Flux Balance Analysis of Mammalian Cell Systems

Flux balance analysis (FBA) is a computational methodology to model and analyze the metabolic behavior of cells. In this chapter, we break down the key steps for formulating an FBA model and other FBA-derived methodologies in the context of mammalian cell biology, including strain design, developing cell line-specific models, and conducting flux sampling. We provide annotated COBRApy code for each step to show how it would work in practice.

A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.

Unlocking the magic in mycelium: Using synthetic biology to optimize filamentous fungi for biomanufacturing and sustainability

Filamentous fungi drive carbon and nutrient cycling across our global ecosystems, through its interactions with growing and decaying flora and their constituent microbiomes. The remarkable metabolic diversity, secretion ability, and fiber-like mycelial structure that have evolved in filamentous fungi have been increasingly exploited in commercial operations. The industrial potential of mycelial fermentation ranges from the discovery and bioproduction of enzymes and bioactive compounds, the decarbonization of food and material production, to environmental remediation and enhanced agricultural production. Despite its fundamental impact in ecology and biotechnology, molds and mushrooms have not, to-date, significantly intersected with synthetic biology in ways comparable to other industrial cell factories (e.g. Escherichia coli,Saccharomyces cerevisiae, and Komagataella phaffii). In this review, we summarize a suite of synthetic biology and computational tools for the mining, engineering and optimization of filamentous fungi as a bioproduction chassis. A combination of methods across genetic engineering, mutagenesis, experimental evolution, and computational modeling can be used to address strain development bottlenecks in established and emerging industries. These include slow mycelium growth rate, low production yields, non-optimal growth in alternative feedstocks, and difficulties in downstream purification. In the scope of biomanufacturing, we then detail previous efforts in improving key bottlenecks by targeting protein processing and secretion pathways, hyphae morphogenesis, and transcriptional control. Bringing synthetic biology practices into the hidden world of molds and mushrooms will serve to expand the limited panel of host organisms that allow for commercially-feasible and environmentally-sustainable bioproduction of enzymes, chemicals, therapeutics, foods, and materials of the future.

Acetone–Butanol–Ethanol Fermentation Phenomenological Models for Process Studies: Parameter Estimation and Multi-Response Model Reduction with Statistical Analysis

A phenomenological multi-response multi-parameter Acetone–Butanol–Ethanol fermentation dynamic model is developed and calibrated for fermentation process studies. The model was constructed based on other models reported in the literature and was calibrated with a maximum likelihood parameter estimation over Acetone–Butanol–Ethanol fermentation experimental data from the literature. After parameter estimation, a rigorous statistical analysis was conducted to evaluate standard deviations of estimated parameters and predicted responses as well as their respective 95% probability confidence intervals for correct parameters and responses. The significance of parameters was assessed via a Fisher’s F test. From the Base-Model with 17 parameters, a tight, more compact, Reduced-Model was developed with 9 highly significant parameters after deleting 8 nonsignificant parameters from the Base-Model and re-estimating the remaining 9 parameters. This Reduced-Model showed good adherence to the experimental data and had better performance comparatively relative to the Base-Model with 17 parameters using two different inhibition functions reported in the literature. The Reduced-Model is sufficiently good for preliminary engineering and economic assessments of ABE fermentation processes.

Optimization and Scale-Up of Fermentation Processes Driven by Models

In the era of sustainable development, the use of cell factories to produce various compounds by fermentation has attracted extensive attention; however, industrial fermentation requires not only efficient production strains, but also suitable extracellular conditions and medium components, as well as scaling-up. In this regard, the use of biological models has received much attention, and this review will provide guidance for the rapid selection of biological models. This paper first introduces two mechanistic modeling methods, kinetic modeling and constraint-based modeling (CBM), and generalizes their applications in practice. Next, we review data-driven modeling based on machine learning (ML), and highlight the application scope of different learning algorithms. The combined use of ML and CBM for constructing hybrid models is further discussed. At the end, we also discuss the recent strategies for predicting bioreactor scale-up and culture behavior through a combination of biological models and computational fluid dynamics (CFD) models.

Molecular characterization of the missing electron pathways for butanol synthesis in Clostridium acetobutylicum

Clostridium acetobutylicum is a promising biocatalyst for the renewable production of n-butanol. Several metabolic strategies have already been developed to increase butanol yields, most often based on carbon pathway redirection. However, it has previously demonstrated that the activities of both ferredoxin-NADP⁺ reductase and ferredoxin-NAD⁺ reductase, whose encoding genes remain unknown, are necessary to produce the NADPH and the extra NADH needed for butanol synthesis under solventogenic conditions. Here, we purify, identify and partially characterize the proteins responsible for both activities and demonstrate the involvement of the identified enzymes in butanol synthesis through a reverse genetic approach. We further demonstrate the yield of butanol formation is limited by the level of expression of CA_C0764, the ferredoxin-NADP⁺ reductase encoding gene and the bcd operon, encoding a ferredoxin-NAD⁺ reductase. The integration of these enzymes into metabolic engineering strategies introduces opportunities for developing a homobutanologenic C. acetobutylicum strain.

