A dynamic, genome-scale flux model of Lactococcus lactis to increase specific recombinant protein expression

Quantification of proteomic and metabolic burdens predicts growth retardation and overflow metabolism in recombinant Escherichia coli

Escherichia coli has been the host organism most frequently investigated for efficient recombinant protein production. However, the production of a foreign protein in recombinant E. coli often leads to growth deterioration and elevated secretion of acetic acid. Such observed phenomena have been widely linked with cell stress responses and metabolic burdens originated particularly from the increased energy demand. In this study, flux balance analysis and dynamic flux balance analysis were applied to investigate the observed growth physiology of recombinant E. coli, incorporating the proteome allocation theory and an adjustable maintenance energy level (ATPM) to capture the proteomic and energetic burdens introduced by recombinant protein synthesis. Model predictions of biomass growth, substrate consumption, acetate excretion, and protein production with two different strains were in good agreement with the experimental data, indicating that the constraint on the available proteomic resource and the change in ATPM might be important contributors governing the growth physiology of recombinant strains. The modeling framework developed in this work, currently with several limitations to overcome, offers a starting point for the development of a practical, model-based tool to guide metabolic engineering decisions for boosting recombinant protein production.

Clostridium butyricum population balance model: Predicting dynamic metabolic flux distributions using an objective function related to extracellular glycerol content

BACKGROUND

Extensive experimentation has been conducted to increment 1,3-propanediol (PDO) production using Clostridium butyricum cultures in glycerol, but computational predictions are limited. Previously, we reconstructed the genome-scale metabolic (GSM) model iCbu641, the first such model of a PDO-producing Clostridium strain, which was validated at steady state using flux balance analysis (FBA). However, the prediction ability of FBA is limited for batch and fed-batch cultures, which are the most often employed industrial processes.

RESULTS

We used the iCbu641 GSM model to develop a dynamic flux balance analysis (DFBA) approach to predict the PDO production of the Colombian strain Clostridium sp IBUN 158B. First, we compared the predictions of the dynamic optimization approach (DOA), static optimization approach (SOA), and direct approach (DA). We found no differences between approaches, but the DOA simulation duration was nearly 5000 times that of the SOA and DA simulations. Experimental results at glycerol limitation and glycerol excess allowed for validating dynamic predictions of growth, glycerol consumption, and PDO formation. These results indicated a 4.4% error in PDO prediction and therefore validated the previously proposed objective functions. We performed two global sensitivity analyses, finding that the kinetic input parameters of glycerol uptake flux had the most significant effect on PDO predictions. The other input parameters evaluated during global sensitivity analysis were biomass composition (precursors and macromolecules), death constants, and the kinetic parameters of acetic acid secretion flux. These last input parameters, all obtained from other Clostridium butyricum cultures, were used to develop a population balance model (PBM). Finally, we simulated fed-batch cultures, predicting a final PDO production near to 66 g/L, almost three times the PDO predicted in the best batch culture.

CONCLUSIONS

We developed and validated a dynamic approach to predict PDO production using the iCbu641 GSM model and the previously proposed objective functions. This validated approach was used to propose a population model and then an increment in predictions of PDO production through fed-batch cultures. Therefore, this dynamic model could predict different scenarios, including its integration into downstream processes to predict technical-economic feasibilities and reducing the time and costs associated with experimentation.

Constraint-based modeling in microbial food biotechnology

Genome-scale metabolic network reconstruction offers a means to leverage the value of the exponentially growing genomics data and integrate it with other biological knowledge in a structured format. Constraint-based modeling (CBM) enables both the qualitative and quantitative analyses of the reconstructed networks. The rapid advancements in these areas can benefit both the industrial production of microbial food cultures and their application in food processing. CBM provides several avenues for improving our mechanistic understanding of physiology and genotype–phenotype relationships. This is essential for the rational improvement of industrial strains, which can further be facilitated through various model-guided strain design approaches. CBM of microbial communities offers a valuable tool for the rational design of defined food cultures, where it can catalyze hypothesis generation and provide unintuitive rationales for the development of enhanced community phenotypes and, consequently, novel or improved food products. In the industrial-scale production of microorganisms for food cultures, CBM may enable a knowledge-driven bioprocess optimization by rationally identifying strategies for growth and stability improvement. Through these applications, we believe that CBM can become a powerful tool for guiding the areas of strain development, culture development and process optimization in the production of food cultures. Nevertheless, in order to make the correct choice of the modeling framework for a particular application and to interpret model predictions in a biologically meaningful manner, one should be aware of the current limitations of CBM.

