Kinetic modeling and sensitivity analysis of acetone–butanol–ethanol production

Mathematical modeling of ABE fermentation on glucose substrate with Zn supplementation for enhanced butanol production

Supplementation or limitation of some micronutrients during acetone-butanol-ethanol (ABE) fermentation has led to improvement in butanol yield and productivity. A mechanistic model of ABE fermentation offers insights in understanding these complex interactions and improving productivity through optimal culture conditions. This study proposes a mechanistic kinetic model of ABE fermentation by two Clostridium Acetobutylicum strains, L7 and ATCC 824 using glucose as sole carbon source without zinc and with various zinc doses. The model incorporates enzyme regulation by zinc on several glycolytic, acidogenesis and solventogenesis enzymes. The model was fitted and validated to experimental data collected from the published literature. The simulated results were in compliance with the experimental data, most importantly indicating higher glucose consumption and butanol productivity when supplemented with zinc compared to the control culture. The average squared correlation factor (R2) between the experimental and the simulated results, without and with zinc, were 0.99 and 0.96 for glucose, and 0.89 and 0.95 for butanol, respectively. A sensitivity analysis performed on the fitted and validated model indicated that the glucose consumption and growth parameters most influenced the model outputs. The developed model can be used as a template for modeling ABE fermentation under different combinations of micronutrients that may offer improved butanol yield and productivity.

Exploring the Influence of pH on the Dynamics of Acetone-Butanol-Ethanol Fermentation

Clostridium acetobutylicum is an anaerobic bacterium that is extensively studied for its ability to produce butanol. Over the past two decades, various genetic and metabolic engineering approaches have been used to investigate the physiology and regulation system of the biphasic metabolic pathway in this organism. However, there has been a relatively limited amount of research focused on the fermentation dynamics of C. acetobutylicum. In this study, we developed a pH-based phenomenological model to predict the fermentative production of butanol from glucose using C. acetobutylicum in a batch system. The model describes the relationship between the dynamics of growth and the production of desired metabolites and the extracellular pH of the media. Our model was found to be successful in predicting the fermentation dynamics of C. acetobutylicum, and the simulations were validated using experimental fermentation data. Furthermore, the proposed model has the potential to be extended to represent the dynamics of butanol production in other fermentation systems, such as fed-batch or continuous fermentation using single and multi-sugars.

Predictive modeling and sensitivity analysis to estimate the experimental data of inulinase fermentation by Aspergillus niger grown on sugar beet molasses-based medium optimized using Plackett-Burman Design

The present work aimed to model Aspergillus niger inulinase fermentation performed in the medium using sigmoidal functions, validate the selected models using an independent set of the experimental values, and perform a sensitivity analysis of the selected models. Based on the results, the selected models were Stannard and Fitzhugh models for substrate consumption (R²  = 0.9976 and 0.9974, respectively), Huang model for inulinase production (R²  = 0.9967), Weibull model for invertase-type production (R²  = 0.9963), and modified logistic model for invertase-type activity/inulinase activity ratio (R²  = 0.9292) with high R² values (>0.90). Kinetics predicted by particularly selected models mentioned above fit well with the experimental kinetic results. Besides, validation of the selected models with an independent set of the experimental data indicated that they gave satisfying results with high R² values for consumption and production (R²  > 0.90). Sensitivity analysis of the selected models showed that the yielded R² values (R²  ≥ 0.9775) were in good agreement with those obtained from the selected models. Consequently, A. niger inulinase fermentation was successfully modeled and the selected models were successfully validated with an independent set of the observed data. Besides, the sensitivity analysis also verified the reliability of the selected models. Those models can serve as universal equations to describe the A. niger inulinase fermentation.

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.

Digital models in biotechnology: Towards multi-scale integration and implementation

Industrial biotechnology encompasses a large area of multi-scale and multi-disciplinary research activities. With the recent megatrend of digitalization sweeping across all industries, there is an increased focus in the biotechnology industry on developing, integrating and applying digital models to improve all aspects of industrial biotechnology. Given the rapid development of this field, we systematically classify the state-of-art modelling concepts applied at different scales in industrial biotechnology and critically discuss their current usage, advantages and limitations. Further, we critically analyzed current strategies to couple cell models with computational fluid dynamics to study the performance of industrial microorganisms in large-scale bioprocesses, which is of crucial importance for the bio-based production industries. One of the most challenging aspects in this context is gathering intracellular data under industrially relevant conditions. Towards comprehensive models, we discuss how different scale-down concepts combined with appropriate analytical tools can capture intracellular states of single cells. We finally illustrated how the efforts could be used to develop digitals models suitable for both cell factory design and process optimization at industrial scales in the future.