Ferredoxin-NAD(P) + oxidoreductases are important enzymes for redox balancing in n-butanol production by Clostridium acetobutylicum, but the encoding genes remain unknown. Here, the authors identify the long sought-after genes and increase n-butanol production by optimizing the levels of the two enzymes.

Metabolism perturbation Causedby the overexpression of carbon monoxide dehydrogenase/Acetyl-CoA synthase gene complex accelerated gas to acetate conversion rate ofEubacterium limosumKIST612

Microbial conversion of carbon monoxide (CO) to acetate is a promising upcycling strategy for carbon sequestration. Herein, we demonstrate that CO conversion and acetate production rates of Eubacterium limosum KIST612 strain can be improved by in silico prediction and in vivo assessment. The mimicked CO metabolic model of KIST612 predicted that overexpressing the CO dehydrogenase (CODH) increases CO conversion and acetate production rates. To validate the prediction, we constructed mutant strains overexpressing CODH gene cluster and measured their CO conversion and acetate production rates. A mutant strain (ELM031) co-overexpressing CODH, coenzyme CooC2 and ACS showed a 3.1 × increased specific CO oxidation rate as well as 1.4 × increased specific acetate production rate, compared to the wild type strain. The transcriptional and translational data with redox balance analysis showed that ELM031 has enhanced reducing potential from up-regulation of ferredoxin and related metabolism directly linked to energy conservation.

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.

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.

A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications

The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, "Metabolic fluxes and metabolic engineering" (Metabolic Engineering, 1: 1-11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. ¹³C-metabolic flux analysis (¹³C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.

Succinic acid production from pulp and paper industry waste: A transcriptomic approach

The fermentative production of biobased chemicals and polymers using crude lignocellulose hydrolysates is challenging due to the presence of various inhibitory compounds and multiple sugars. This study evaluates the metabolic response of Actinobacillus succinogenes for the production of succinic acid using spent sulphite liquor (SSL) as feedstock derived from industrial acidic sulphite pulping of Eucalyptus globulus hardwood. A transcriptomic approach led to significant insights on gene regulation of the major metabolic pathways (glycolysis, pentose phosphate pathway, TCA cycle, pyruvate metabolism and oxidative phosphorylation) in batch cultures carried out on SSL and compared with glucose and xylose. Significantly overexpressed genes in SSL compared to glucose and xylose were fructose biphosphate aldolase (> 1.18-fold change) in the catabolism, phosphoenolpyruvate carboxykinase (> 1.59-fold change) and malate dehydrogenase (> 1.49-fold change) in the TCA cycle, citrate lyase (> 1.7-fold change), dihydrolipoamide dehydrogenase (> 0.88-fold change), pyruvate dehydrogenase E2 (> 1.63-fold change) and pyruvate formate lyase (> 0.61-fold change), involved in acetyl-CoA pathways. Finally, C4 tricarboxylic transporters were overexpressed (DCU (> 1.61-fold change) and 0079 (> 4.19-fold change). SSL was responsible for the upregulation of genes involved in the TCA cycle and oxidative phosphorylation, while xylose showed similar results with SSL in the oxidative phosphorylation.

Towards continuous industrial bioprocessing with solventogenic and acetogenic clostridia: challenges, progress and perspectives

The sustainable production of solvents from above ground carbon is highly desired. Several clostridia naturally produce solvents and use a variety of renewable and waste-derived substrates such as lignocellulosic biomass and gas mixtures containing H2/CO2 or CO. To enable economically viable production of solvents and biofuels such as ethanol and butanol, the high productivity of continuous bioprocesses is needed. While the first industrial-scale gas fermentation facility operates continuously, the acetone-butanol-ethanol (ABE) fermentation is traditionally operated in batch mode. This review highlights the benefits of continuous bioprocessing for solvent production and underlines the progress made towards its establishment. Based on metabolic capabilities of solvent producing clostridia, we discuss recent advances in systems-level understanding and genome engineering. On the process side, we focus on innovative fermentation methods and integrated product recovery to overcome the limitations of the classical one-stage chemostat and give an overview of the current industrial bioproduction of solvents.

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.

From Escherichia coli mutant 13C labeling data to a core kinetic model: A kinetic model parameterization pipeline

Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated.

Production of butanol from biomass: recent advances and future prospects

At present, diminishing oil resources and increasing environmental concerns have led to a shift toward the production of alternative biofuels. In the last few decades, butanol, as liquid biofuel, has received considerable research attention due to its advantages over ethanol. Several studies have focused on the production of butanol through the fermentation from raw renewable biomass, such as lignocellulosic materials. However, the low concentration and productivity of butanol production and the price of raw materials are limitations for butanol fermentation. Moreover, these limitations are the main causes of industrial decline in butanol production. This study reviews butanol fermentation, including the metabolism and characteristics of acetone-butanol-ethanol (ABE) producing clostridia. Furthermore, types of butanol production from biomass feedstock are detailed in this study. Specifically, this study introduces the recent progress on the efficient butanol production of "designed" and modified biomass. Additionally, the recent advances in the butanol fermentation process, such as multistage continuous fermentation, metabolic flow change of the electron carrier supplement, continuous fermentation with immobilization and recycling of cell, and the recent technical separation of the products from the fermentation broth, are described in this study.