Genome-scale modeling and transcriptome analysis of Leuconostoc mesenteroides unravel the redox governed metabolic states in obligate heterofermentative lactic acid bacteria

Obligate heterofermentative lactic acid bacteria (LAB) are well-known for their beneficial health effects in humans. To delineate the incompletely characterized metabolism that currently limits their exploitation, at systems-level, we developed a genome-scale metabolic model of the representative obligate heterofermenting LAB, Leuconostoc mesenteroides (iLME620). Constraint-based flux analysis was then used to simulate several qualitative and quantitative phenotypes of L. mesenteroides, thereby evaluating the model validity. With established predictive capabilities, we subsequently employed iLME620 to elucidate unique metabolic characteristics of L. mesenteroides, such as the limited ability to utilize amino acids as energy source, and to substantiate the role of malolactic fermentation (MLF) in the reduction of pH-homeostatic burden on F0F1-ATPase. We also reported new hypothesis on the MLF mechanism that could be explained via a substrate channelling-like phenomenon mainly influenced by intracellular redox state rather than the intermediary reactions. Model simulations further revealed possible proton-symporter dependent activity of the energy efficient glucose-phosphotransferase system in obligate heterofermentative LAB. Moreover, integrated transcriptomic analysis allowed us to hypothesize transcriptional regulatory bias affecting the intracellular redox state. The insights gained here about the low ATP-yielding metabolism of L. mesenteroides, dominantly controlled by the cellular redox state, could potentially aid strain design for probiotic and cell factory applications.

Protein Secretion in Gram-Positive Bacteria: From Multiple Pathways to Biotechnology

A number of Gram-positive bacteria are important players in industry as producers of a diverse array of economically interesting metabolites and proteins. As discussed in this overview, several Gram-positive bacteria are valuable hosts for the production of heterologous proteins. In contrast to Gram-negative bacteria, proteins secreted by Gram-positive bacteria are released into the culture medium where conditions for correct folding are more appropriate, thus facilitating the isolation and purification of active proteins. Although seven different protein secretion pathways have been identified in Gram-positive bacteria, the majority of heterologous proteins are produced via the general secretion or Sec pathway. Not all proteins are equally well secreted, because heterologous protein production often faces bottlenecks including hampered secretion, susceptibility to proteases, secretion stress, and metabolic burden. These bottlenecks are associated with reduced yields leading to non-marketable products. In this chapter, besides a general overview of the different protein secretion pathways, possible hurdles that may hinder efficient protein secretion are described and attempts to improve yield are discussed including modification of components of the Sec pathway. Attention is also paid to omics-based approaches that may offer a more rational approach to optimize production of heterologous proteins.

Genome-Scale Reconstruction of the Metabolic Network in Oenococcus oeni to Assess Wine Malolactic Fermentation

Oenococcus oeni is the main responsible agent for malolactic fermentation in wine, an unpredictable and erratic process in winemaking. To address this, we have constructed and exhaustively curated the first genome-scale metabolic model of Oenococcus oeni, comprising 660 reactions, 536 metabolites and 454 genes. In silico experiments revealed that nutritional requirements are predicted with an accuracy of 93%, while 14 amino acids were found to be essential for the growth of this bacterial species. When the model was applied to determine the non-growth associated maintenance, results showed that O. oeni grown at 12% ethanol concentration spent 30 times more ATP to stay alive than in the absence of ethanol. Most of this ATP is employed for extruding protons outside of the cell. A positive relationship was also found between specific consumption rates of fructose, amino acids, oxygen, and malic acid and the specific production rates of erythritol, lactate, and acetate, according to the ethanol content of the medium. The metabolic model reconstructed here represents a unique tool to predict the successful completion of wine malolactic fermentation carried out either by different strains of Oenococcus oeni, as well as at any particular physico-chemical composition of wine. It will also allow the development of consortium metabolic models that could be applied to winemaking to simulate and understand the interactions between O. oeni and other microorganisms that share this ecological niche.