Kinetic model of Clostridium beijerinckii's Acetone-Butanol-Ethanol fermentation considering metabolically diverse cell types

Clostridium beijerinckii population branches into metabolically diverse cell types in batch cultures. Here, we present a new kinetic model of C. beijerinckii's Acetone-Butanol-Ethanol fermentation that considers three cell types: producers of acids (acidogenic), consumer of acids and producers of solvents (solventogenic), and spores cells. The model accurately recapitulates batch culture data. Also, the model estimates cell type-specific kinetic parameters, which can be helpful to improve the operation of the ABE fermentation and give a framework to study acidogenic and solventogenic metabolic pathways. To exemplify the latter, we used a constraint-based model to study how the ABE pathways are used among acidogenic and solventogenic cell types. We found that among both cell types, glycolytic production of ATP and consumption of NAD⁺ varies widely during the fermentation, with their maximum production/consumption rates happening when acidogenic and solventogenic growth rates were at their highest. However, acidogenic cells use the ABE pathway to contribute with an extra 12.5% of the total production of ATP, whereas solventogenic cell types use the ABE pathway to supply more than 75% of the demand for NAD⁺, alternating between the production of lactate and butyrate, being both coupled to the production of NAD⁺.

WinBEST-KIT: Biochemical Reaction Simulator for Analyzing Multi-Layered Metabolic Pathways

We previously developed the biochemical reaction simulator WinBEST-KIT. In recent years, research interest has shifted from analysis of individual biochemical reactions to analysis of metabolic pathways as systems. These large-scale and complicated metabolic pathways can be considered as characteristic multi-layered structures, which, for convenience, are separated from whole biological systems according to their specific roles. These pathways include reactants having the same name but with unique stoichiometric coefficients arranged across many different places and connected between arbitrary layers. Accordingly, in this study, we have developed a new version of WinBEST-KIT that allows users (1) to utilize shortcut symbols that can be arranged with multiple reactants having the same name but with unique stoichiometric coefficients, thereby providing a layout that is similar to metabolic pathways depicted in biochemical textbooks; (2) to create layers that divide large-scale and complicated metabolic pathways according to their specific roles; (3) to connect the layers by using shortcut symbols; and (4) to analyze the interactions between these layers. These new and existing features allow users to create and analyze such multi-layered metabolic pathways efficiently. Furthermore, WinBEST-KIT supports SBML, making it possible for users to utilize these new and existing features to create and publish SBML models.

Butanol production from lignocellulosic sugars by Clostridium beijerinckii in microbioreactors

BACKGROUND

Butanol (n-butanol) has been gaining attention as a renewable energy carrier and an alternative biofuel with superior properties to the most widely used ethanol. We performed 48 anaerobic fermentations simultaneously with glucose and xylose as representative lignocellulosic sugars by Clostridium beijerinckii NCIMB 8052 in BioLector® microbioreactors to understand the effect of different sugar mixtures on fermentation and to demonstrate the applicability of the micro-cultivation system for high-throughput anaerobic cultivation studies. We then compared the results to those of similar cultures in serum flasks to provide insight into different setups and measurement methods.

RESULTS

ANOVA results showed that the glucose-to-xylose ratio affects both growth and production due to Carbon Catabolite Repression. The study demonstrated successful use of BioLector® system for the first time for screening several media and sugar compositions under anaerobic conditions by using online monitoring of cell mass and pH in real-time and at unprecedented time-resolution. Fermentation products possibly interfered with dissolved oxygen (DO) measurements, which require a careful interpretation of DO monitoring results.

CONCLUSIONS

The statistical approach to evaluate the microbioreactor setup, and information obtained in this study will support further research in bioreactor and bioprocess design, which are very important aspects of industrial fermentations of lignocellulosic biomass.