Identifying the Characteristics of Promising Renewable Replacement Chemicals

Although several “renewable” strategies have been recently proposed to produce high-volume as well as new (replacement) chemicals, the identification of good targets for such strategies remains challenging. Such chemicals that are expensive to obtain today from fossil fuel feedstocks would have an advantage if produced cheaply using alternative methods in the future. In this work we identify the characteristics of such potentially promising replacement chemicals. We also identify the characteristic of promising bio-based replacement chemicals that are relatively easy to obtain through bio-conversions. This work provides insights into the development of renewable chemicals to support a sustainable economy.

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Identified molecular characteristics of potentially promising replacement chemicals

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Key characteristics: number of carbon and oxygen atoms and functional groups

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Proposed metric for evaluating bio-based production efficiency of various chemicals

Direct cell-to-cell exchange of matter in a synthetic Clostridium syntrophy enables CO2 fixation, superior metabolite yields, and an expanded metabolic space

In microbial fermentations at least 33% of the sugar-substrate carbon is lost as CO₂ during pyruvate decarboxylation to acetyl-CoA, with the corresponding electrons lost in the form of H₂. Previous attempts to reduce this carbon and electron loss focused on engineering of a single organism. In nature, most microorganisms live in complex communities where syntrophic interactions result in superior resource utilization. Here, we show that a synthetic syntrophy consisting of the solventogen Clostridium acetobutylicum, which converts simple and complex carbohydrates into a variety of chemicals, and the acetogen C. ljungdahlii which fixes CO₂, achieved carbon recoveries into C2-C4 alcohols almost to the limit of substrate-electron availability, with minimal H₂ and CO₂ release. The syntrophic co-culture produced robust metabolic outcomes over a broad range of starting population ratios of the two organisms. We show that direct cell-to-cell interactions and material exchange among the two microbes enabled unforeseen rearrangements in the metabolism of the individual species that resulted in the production of non-native metabolites, namely isopropanol and 2,3-butanediol. This was accomplished by pathway-specific alterations of gene expression brought about by one organism on the other, and vice versa. While some of these gene-expression alterations can be explained by the exchange of metabolites that induce specific gene expression patterns, others, as demonstrated by co-culture setup in a transwell system, cannot. The latter, for now, would be attributed to complex direct physical interactions among the two organisms, thus providing a glimpse of the potential microbial complexity of simple or multicomponent microbiomes. Such direct material-transfer phenomena have not been documented in the literature. Furthermore, our study shows that syntrophic cultures offer a flexible platform for metabolite production with superior carbon recovery that can also be applied to electron-enhanced fermentations enabling even higher carbon recoveries.

Analyzing and Designing Cell Factories with OptFlux

OptFlux was launched in 2010 as the first open-source and user-friendly platform containing all the major methods for performing metabolic engineering tasks in silico. Main features included the possibility of performing microbial strain simulations with widely used methods such as Flux Balance Analysis and strain design using Evolutionary Algorithms. Since then, OptFlux suffered a major re-factoring to improve its efficiency and reliability, while many features were added in the form of novel plug-ins, such as the BioVisualizer and the over/under expression plug-ins. The current chapter described the main mathematical formulations of the major methods implemented within OptFlux, also providing a detailed guide on the usage of those functionalities.

Recent trends in biobutanol production

Finite availability of conventional fossil carbonaceous fuels coupled with increasing pollution due to their overexploitation has necessitated the quest for renewable fuels. Consequently, biomass-derived fuels are gaining importance due to their economic viability and environment-friendly nature. Among various liquid biofuels, biobutanol is being considered as a suitable and sustainable alternative to gasoline. This paper reviews the present state of the preprocessing of the feedstock, biobutanol production through fermentation and separation processes. Low butanol yield and its toxicity are the major bottlenecks. The use of metabolic engineering and integrated fermentation and product recovery techniques has the potential to overcome these challenges. The application of different nanocatalysts to overcome the existing challenges in the biobutanol field is gaining much interest. For the sustainable production of biobutanol, algae, a third-generation feedstock has also been evaluated.

Improving flux predictions by integrating data from multiple strains

Motivation

Incorporating experimental data into constraint-based models can improve the quality and accuracy of their metabolic flux predictions. Unfortunately, routinely and easily measured experimental data such as growth rates, extracellular fluxes, transcriptomics and even proteomics are not always sufficient to significantly improve metabolic flux predictions.

Results

We developed a new method (called REPPS) for incorporating experimental measurements of growth rates and extracellular fluxes from a set of perturbed reference strains (RSs) and a parental strain (PS) to substantially improve the predicted flux distribution of the parental strain. Using data from five single gene knockouts and the wild type strain, we decrease the mean squared error of predicted central metabolic fluxes by ∼47% compared to parsimonious flux balance analysis (pFBA). This decrease in error further improves flux predictions for new knockout strains. Furthermore, REPPS is less sensitive to the completeness of the metabolic network than pFBA.