Genomics of lactic acid bacteria: Current status and potential applications

Lactic acid bacteria (LAB) are widely used for the production of a variety of foods and feed raw materials where they contribute to flavor and texture of the fermented products. In addition, specific LAB strains are considered as probiotic due to their health-promoting effects in consumers. Recently, the genome sequencing of LAB is booming and the increased amount of published genomics data brings unprecedented opportunity for us to reveal the important traits of LAB. This review describes the recent progress on LAB genomics and special emphasis is placed on understanding the industry-related physiological features based on genomics analysis. Moreover, strategies to engineer metabolic capacity and stress tolerance of LAB with improved industrial performance are also discussed.

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.

Genome-scale modelling of microbial metabolism with temporal and spatial resolution

Most natural microbial systems have evolved to function in environments with temporal and spatial variations. A major limitation to understanding such complex systems is the lack of mathematical modelling frameworks that connect the genomes of individual species and temporal and spatial variations in the environment to system behaviour. The goal of this review is to introduce the emerging field of spatiotemporal metabolic modelling based on genome-scale reconstructions of microbial metabolism. The extension of flux balance analysis (FBA) to account for both temporal and spatial variations in the environment is termed spatiotemporal FBA (SFBA). Following a brief overview of FBA and its established dynamic extension, the SFBA problem is introduced and recent progress is described. Three case studies are reviewed to illustrate the current state-of-the-art and possible future research directions are outlined. The author posits that SFBA is the next frontier for microbial metabolic modelling and a rapid increase in methods development and system applications is anticipated.

Optimizing eukaryotic cell hosts for protein production through systems biotechnology and genome-scale modeling

Eukaryotic cell lines, including Chinese hamster ovary cells, yeast, and insect cells, are invaluable hosts for the production of many recombinant proteins. With the advent of genomic resources, one can now leverage genome-scale computational modeling of cellular pathways to rationally engineer eukaryotic host cells. Genome-scale models of metabolism include all known biochemical reactions occurring in a specific cell. By describing these mathematically and using tools such as flux balance analysis, the models can simulate cell physiology and provide targets for cell engineering that could lead to enhanced cell viability, titer, and productivity. Here we review examples in which metabolic models in eukaryotic cell cultures have been used to rationally select targets for genetic modification, improve cellular metabolic capabilities, design media supplementation, and interpret high-throughput omics data. As more comprehensive models of metabolism and other cellular processes are developed for eukaryotic cell culture, these will enable further exciting developments in cell line engineering, thus accelerating recombinant protein production and biotechnology in the years to come.

Monte-Carlo modeling of the central carbon metabolism of Lactococcus lactis: insights into metabolic regulation

Metabolic pathways are complex dynamic systems whose response to perturbations and environmental challenges are governed by multiple interdependencies between enzyme properties, reactions rates, and substrate levels. Understanding the dynamics arising from such a network can be greatly enhanced by the construction of a computational model that embodies the properties of the respective system. Such models aim to incorporate mechanistic details of cellular interactions to mimic the temporal behavior of the biochemical reaction system and usually require substantial knowledge of kinetic parameters to allow meaningful conclusions. Several approaches have been suggested to overcome the severe data requirements of kinetic modeling, including the use of approximative kinetics and Monte-Carlo sampling of reaction parameters. In this work, we employ a probabilistic approach to study the response of a complex metabolic system, the central metabolism of the lactic acid bacterium Lactococcus lactis, subject to perturbations and brief periods of starvation. Supplementing existing methodologies, we show that it is possible to acquire a detailed understanding of the control properties of a corresponding metabolic pathway model that is directly based on experimental observations. In particular, we delineate the role of enzymatic regulation to maintain metabolic stability and metabolic recovery after periods of starvation. It is shown that the feedforward activation of the pyruvate kinase by fructose-1,6-bisphosphate qualitatively alters the bifurcation structure of the corresponding pathway model, indicating a crucial role of enzymatic regulation to prevent metabolic collapse for low external concentrations of glucose. We argue that similar probabilistic methodologies will help our understanding of dynamic properties of small-, medium- and large-scale metabolic networks models.

Model based engineering of Pichia pastoris central metabolism enhances recombinant protein production

The production of recombinant proteins is frequently enhanced at the levels of transcription, codon usage, protein folding and secretion. Overproduction of heterologous proteins, however, also directly affects the primary metabolism of the producing cells. By incorporation of the production of a heterologous protein into a genome scale metabolic model of the yeast Pichia pastoris, the effects of overproduction were simulated and gene targets for deletion or overexpression for enhanced productivity were predicted. Overexpression targets were localized in the pentose phosphate pathway and the TCA cycle, while knockout targets were found in several branch points of glycolysis. Five out of 9 tested targets led to an enhanced production of cytosolic human superoxide dismutase (hSOD). Expression of bacterial β-glucuronidase could be enhanced as well by most of the same genetic modifications. Beneficial mutations were mainly related to reduction of the NADP/H pool and the deletion of fermentative pathways. Overexpression of the hSOD gene itself had a strong impact on intracellular fluxes, most of which changed in the same direction as predicted by the model. In vivo fluxes changed in the same direction as predicted to improve hSOD production. Genome scale metabolic modeling is shown to predict overexpression and deletion mutants which enhance recombinant protein production with high accuracy.

