computing Metabolic Modules for Cell-Free Production Systems

Cell-free bioproduction systems represent a promising alternative to classical microbial fermentation processes to synthesize value-added products from biological feedstocks. An essential step for establishing cell-free production systems is the identification of suitable metabolic modules with defined properties. Here we present MEMO, a novel computational approach to find smallest metabolic modules with specified stoichiometric and thermodynamic constraints supporting the design of cell-free systems in various regards. In particular, one key challenge for a sustained operation of cell-free systems is the regeneration of utilized cofactors (such as ATP and NAD(P)H). Given a production pathway with certain cofactor requirements, MEMO can be used to compute smallest regeneration modules that recover these cofactors with required stoichiometries. MEMO incorporates the stoichiometric and thermodynamic constraints in a single mixed-integer linear program, which can then be solved to find smallest suitable modules from a given reaction database. We illustrate the applicability of MEMO by calculating regeneration modules for the recently published synthetic CETCH cycle for in vitro carbon dioxide fixation. We demonstrate that MEMO is very flexible in taking into account the diverse constraints of the CETCH cycle (e.g., regeneration of 1 ATP, 4 NADPH and of 1 acetyl-group without net production of CO₂ and with permitted side production of malate) and is able to determine multiple solutions in reasonable time in two large reaction databases (MetaCyc and BiGG). The most promising regeneration modules found utilize glycerol as substrate and require only 8 enzymatic steps. It is also shown that some of these modules are robust against spontaneous loss of cofactors (e.g., oxidation of NAD(P)H or hydrolysis of ATP). Furthermore, we demonstrate that MEMO can also find cell-free production systems with integrated product synthesis and cofactor regeneration. Overall, MEMO provides a powerful method for finding metabolic modules and for designing cell-free production systems as one particular application.

Thermodynamic analysis of the pathway for ethanol production from cellobiose in Clostridium thermocellum

Clostridium thermocellum is a candidate for consolidated bioprocessing by carrying out both cellulose solubilization and fermentation. However, despite significant efforts the maximum ethanol titer achieved to date remains below industrially required targets. Several studies have analyzed the impact of increasing ethanol concentration on C. thermocellum's membrane properties, cofactor pool ratios, and altered enzyme regulation. In this study, we explore the extent to which thermodynamic equilibrium limits maximum ethanol titer. We used the max-min driving force (MDF) algorithm (Noor et al., 2014) to identify the range of allowable metabolite concentrations that maintain a negative free energy change for all reaction steps in the pathway from cellobiose to ethanol. To this end, we used a time-series metabolite concentration dataset to flag five reactions (phosphofructokinase (PFK), fructose bisphosphate aldolase (FBA), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), aldehyde dehydrogenase (ALDH) and alcohol dehydrogenase (ADH)) which become thermodynamic bottlenecks under high external ethanol concentrations. Thermodynamic analysis was also deployed in a prospective mode to evaluate genetic interventions which can improve pathway thermodynamics by generating minimal set of reactions or elementary flux modes (EFMs) which possess unique genetic variations while ensuring mass and redox balance with ethanol production. MDF evaluation of all generated (336) EFMs indicated that, i) pyruvate phosphate dikinase (PPDK) has a higher pathway MDF than the malate shunt alternative due to limiting CO₂ concentrations under physiological conditions, and ii) NADPH-dependent glyceraldehyde-3-phosphate dehydrogenase (GAPN) can alleviate thermodynamic bottlenecks at high ethanol concentrations due to cofactor modification and reduction in ATP generation. The combination of ATP linked phosphofructokinase (PFK-ATP) and NADPH linked alcohol dehydrogenase (ADH-NADPH) with NADPH linked aldehyde dehydrogenase (ALDH-NADPH) or ferredoxin: NADP ​+ ​oxidoreductase (NADPH-FNOR) emerges as the best intervention strategy for ethanol production that balances MDF improvements with ATP generation, and appears to functionally reproduce the pathway employed by the ethanologen Thermoanaerobacterium saccharolyticum. Expanding the list of measured intracellular metabolites and improving the quantification accuracy of measurements was found to improve the fidelity of pathway thermodynamics analysis in C. thermocellum. This study demonstrates even before addressing an organism's enzyme kinetics and allosteric regulations, pathway thermodynamics can flag pathway bottlenecks and identify testable strategies for enhancing pathway thermodynamic feasibility and function.