Availability and Implementation

Code is available in the Supplementary data available at Bioinformatics online.

Contact

reed@engr.wisc.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Amino acid catabolism-directed biofuel production in Clostridium sticklandii: An insight into model-driven systems engineering

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Model-driven systems engineering has been more fascinating process for microbial biofuel production.

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Clostridium sticklandii is a potential strain for the solventogenesis and acidogenesis.

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The present review provides an insight for the protein catabolism-directed biofuel production.

Model-driven systems engineering has been more fascinating process for the microbial production of biofuel and bio-refineries in chemical and pharmaceutical industries. Genome-scale modeling and simulations have been guided for metabolic engineering of Clostridium species for the production of organic solvents and organic acids. Among them, Clostridium sticklandii is one of the potential organisms to be exploited as a microbial cell factory for biofuel production. It is a hyper-ammonia producing bacterium and is able to catabolize amino acids as important carbon and energy sources via Stickland reactions and the development of the specific pathways. Current genomic and metabolic aspects of this bacterium are comprehensively reviewed herein, which provided information for learning about protein catabolism-directed biofuel production. It has a metabolic potential to drive energy and direct solventogenesis as well as acidogenesis from protein catabolism. It produces by-products such as ethanol, acetate, n-butanol, n-butyrate and hydrogen from amino acid catabolism. Model-driven systems engineering of this organism would improve the performance of the industrial sectors and enhance the industrial economy by using protein-based waste in environment-friendly ways.

Constraint-based stoichiometric modelling from single organisms to microbial communities

Microbial communities are ubiquitously found in Nature and have direct implications for the environment, human health and biotechnology. The species composition and overall function of microbial communities are largely shaped by metabolic interactions such as competition for resources and cross-feeding. Although considerable scientific progress has been made towards mapping and modelling species-level metabolism, elucidating the metabolic exchanges between microorganisms and steering the community dynamics remain an enormous scientific challenge. In view of the complexity, computational models of microbial communities are essential to obtain systems-level understanding of ecosystem functioning. This review discusses the applications and limitations of constraint-based stoichiometric modelling tools, and in particular flux balance analysis (FBA). We explain this approach from first principles and identify the challenges one faces when extending it to communities, and discuss the approaches used in the field in view of these challenges. We distinguish between steady-state and dynamic FBA approaches extended to communities. We conclude that much progress has been made, but many of the challenges are still open.

Evidence of mixotrophic carbon-capture by n-butanol-producer Clostridium beijerinckii

Recent efforts to combat increasing greenhouse gas emissions include their capture into advanced biofuels, such as butanol. Traditionally, biobutanol research has been centered solely on its generation from sugars. Our results show partial re-assimilation of CO₂ and H₂ by n-butanol-producer C. beijerinckii. This was detected as synchronous CO₂/H₂ oscillations by direct (real-time) monitoring of their fermentation gasses. Additional functional analysis demonstrated increased total carbon recovery above heterotrophic values associated to mixotrophic assimilation of synthesis gas (H₂, CO₂ and CO). This was further confirmed using ¹³C-Tracer experiments feeding ¹³CO₂ and measuring the resulting labeled products. Genome- and transcriptome-wide analysis revealed transcription of key C-1 capture and additional energy conservation genes, including partial Wood-Ljungdahl and complete reversed pyruvate ferredoxin oxidoreductase / pyruvate-formate-lyase-dependent (rPFOR/Pfl) pathways. Therefore, this report provides direct genetic and physiological evidences of mixotrophic inorganic carbon-capture by C. beijerinckii.

Metabolic flux network analysis of hydrogen production from crude glycerol by Clostridium pasteurianum

The present study has attempted to get insight into ultrasound induced enhancement in biohydrogen production from glycerol fermentation using metabolic flux analysis (MFA). A pseudo steady state metabolic flux network model was constructed and analyzed using experimentally measured glycerol uptake rate and fluxes of four metabolites, viz. acetate, butyrate, succinate and 1,3-PDO. Glycerol consumption increased by ∼50% under sonication. Biohydrogen yield showed marked rise of ∼40% with application of ultrasound. Butyrate and 1,3-PDO were the major products of glycerol metabolism. Sonication had major influence on carbon fluxes at the acetyl-CoA node. MFA results revealed enhanced flux towards butyrate under sonication, which was manifested in higher butyrate to acetate (B/A) ratio in products and greater H₂ generation. Biohydrogen production was also a microbial growth associated process. Finally, two theoretical alternatives for further enhancement of biohydrogen production were assessed with MFA, viz. enhancement of glycerol uptake and blocking of butyrate pathway.