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Recombinant protein production in P. pastoris affects the central metabolism.

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A genome scale metabolic model can predict these metabolic flux changes.

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Mutations in central metabolic genes enhanced recombinant protein yield up to 40%.

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These beneficial mutations were predicted by the metabolic model with high accuracy.

Kinetic models in industrial biotechnology - Improving cell factory performance

An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed.

Genome-scale metabolic network reconstruction of Saccharopolyspora spinosa for spinosad production improvement

BACKGROUND

Spinosad is a macrolide antibiotic produced by Saccharopolyspora spinosa with aerobic fermentation. However, the wild strain has a low productivity. In this article, a computational guided engineering approach was adopted in order to improve the yield of spinosad in S. spinosa.

RESULTS

Firstly, a genome-scale metabolic network reconstruction (GSMR) for S.spinosa based on its genome information, literature data and experimental data was established. The model was consists of 1,577 reactions, 1,726 metabolites, and 733 enzymes after manually refined. Then, amino acids supplying experiments were performed in order to test the capabilities of the model, and the results showed a high consistency. Subsequently, transhydrogenase (PntAB, EC 1.6.1.2) was chosen as the potential target for spinosad yield improvement based on the in silico metabolic network models. Furthermore, the target gene was manipulated in the parent strain in order to validate the model predictions. At last, shake flask fermentation was carried out which led to spinosad production of 75.32 mg/L, 86.5% higher than the parent strain (40.39 mg/L).

CONCLUSIONS

Results confirmed the model had a high potential in engineering S. spinosa for spinosad production. It is the first GSMM for S.spinosa, it has significance for a better understanding of the comprehensive metabolism and guiding strain designing of Saccharopolyspora spinosa in the future.

Dynamic flux balance analysis of batch fermentation: effect of genetic manipulations on ethanol production

In silico optimization of bioethanol production from lignocellulosic biomasses is investigated by combining process systems engineering approach and systems biology approach. Lignocellulosic biomass is an attractive sustainable carbon source for fermentative production of bioethanol. For enhanced ethanol production, metabolic engineering of wild-type strains-that can metabolize both hexose and pentose sugars or microbial consortia consisting of substrate-selective microbes-may be advantageous. This study presents a detailed in silico analysis of bioethanol production from glucose-xylose mixtures of various compositions by batch mono-culture and co-culture fermentation of specialized microbes. Dynamic flux balance models based on available genome-scale reconstructions of the microorganisms have been used to analyze bioethanol production, and the maximization of ethanol productivity is addressed by computing optimal aerobic-anaerobic switching times. Effects of ten metabolic engineering strategies that have been suggested in the literature for ethanol overproduction, have been evaluated for their efficiency in enhancing the ethanol productivity in the context of batch mono-culture and co-culture processes.

Dynamic strain scanning optimization: an efficient strain design strategy for balanced yield, titer, and productivity. DySScO strategy for strain design

Background

In recent years, constraint-based metabolic models have emerged as an important tool for metabolic engineering; a number of computational algorithms have been developed for identifying metabolic engineering strategies where the production of the desired chemical is coupled with the growth of the organism. A caveat of the existing algorithms is that they do not take the bioprocess into consideration; as a result, while the product yield can be optimized using these algorithms, the product titer and productivity cannot be optimized. In order to address this issue, we developed the Dynamic Strain Scanning Optimization (DySScO) strategy, which integrates the Dynamic Flux Balance Analysis (dFBA) method with existing strain algorithms.

Results

In order to demonstrate the effective of the DySScO strategy, we applied this strategy to the design of Escherichia coli strains targeted for succinate and 1,4-butanediol production respectively. We evaluated consequences of the tradeoff between growth yield and product yield with respect to titer and productivity, and showed that the DySScO strategy is capable of producing strains that balance the product yield, titer, and productivity. In addition, we evaluated the economic viability of the designed strain, and showed that the economic performance of a strain can be strongly affected by the price difference between the product and the feedstock.