WinBEST-KIT: Biochemical reaction simulator that can define and customize algebraic equations and events as GUI components

We previously developed Windows-based Biochemical Engineering System analyzing Tool-KIT (WinBEST-KIT), a biochemical reaction simulator for analyzing large-scale and complicated biochemical reaction networks. One particularly notable feature is the ability for users to define original mathematical equations for representing unknown kinetic mechanisms and customize them as GUI components for representing reaction steps. Many simulators support System Biology Markup Language SBML; however, since the definition of the algebraic equations (AssignmentRule) and the events are made through an interface that is distinct from the definition of the reaction steps, there are tough works to define them. Accordingly, we have developed a new version of WinBEST-KIT that allows users to define the algebraic equations and the events through the same interface as those used in the definition of the reaction steps and customize them as GUI components appearing in the symbol selection area. The customized algebraic equations and events can thus be visually arranged at any time and any place. It also allows users to easily understand the roles of the algebraic equations and the events. We have also implemented other useful features, including importing/exporting of SBML format files, exporting to MATLAB, and merging the existing models into the model currently being created. The current version of WinBEST-KIT is freely available at http://winbest-kit.org/.

Dynamic Flux Balance Analysis to Evaluate the Strain Production Performance on Shikimic Acid Production in Escherichia coli

Flux balance analysis (FBA) is used to improve the microbial production of useful compounds. However, a large gap often exists between the FBA solution and the experimental yield, because of growth and byproducts. FBA has been extended to dynamic FBA (dFBA), which is applicable to time-varying processes, such as batch or fed-batch cultures, and has significantly contributed to metabolic and cultural engineering applications. On the other hand, the performance of the experimental strains has not been fully evaluated. In this study, we applied dFBA to the production of shikimic acid from glucose in Escherichia coli, to evaluate the production performance of the strain as a case study. The experimental data of glucose consumption and cell growth were used as FBA constraints. Bi-level FBA optimization with maximized growth and shikimic acid production were the objective functions. Results suggest that the shikimic acid concentration in the high-shikimic-acid-producing strain constructed in the experiment reached up to 84% of the maximum value by simulation. Thus, this method can be used to evaluate the performance of strains and estimate the milestones of strain improvement.

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.

Enhanced butanol production by optimization of medium parameters using Clostridium acetobutylicum YM1

A new isolate of the solvent-producing Clostridium acetobutylicum YM1 was used to produce butanol in batch culture fermentation. The effects of glucose concentration, butyric acid addition and C/N ratio were studied conventionally (one-factor-at-a-time). Moreover, the interactions between glucose concentration, butyric acid addition and C/N ratio were further investigated to optimize butanol production using response surface methodology (RSM). A central composite design was applied, and a polynomial regression model with a quadratic term was used to analyze the experimental data using analysis of variance (ANOVA). ANOVA revealed that the model was highly significant (p < 0.0001) and the effects of the glucose and butyric acid concentrations on butanol production were significant. The model validation experiment showed 13.82 g/L butanol was produced under optimum conditions. Scale up fermentation in optimized medium resulted in 17 g/L of butanol and 21.71 g/L of ABE. The experimental data of scale up in 5 L bioreactor and flask scale were fitted to kinetic mathematical models published in the literature to estimate the kinetic parameters of the fermentation. The models used gave the best fit for butanol production, biomass and glucose consumption for both flask scale and bioreactor scale up.

An Integrated Approach of Proteomics and Computational Genetic Modification Effectiveness Analysis to Uncover the Mechanisms of Flood Tolerance in Soybeans

Flooding negatively affects the growth of soybeans. Recently, omic approaches have been used to study abiotic stress responses in plants. To explore flood-tolerant genes in soybeans, an integrated approach of proteomics and computational genetic modification effectiveness analysis was applied to the soybean (Glycine max L. (Merrill)). Flood-tolerant mutant and abscisic acid (ABA)-treated soybean plants were used as the flood-tolerant materials. Among the primary metabolism, glycolysis, fermentation, and tricarboxylic acid cycle were markedly affected under flooding. Fifteen proteins, which were related to the affected processes, displayed similar protein profiles in the mutant and ABA-treated soybean plants. Protein levels of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), aconitase 1, and 2-oxoglutarate dehydrogenase were higher in flood-tolerant materials than in wild-type soybean plants under flood conditions. These three proteins were positioned in each of the three enzyme groups revealed by our computational genetic modification effectiveness analysis, and the three proteins configured a candidate set of genes to promote flood tolerance. Additionally, transcript levels of GAPDH were similar in flood-tolerant materials and in unstressed plants. These results suggest that proteins related to energy metabolism might play an essential role to confer flood tolerance in soybeans.

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.

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.

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.