Heterologous Expression of the Clostridium carboxidivorans CO Dehydrogenase Alone or Together with the Acetyl Coenzyme A Synthase Enables both Reduction of CO2 and Oxidation of CO by Clostridium acetobutylicum

With recent advances in synthetic biology, CO₂ could be utilized as a carbon feedstock by native or engineered organisms, assuming the availability of electrons. Two key enzymes used in autotrophic CO₂ fixation are the CO dehydrogenase (CODH) and acetyl coenzyme A (acetyl-CoA) synthase (ACS), which form a bifunctional heterotetrameric complex. The CODH/ACS complex can reversibly catalyze CO₂ to CO, effectively enabling a biological water-gas shift reaction at ambient temperatures and pressures. The CODH/ACS complex is part of the Wood-Ljungdahl pathway (WLP) used by acetogens to fix CO₂, and it has been well characterized in native hosts. So far, only a few recombinant CODH/ACS complexes have been expressed in heterologous hosts, none of which demonstrated in vivo CO₂ reduction. Here, functional expression of the Clostridium carboxidivorans CODH/ACS complex is demonstrated in the solventogen Clostridium acetobutylicum, which was engineered to express CODH alone or together with the ACS. Both strains exhibited CO₂ reduction and CO oxidation activities. The CODH reactions were interrogated using isotopic labeling, thus verifying that CO was a direct product of CO₂ reduction, and vice versa. CODH apparently uses a native C. acetobutylicum ferredoxin as an electron carrier for CO₂ reduction. Heterologous CODH activity depended on actively growing cells and required the addition of nickel, which is inserted into CODH without the need to express the native Ni insertase protein. Increasing CO concentrations in the gas phase inhibited CODH activity and altered the metabolite profile of the CODH-expressing cells. This work provides the foundation for engineering a complete and functional WLP in nonnative host organisms.IMPORTANCE Functional expression of CO dehydrogenase (CODH) from Clostridium carboxidivorans was demonstrated in C. acetobutylicum, which is natively incapable of CO₂ fixation. The expression of CODH, alone or together with the C. carboxidivorans acetyl-CoA synthase (ACS), enabled C. acetobutylicum to catalyze both CO₂ reduction and CO oxidation. Importantly, CODH exhibited activity in both the presence and absence of ACS. ¹³C-tracer studies confirmed that the engineered C. acetobutylicum strains can reduce CO₂ to CO and oxidize CO during growth on glucose.

redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models

Genome-scale metabolic reconstructions have proven to be valuable resources in enhancing our understanding of metabolic networks as they encapsulate all known metabolic capabilities of the organisms from genes to proteins to their functions. However the complexity of these large metabolic networks often hinders their utility in various practical applications. Although reduced models are commonly used for modeling and in integrating experimental data, they are often inconsistent across different studies and laboratories due to different criteria and detail, which can compromise transferability of the findings and also integration of experimental data from different groups. In this study, we have developed a systematic semi-automatic approach to reduce genome-scale models into core models in a consistent and logical manner focusing on the central metabolism or subsystems of interest. The method minimizes the loss of information using an approach that combines graph-based search and optimization methods. The resulting core models are shown to be able to capture key properties of the genome-scale models and preserve consistency in terms of biomass and by-product yields, flux and concentration variability and gene essentiality. The development of these “consistently-reduced” models will help to clarify and facilitate integration of different experimental data to draw new understanding that can be directly extendable to genome-scale models.

Reduced models are used commonly to understand the metabolism of organisms and to integrate experimental data for many different studies such as physiology, fluxomics and metabolomics. Without consistent or clear criteria on how these reduced models are actually developed, it is difficult to ensure that they reflect the detailed knowledge that is kept in genome scale metabolic network models (GEMs). The redGEM algorithm presented here allows us to systematically develop consistently reduced metabolic models from their genome-scale counterparts. We applied redGEM for the construction of a core model for E. coli central carbon metabolism. We constructed the core model irJO1366 based on the latest genome-scale E. coli metabolic reconstruction (iJO1366). irJO1366 contains the central carbon pathways and other immediate pathways that must be connected to them for consistency with the iJO1366. irJO1366 can be used to understand metabolism of the organism and also to provide guidance for metabolic engineering purposes. The algorithm is also designed to be modular so that heterologous reactions or pathways can be appended to the core model akin to a “plug-and-play”, synthetic biology approach. The algorithm is applicable to any compartmentalized or non-compartmentalized GEM.

lumpGEM: Systematic generation of subnetworks and elementally balanced lumped reactions for the biosynthesis of target metabolites

In the post-genomic era, Genome-scale metabolic networks (GEMs) have emerged as invaluable tools to understand metabolic capabilities of organisms. Different parts of these metabolic networks are defined as subsystems/pathways, which are sets of functional roles to implement a specific biological process or structural complex, such as glycolysis and TCA cycle. Subsystem/pathway definition is also employed to delineate the biosynthetic routes that produce biomass building blocks. In databases, such as MetaCyc and SEED, these representations are composed of linear routes from precursors to target biomass building blocks. However, this approach cannot capture the nested, complex nature of GEMs. Here we implemented an algorithm, lumpGEM, which generates biosynthetic subnetworks composed of reactions that can synthesize a target metabolite from a set of defined core precursor metabolites. lumpGEM captures balanced subnetworks, which account for the fate of all metabolites along the synthesis routes, thus encapsulating reactions from various subsystems/pathways to balance these metabolites in the metabolic network. Moreover, lumpGEM collapses these subnetworks into elementally balanced lumped reactions that specify the cost of all precursor metabolites and cofactors. It also generates alternative subnetworks and lumped reactions for the same metabolite, accounting for the flexibility of organisms. lumpGEM is applicable to any GEM and any target metabolite defined in the network. Lumped reactions generated by lumpGEM can be also used to generate properly balanced reduced core metabolic models.