Conclusion

Our study demonstrated that the DySScO strategy is a useful computational tool for designing microbial strains with balanced yield, titer, and productivity, and has potential applications in evaluating the economic performance of the design strains.

Towards metagenome-scale models for industrial applications--the case of Lactic Acid Bacteria

We review the uses and limitations of modelling approaches that are in use in the field of Lactic Acid Bacteria (LAB). We describe recent developments in model construction and computational methods, starting from application of such models to monocultures. However, since most applications in food biotechnology involve complex nutrient environments and mixed cultures, we extend the scope to discuss developments in modelling such complex systems. With metagenomics and meta-functional genomics data becoming available, the developments in genome-scale community models are discussed. We conclude that exploratory tools are available and useful, but truly predictive mechanistic models will remain a major challenge in the field.

Prediction of dynamic metabolic behavior of Pediococcus pentosaceus producing lactic acid from lignocellulosic sugars

A dynamic metabolic model is presented for Pediococcus pentosaceus producing lactic acid from lignocellulose-derived mixed sugars including glucose, mannose, galactose, arabinose, and xylose. Depending on the pairs of mixed sugars, P. pentosaceus exhibits diverse (i.e., sequential, simultaneous or mixed) consumption patterns. This regulatory behavior of P. pentosaceus is portrayed using the hybrid cybernetic model (HCM) framework which views elementary modes of the network as metabolic options dynamically modulated. Comprehensive data are collected for model identification and validation through fermentation experiments involving single substrates and various combinations of mixed sugars. Most sugars are metabolized rather sequentially while co-consumption of galactose and arabinose is observed. It is demonstrated that the developed HCM successfully predicts mixed sugar data based on the parameters identified mostly from single substrate data only. Further, we discuss the potential of HCMs as a tool for predicting intracellular flux distribution with comparison with flux balance analysis.

Integrating flux balance analysis into kinetic models to decipher the dynamic metabolism of Shewanella oneidensis MR-1

Shewanella oneidensis MR-1 sequentially utilizes lactate and its waste products (pyruvate and acetate) during batch culture. To decipher MR-1 metabolism, we integrated genome-scale flux balance analysis (FBA) into a multiple-substrate Monod model to perform the dynamic flux balance analysis (dFBA). The dFBA employed a static optimization approach (SOA) by dividing the batch time into small intervals (i.e., ∼400 mini-FBAs), then the Monod model provided time-dependent inflow/outflow fluxes to constrain the mini-FBAs to profile the pseudo-steady-state fluxes in each time interval. The mini-FBAs used a dual-objective function (a weighted combination of "maximizing growth rate" and "minimizing overall flux") to capture trade-offs between optimal growth and minimal enzyme usage. By fitting the experimental data, a bi-level optimization of dFBA revealed that the optimal weight in the dual-objective function was time-dependent: the objective function was constant in the early growth stage, while the functional weight of minimal enzyme usage increased significantly when lactate became scarce. The dFBA profiled biologically meaningful dynamic MR-1 metabolisms: 1. the oxidative TCA cycle fluxes increased initially and then decreased in the late growth stage; 2. fluxes in the pentose phosphate pathway and gluconeogenesis were stable in the exponential growth period; and 3. the glyoxylate shunt was up-regulated when acetate became the main carbon source for MR-1 growth.

Exploring the gap between dynamic and constraint-based models of metabolism

Systems biology provides new approaches for metabolic engineering through the development of models and methods for simulation and optimization of microbial metabolism. Here we explore the relationship between two modeling frameworks in common use namely, dynamic models with kinetic rate laws and constraint-based flux models. We compare and analyze dynamic and constraint-based formulations of the same model of the central carbon metabolism of Escherichia coli. Our results show that, if unconstrained, the space of steady states described by both formulations is the same. However, the imposition of parameter-range constraints can be mapped into kinetically feasible regions of the solution space for the dynamic formulation that is not readily transferable to the constraint-based formulation. Therefore, with partial kinetic parameter knowledge, dynamic models can be used to generate constraints that reduce the solution space below that identified by constraint-based models, eliminating infeasible solutions and increasing the accuracy of simulation and optimization methods.