Steady-State-Preserving Simulation of Genetic Regulatory Systems

A novel family of exponential Runge-Kutta (expRK) methods are designed incorporating the stable steady-state structure of genetic regulatory systems. A natural and convenient approach to constructing new expRK methods on the base of traditional RK methods is provided. In the numerical integration of the one-gene, two-gene, and p53-mdm2 regulatory systems, the new expRK methods are shown to be more accurate than their prototype RK methods. Moreover, for nonstiff genetic regulatory systems, the expRK methods are more efficient than some traditional exponential RK integrators in the scientific literature.

Butanol production from lignocellulose by simultaneous fermentation, saccharification, and pervaporation or vacuum evaporation

Techno-economic study of acetone, butanol and ethanol (ABE) fermentation from lignocellulose was performed. Simultaneous saccharification, fermentation and vacuum evaporation (SFS-V) or pervaporation (SFS-P) were proposed. A kinetic model of metabolic pathways for ABE fermentation with the effect of phenolics and furans in the growth was proposed based on published laboratory results. The processes were optimized in Matlab®. The end ABE purification was carried out by heat-integrated distillation. The objective function of the minimization was the total annualized cost (TAC). Fuel consumption of SFS-P using poly[1-(trimethylsilyl)-1-propyne] membrane was between 13.8 and 19.6% lower than SFS-V. Recovery of furans and phenolics for the hybrid reactors was difficult for its high boiling point. TAC of SFS-P was increased 1.9 times with supplementation of phenolics and furans to 3g/l each one for its high toxicity. Therefore, an additional detoxification method or an efficient pretreatment process will be necessary.

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.

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.

Modelling the role of CtfA/B in reverse shift continuous culture experiments of Clostridium acetobutylicum

In continuous phosphate-limited conditions, under pH control from high pH (pH ≳ 5.2) to low pH (pH ≲ 5.2), the metabolism of the Gram-positive bacterium Clostridium acetobutylicum,switches from acid to solvent production. Three main enzymes are responsible for the shift, acetoacetate decarboxylase (Adc), alcohol dehydrogenase (AdhE1/2) and a CoA-transferase (CtfA/B), which are produced in increased quantities during solventogenesis. A two-population model, Millat et al. (2013) and fitted to such 'forward'-shift data, can explain this, as well as observed changes in optical density immediately following the shift: an acidogenic subpopulation is washed out and a solventogenic subpopulation grows in its place, each with distinct physiologies and proteomes. We fit this model to a 'reverse'-shift experiment, where the pH is increased from solventogenic to acidogenic conditions. We find corresponding changes in reaction rates, with AdhE1 and Adc production falling, as in the 'forward' experiments; however, for CtfA/B, the best fit surprisingly arises from the same level of production in both conditions. We propose experiments that would test whether this is a model artefact or accurately reflects cultures shifted in this reverse direction, and, if true, may suggest that over-expressing CtfA/B in both solventogenic and acidogenic conditions could improve the efficiency of fermentation.

High acetone-butanol-ethanol production in pH-stat co-feeding of acetate and glucose

We previously reported the metabolic analysis of butanol and acetone production from exogenous acetate by (13)C tracer experiments (Gao et al., RSC Adv., 5, 8486-8495, 2015). To clarify the influence of acetate on acetone-butanol-ethanol (ABE) production, we first performed an enzyme assay in Clostridium saccharoperbutylacetonicum N1-4. Acetate addition was found to drastically increase the activities of key enzymes involved in the acetate uptake (phosphate acetyltransferase and CoA transferase), acetone formation (acetoacetate decarboxylase), and butanol formation (butanol dehydrogenase) pathways. Subsequently, supplementation of acetate during acidogenesis and early solventogenesis resulted in a significant increase in ABE production. To establish an efficient ABE production system using acetate as a co-substrate, several shot strategies were investigated in batch culture. Batch cultures with two substrate shots without pH control produced 14.20 g/L butanol and 23.27 g/L ABE with a maximum specific butanol production rate of 0.26 g/(g h). Furthermore, pH-controlled (at pH 5.5) batch cultures with two substrate shots resulted in not only improved acetate consumption but also a further increase in ABE production. Finally, we obtained 15.13 g/L butanol and 24.37 g/L ABE at the high specific butanol production rate of 0.34 g/(g h) using pH-stat co-feeding method. Thus, in this study, we established a high ABE production system using glucose and acetate as co-substrates in a pH-stat co-feeding system with C. saccharoperbutylacetonicum N1-4.