Stoichiometric models have been used in the area of metabolic engineering and systems biology for many decades. The early examples of these models include simplified ad hoc built metabolic pathways, and biomass compositions. The development of genome scale models (GEMs) brought a standard to metabolic network modeling. However, the vast amount of detailed biochemistry in GEMs makes it necessary to develop methods to manage the complexity in them. In this study, we developed lumpGEM, a tool that can systematically identify subnetworks from metabolic networks that can perform certain tasks, such as biosynthesis of a biomass building block and any other target metabolite. By generating alternative subnetworks, lumpGEM also accounts for the redundancy in metabolic networks. We applied lumpGEM on latest E. coli GEM iJO1366 and identified subnetworks/lumped reactions for every biomass building block defined in its biomass formulation. We also compared the results from lumpGEM with existing knowledge in the literature. The lumped reactions generated by lumpGEM can be used to generate consistently reduced metabolic network models.

Constraint-based metabolic modelling of marine microbes and communities

Constraint-based metabolic modelling (CBMM) consists in the use of computational methods and tools to perform genome-scale simulations and predict metabolic features at the whole cellular level. This approach is rapidly expanding in microbiology, as it combines reliable predictive abilities with conceptually and technically simple frameworks. Among the possible outcomes of CBMM, the capability to i) guide a focused planning of metabolic engineering experiments and ii) provide a system-level understanding of (single or community-level) microbial metabolic circuits also represent primary aims in present-day marine microbiology. In this work we briefly introduce the theoretical formulation behind CBMM and then review the most recent and effective case studies of CBMM of marine microbes and communities. Also, the emerging challenges and possibilities in the use of such methodologies in the context of marine microbiology/biotechnology are discussed. As the potential applications of CBMM have a very broad range, the topics presented in this review span over a large plethora of fields such as ecology, biotechnology and evolution.

Lignocellulosic butanol production from Napier grass using semi-simultaneous saccharification fermentation

Napier grass is a potential feedstock for biofuel production because of its strong adaptability and wide availability. Compositional analysis has been done on Napier grass which was collected from a local area of Taiwan. By comparing acid- and alkali-pretreatment, it was found that the alkali-pretreatment process is favorable for Napier grass. An overall glucose yield of 0.82g/g-glucose_total can be obtained with the combination of alkali-pretreatment (2.5wt% NaOH, 8wt% sample loading, 121°C, and a reaction time of 40min) and enzymatic hydrolysis (40FPU/g-substrate). Semi-simultaneous saccharification fermentation (sSSF) was carried out, where enzymatic hydrolysis and ABE fermentation were operated in the same batch. It was found that after 24-h hydrolysis, followed by 96-h fermentation, the butanol and acetone concentrations reached 9.45 and 4.85g/L, respectively. The butanol yield reached 0.22g/g-sugar_{glucose+xylose}. Finally, the efficiency of butanol production from Napier grass was calculated at 31%.

Kinetically accessible yield (KAY) for redirection of metabolism to produce exo-metabolites

The product formation yield (product formed per unit substrate consumed) is often the most important performance indicator in metabolic engineering. Until now, the actual yield cannot be predicted, but it can be bounded by its maximum theoretical value. The maximum theoretical yield is calculated by considering the stoichiometry of the pathways and cofactor regeneration involved. Here we found that in many cases, dynamic stability becomes an issue when excessive pathway flux is drawn to a product. This constraint reduces the yield and renders the maximal theoretical yield too loose to be predictive. We propose a more realistic quantity, defined as the kinetically accessible yield (KAY) to predict the maximum accessible yield for a given flux alteration. KAY is either determined by the point of instability, beyond which steady states become unstable and disappear, or a local maximum before becoming unstable. Thus, KAY is the maximum flux that can be redirected for a given metabolic engineering strategy without losing stability. Strictly speaking, calculation of KAY requires complete kinetic information. With limited or no kinetic information, an Ensemble Modeling strategy can be used to determine a range of likely values for KAY, including an average prediction. We first apply the KAY concept with a toy model to demonstrate the principle of kinetic limitations on yield. We then used a full-scale E. coli model (193 reactions, 153 metabolites) and this approach was successful in E. coli for predicting production of isobutanol: the calculated KAY values are consistent with experimental data for three genotypes previously published.