Systems biology of lactic acid bacteria: a critical review

Understanding the properties of a system as emerging from the interaction of well described parts is the most important goal of Systems Biology. Although in the practice of Lactic Acid Bacteria (LAB) physiology we most often think of the parts as the proteins and metabolites, a wider interpretation of what a part is can be useful. For example, different strains or species can be the parts of a community, or we could study only the chemical reactions as the parts of metabolism (and forgetting about the enzymes that catalyze them), as is done in flux balance analysis. As long as we have some understanding of the properties of these parts, we can investigate whether their interaction leads to novel or unanticipated behaviour of the system that they constitute.

There has been a tendency in the Systems Biology community to think that the collection and integration of data should continue ad infinitum, or that we will otherwise not be able to understand the systems that we study in their details. However, it may sometimes be useful to take a step back and consider whether the knowledge that we already have may not explain the system behaviour that we find so intriguing. Reasoning about systems can be difficult, and may require the application of mathematical techniques. The reward is sometimes the realization of unexpected conclusions, or in the worst case, that we still do not know enough details of the parts, or of the interactions between them.

We will discuss a number of cases, with a focus on LAB-related work, where a typical systems approach has brought new knowledge or perspective, often counterintuitive, and clashing with conclusions from simpler approaches. Also novel types of testable hypotheses may be generated by the systems approach, which we will illustrate. Finally we will give an outlook on the fields of research where the systems approach may point the way for the near future.

Expanding a dynamic flux balance model of yeast fermentation to genome-scale

Background

Yeast is considered to be a workhorse of the biotechnology industry for the production of many value-added chemicals, alcoholic beverages and biofuels. Optimization of the fermentation is a challenging task that greatly benefits from dynamic models able to accurately describe and predict the fermentation profile and resulting products under different genetic and environmental conditions. In this article, we developed and validated a genome-scale dynamic flux balance model, using experimentally determined kinetic constraints.

Results

Appropriate equations for maintenance, biomass composition, anaerobic metabolism and nutrient uptake are key to improve model performance, especially for predicting glycerol and ethanol synthesis. Prediction profiles of synthesis and consumption of the main metabolites involved in alcoholic fermentation closely agreed with experimental data obtained from numerous lab and industrial fermentations under different environmental conditions. Finally, fermentation simulations of genetically engineered yeasts closely reproduced previously reported experimental results regarding final concentrations of the main fermentation products such as ethanol and glycerol.

Conclusion

A useful tool to describe, understand and predict metabolite production in batch yeast cultures was developed. The resulting model, if used wisely, could help to search for new metabolic engineering strategies to manage ethanol content in batch fermentations.

Bridging the gap between fluxomics and industrial biotechnology

Metabolic flux analysis is a vital tool used to determine the ultimate output of cellular metabolism and thus detect biotechnologically relevant bottlenecks in productivity. ¹³C-based metabolic flux analysis (¹³C-MFA) and flux balance analysis (FBA) have many potential applications in biotechnology. However, noteworthy hurdles in fluxomics study are still present. First, several technical difficulties in both ¹³C-MFA and FBA severely limit the scope of fluxomics findings and the applicability of obtained metabolic information. Second, the complexity of metabolic regulation poses a great challenge for precise prediction and analysis of metabolic networks, as there are gaps between fluxomics results and other omics studies. Third, despite identified metabolic bottlenecks or sources of host stress from product synthesis, it remains difficult to overcome inherent metabolic robustness or to efficiently import and express nonnative pathways. Fourth, product yields often decrease as the number of enzymatic steps increases. Such decrease in yield may not be caused by rate-limiting enzymes, but rather is accumulated through each enzymatic reaction. Fifth, a high-throughput fluxomics tool hasnot been developed for characterizing nonmodel microorganisms and maximizing their application in industrial biotechnology. Refining fluxomics tools and understanding these obstacles will improve our ability to engineer highlyefficient metabolic pathways in microbial hosts.

Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology

Driven by advancements in high-throughput biological technologies and the growing number of sequenced genomes, the construction of in silico models at the genome scale has provided powerful tools to investigate a vast array of biological systems and applications. Here, we review comprehensively the uses of such models in industrial and medical biotechnology, including biofuel generation, food production, and drug development. While the use of in silico models is still in its early stages for delivering to industry, significant initial successes have been achieved. For the cases presented here, genome-scale models predict engineering strategies to enhance properties of interest in an organism or to inhibit harmful mechanisms of pathogens. Going forward, genome-scale in silico models promise to extend their application and analysis scope to become a trans-formative tool in biotechnology.