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.

Kinetic modeling of cell metabolism for microbial production

Kinetic models of cellular metabolism are important tools for the rational design of metabolic engineering strategies and to explain properties of complex biological systems. The recent developments in high-throughput experimental data are leading to new computational approaches for building kinetic models of metabolism. Herein, we briefly survey the available databases, standards and software tools that can be applied for kinetic models of metabolism. In addition, we give an overview about recently developed ordinary differential equations (ODE)-based kinetic models of metabolism and some of the main applications of such models are illustrated in guiding metabolic engineering design. Finally, we review the kinetic modeling approaches of large-scale networks that are emerging, discussing their main advantages, challenges and limitations.

Integrated, systems metabolic picture of acetone-butanol-ethanol fermentation by Clostridium acetobutylicum

Microbial metabolism involves complex, system-level processes implemented via the orchestration of metabolic reactions, gene regulation, and environmental cues. One canonical example of such processes is acetone-butanol-ethanol (ABE) fermentation by Clostridium acetobutylicum, during which cells convert carbon sources to organic acids that are later reassimilated to produce solvents as a strategy for cellular survival. The complexity and systems nature of the process have been largely underappreciated, rendering challenges in understanding and optimizing solvent production. Here, we present a system-level computational framework for ABE fermentation that combines metabolic reactions, gene regulation, and environmental cues. We developed the framework by decomposing the entire system into three modules, building each module separately, and then assembling them back into an integrated system. During the model construction, a bottom-up approach was used to link molecular events at the single-cell level into the events at the population level. The integrated model was able to successfully reproduce ABE fermentations of the WT C. acetobutylicum (ATCC 824), as well as its mutants, using data obtained from our own experiments and from literature. Furthermore, the model confers successful predictions of the fermentations with various network perturbations across metabolic, genetic, and environmental aspects. From foundation to applications, the framework advances our understanding of complex clostridial metabolism and physiology and also facilitates the development of systems engineering strategies for the production of advanced biofuels.

Engineering Escherichia coli Cell Factories for n-Butanol Production

The production of n-butanol, as a widely applied solvent and potential fuel, is attracting much attention. The fermentative production of butanol coupled with the production of acetone and ethanol by Clostridium (ABE fermentation) was once one of the oldest biotechnological processes, ranking second in scale behind ethanol fermentation. However, there remain problems with butanol production by Clostridium, especially the difficulty in genetically manipulating clostridial strains. In recent years, many efforts have been made to produce butanol using non-native strains. Until now, the most advanced effort was the engineering of the user-friendly and widely studied Escherichia coli for butanol production. This paper reviews the current progress and problems relating to butanol production by engineered E. coli in terms of prediction using mathematical models, pathway construction, novel enzyme replacement, butanol toxicity, and tolerance engineering strategies.

Computational strategies for a system-level understanding of metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided.

Coenzyme A-transferase-independent butyrate re-assimilation in Clostridium acetobutylicum-evidence from a mathematical model

The hetero-dimeric CoA-transferase CtfA/B is believed to be crucial for the metabolic transition from acidogenesis to solventogenesis in Clostridium acetobutylicum as part of the industrial-relevant acetone-butanol-ethanol (ABE) fermentation. Here, the enzyme is assumed to mediate re-assimilation of acetate and butyrate during a pH-induced metabolic shift and to faciliate the first step of acetone formation from acetoacetyl-CoA. However, recent investigations using phosphate-limited continuous cultures have questioned this common dogma. To address the emerging experimental discrepancies, we investigated the mutant strain Cac-ctfA398s::CT using chemostat cultures. As a consequence of this mutation, the cells are unable to express functional ctfA and are thus lacking CoA-transferase activity. A mathematical model of the pH-induced metabolic shift, which was recently developed for the wild type, is used to analyse the observed behaviour of the mutant strain with a focus on re-assimilation activities for the two produced acids. Our theoretical analysis reveals that the ctfA mutant still re-assimilates butyrate, but not acetate. Based upon this finding, we conclude that C. acetobutylicum possesses a CoA-tranferase-independent butyrate uptake mechanism that is activated by decreasing pH levels. Furthermore, we observe that butanol formation is not inhibited under our experimental conditions, as suggested by previous batch culture experiments. In concordance with recent batch experiments, acetone formation is abolished in chemostat cultures using the ctfa mutant.