Mathematical modelling of clostridial acetone-butanol-ethanol fermentation

Clostridial acetone-butanol-ethanol (ABE) fermentation features a remarkable shift in the cellular metabolic activity from acid formation, acidogenesis, to the production of industrial-relevant solvents, solventogensis. In recent decades, mathematical models have been employed to elucidate the complex interlinked regulation and conditions that determine these two distinct metabolic states and govern the transition between them. In this review, we discuss these models with a focus on the mechanisms controlling intra- and extracellular changes between acidogenesis and solventogenesis. In particular, we critically evaluate underlying model assumptions and predictions in the light of current experimental knowledge. Towards this end, we briefly introduce key ideas and assumptions applied in the discussed modelling approaches, but waive a comprehensive mathematical presentation. We distinguish between structural and dynamical models, which will be discussed in their chronological order to illustrate how new biological information facilitates the ‘evolution’ of mathematical models. Mathematical models and their analysis have significantly contributed to our knowledge of ABE fermentation and the underlying regulatory network which spans all levels of biological organization. However, the ties between the different levels of cellular regulation are not well understood. Furthermore, contradictory experimental and theoretical results challenge our current notion of ABE metabolic network structure. Thus, clostridial ABE fermentation still poses theoretical as well as experimental challenges which are best approached in close collaboration between modellers and experimentalists.

CO2 fixation by anaerobic non-photosynthetic mixotrophy for improved carbon conversion

Maximizing the conversion of biogenic carbon feedstocks into chemicals and fuels is essential for fermentation processes as feedstock costs and processing is commonly the greatest operating expense. Unfortunately, for most fermentations, over one-third of sugar carbon is lost to CO₂ due to the decarboxylation of pyruvate to acetyl-CoA and limitations in the reducing power of the bio-feedstock. Here we show that anaerobic, non-photosynthetic mixotrophy, defined as the concurrent utilization of organic (for example, sugars) and inorganic (for example, CO₂) substrates in a single organism, can overcome these constraints to increase product yields and reduce overall CO₂ emissions. As a proof-of-concept, Clostridium ljungdahlii was engineered to produce acetone and achieved a mass yield 138% of the previous theoretical maximum using a high cell density continuous fermentation process. In addition, when enough reductant (that is, H₂) is provided, the fermentation emits no CO₂. Finally, we show that mixotrophy is a general trait among acetogens.

Kinetic Study of Acetone-Butanol-Ethanol Fermentation in Continuous Culture

Acetone-butanol-ethanol (ABE) fermentation by clostridia has shown promise for industrial-scale production of biobutanol. However, the continuous ABE fermentation suffers from low product yield, titer, and productivity. Systems analysis of the continuous ABE fermentation will offer insights into its metabolic pathway as well as into optimal fermentation design and operation. For the ABE fermentation in continuous Clostridium acetobutylicum culture, this paper presents a kinetic model that includes the effects of key metabolic intermediates and enzymes as well as culture pH, product inhibition, and glucose inhibition. The kinetic model is used for elucidating the behavior of the ABE fermentation under the conditions that are most relevant to continuous cultures. To this end, dynamic sensitivity analysis is performed to systematically investigate the effects of culture conditions, reaction kinetics, and enzymes on the dynamics of the ABE production pathway. The analysis provides guidance for future metabolic engineering and fermentation optimization studies.

Constrained Allocation Flux Balance Analysis

New experimental results on bacterial growth inspire a novel top-down approach to study cell metabolism, combining mass balance and proteomic constraints to extend and complement Flux Balance Analysis. We introduce here Constrained Allocation Flux Balance Analysis, CAFBA, in which the biosynthetic costs associated to growth are accounted for in an effective way through a single additional genome-wide constraint. Its roots lie in the experimentally observed pattern of proteome allocation for metabolic functions, allowing to bridge regulation and metabolism in a transparent way under the principle of growth-rate maximization. We provide a simple method to solve CAFBA efficiently and propose an “ensemble averaging” procedure to account for unknown protein costs. Applying this approach to modeling E. coli metabolism, we find that, as the growth rate increases, CAFBA solutions cross over from respiratory, growth-yield maximizing states (preferred at slow growth) to fermentative states with carbon overflow (preferred at fast growth). In addition, CAFBA allows for quantitatively accurate predictions on the rate of acetate excretion and growth yield based on only 3 parameters determined by empirical growth laws.

The intracellular protein levels of exponentially growing bacteria are known to vary strongly with growth conditions, as described by quantitative “growth laws”. This work introduces a computational genome-scale framework (Constrained Allocation Flux Balance Analysis, CAFBA) which incorporates growth laws into canonical Flux Balance Analysis. Upon introducing 3 parameters based on established growth laws for E. coli, CAFBA accurately reproduces empirical results on the growth-rate dependent rate of carbon overflow and growth yield, and generates testable predictions about cellular energetic strategies and protein expression levels. CAFBA therefore provides a simple, quantitative approach to balancing the trade-off between growth and its associated biosynthetic costs at genome-scale, without the burden of tuning many inaccessible parameters.

System-level modeling of acetone-butanol-ethanol fermentation

Acetone-butanol-ethanol (ABE) fermentation is a metabolic process of clostridia that produces bio-based solvents including butanol. It is enabled by an underlying metabolic reaction network and modulated by cellular gene regulation and environmental cues. Mathematical modeling has served as a valuable strategy to facilitate the understanding, characterization and optimization of this process. In this review, we highlight recent advances in system-level, quantitative modeling of ABE fermentation. We begin with an overview of integrative processes underlying the fermentation. Next we survey modeling efforts including early simple models, models with a systematic metabolic description, and those incorporating metabolism through simple gene regulation. Particular focus is given to a recent system-level model that integrates the metabolic reactions, gene regulation and environmental cues. We conclude by discussing the remaining challenges and future directions towards predictive understanding of ABE fermentation.

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.

In silico analysis of Clostridium acetobutylicum ATCC 824 metabolic response to an external electron supply

The biological production of butanol has become an important research field and thanks to genome sequencing and annotation; genome-scale metabolic reconstructions have been developed for several Clostridium species. This work makes use of the iCAC490 model of Clostridium acetobutylicum ATCC 824 to analyze its metabolic capabilities and response to an external electron supply through a constraint-based approach using the Constraint-Based Reconstruction Analysis Toolbox. Several analyses were conducted, which included sensitivity, production envelope, and phenotypic phase planes. The model showed that the use of an external electron supply, which acts as co-reducing agent along with glucose-derived reducing power (electrofermentation), results in an increase in the butanol-specific productivity. However, a proportional increase in the butyrate uptake flux is required. Besides, the uptake of external butyrate leads to the coupling of butanol production and growth, which coincides with results reported in literature. Phenotypic phase planes showed that the reducing capacity becomes more limiting for growth at high butyrate uptake fluxes. An electron uptake flux allows the metabolism to reach the growth optimality line. Although the maximum butanol flux does not coincide with the growth optimality line, a butyrate uptake combined with an electron uptake flux would result in an increased butanol volumetric productivity, being a potential strategy to optimize the production of butanol by C. acetobutylicum ATCC 824.

Cell scale host-pathogen modeling: another branch in the evolution of constraint-based methods

Constraint-based models have become popular methods for systems biology as they enable the integration of complex, disparate datasets in a biologically cohesive framework that also supports the description of biological processes in terms of basic physicochemical constraints and relationships. The scope, scale, and application of genome scale models have grown from single cell bacteria to multi-cellular interaction modeling; host-pathogen modeling represents one of these examples at the current horizon of constraint-based methods. There are now a small number of examples of host-pathogen constraint-based models in the literature, however there has not yet been a definitive description of the methodology required for the functional integration of genome scale models in order to generate simulation capable host-pathogen models. Herein we outline a systematic procedure to produce functional host-pathogen models, highlighting steps which require debugging and iterative revisions in order to successfully build a functional model. The construction of such models will enable the exploration of host-pathogen interactions by leveraging the growing wealth of omic data in order to better understand mechanism of infection and identify novel therapeutic strategies.

Continuous xylose fermentation by Clostridium acetobutylicum--Assessment of solventogenic kinetics

This work deals with the specific butanol production rate of Clostridium acetobutylicum using xylose--a relevant fraction of lignocellulosic feedstock for biofuel production--as carbon source. The tests were carried out in a CSTR equipped with a microfiltration unit. The dilution rate (D) ranged between 0.02 and 0.22 h(-1) and the ratio R between the permeate stream rate and the stream fed to the reactor ranged between 14% and 88%. The biomass present in the broth was identified as a heterogeneous cell population consisting of: acidogenic cells, solventogenic cells and spores. The results were processed to assess the concentration of acidogenic cells, solventogenic cells and spores. The specific butanol production rate was also assessed. The max butanol productivity was 1.3 g L(-1) h(-1) at D = 0.17 h(-1) and R = 30%. A comparison between the results reported in a previous work carried out with lactose was made.

Synthetic methylotrophy: engineering the production of biofuels and chemicals based on the biology of aerobic methanol utilization

Synthetic methylotrophy is the development of non-native methylotrophs that can utilize methane and methanol as sole carbon and energy sources or as co-substrates with carbohydrates to produce metabolites as biofuels and chemicals. The availability of methane (from natural gas) and its oxidation product, methanol, has been increasing, while prices have been decreasing, thus rendering them as attractive fermentation substrates. As they are more reduced than most carbohydrates, methane and methanol, as co-substrates, can enhance the yields of biologically produced metabolites. Here we discuss synthetic biology and metabolic engineering strategies based on the native biology of aerobic methylotrophs for developing synthetic strains grown on methanol, with Escherichia coli as the prototype.

Continuous lactose fermentation by Clostridium acetobutylicum--assessment of solventogenic kinetics

This work reports the results of a series of tests on the specific butanol production rate by Clostridium acetobutylicum continuous cultures. The tests were carried out using lactose as carbon source to mimic cheese-whey. A continuous stirred tank reactor equipped with a microfiltration unit was used. The dilution rate (D) ranged between 0.02 and 0.15h(-1) and the ratio R of the permeate stream rate to the stream fed to the reactor ranged between 14% and 95%. For each set of D and R values, the continuous cultures were characterized in terms of concentration of cells, acids and solvents. Results were processed to assess the concentration of acidogenic cells, solventogenic cells, spores and the specific butanol production rate. The max butanol productivity was 0.5gL(-1)h(-1) at D=0.1h(-1) and R=95%. The butanol productivity referred to solventogenic cells was expressed as a function of concentration of lactose, acids and butanol.