Metabolism of citric acid production by Aspergillus niger: model definition, steady-state analysis and constrained optimization of citric acid production rate

Recent advances and perspectives on production of value-added organic acids through metabolic engineering

Organic acids are important consumable materials with a wide range of applications in the food, biopolymer and chemical industries. The global consumer organic acids market is estimated to increase to $36.86 billion by 2026. Conventionally, organic acids are produced from the chemical catalysis process with petrochemicals as raw materials, which posts severe environmental concerns and conflicts with our sustainable development goals. Most of the commonly used organic acids can be produced from various organisms. As a state-of-the-art technology, large-scale fermentative production of important organic acids with genetically-modified microbes has become an alternative to the chemical route to meet the market demand. Despite the fact that bio-based organic acid production from renewable cheap feedstock provides a viable solution, low productivity has impeded their industrial-scale application. With our deeper understanding of strain genetics, physiology and the availability of strain engineering tools, new technologies including synthetic biology, various metabolic engineering strategies, omics-based system biology tools, and high throughput screening methods are gradually established to bridge our knowledge gap. And they were further applied to modify the cellular reaction networks of potential microbial hosts and improve the strain performance, which facilitated the commercialization of consumable organic acids. Here we present the recent advances of metabolic engineering strategies to improve the production of important organic acids including fumaric acid, citric acid, itaconic acid, adipic acid, muconic acid, and we also discuss the current challenges and future perspectives on how we can develop a cost-efficient, green and sustainable process to produce these important chemicals from low-cost feedstocks.

The white koji fungus Aspergillus luchuensis mut. kawachii

The white koji fungus, Aspergillus luchuensis mut. kawachii, is used in the production of shochu, a traditional Japanese distilled spirit. White koji fungus plays an important role in the shochu production process by supplying amylolytic enzymes such as α-amylase and glucoamylase. These enzymes convert starch contained in primary ingredients such as rice, barley, buckwheat, and sweet potato into glucose, which is subsequently utilized by the yeast Saccharomyces cerevisiae to produce ethanol. White koji fungus also secretes large amounts of citric acid, which lowers the pH of the shochu mash, thereby preventing the growth of undesired microbes and enabling stable production of shochu in relatively warm regions of Japan. This review describes the historical background, research tools, and recent advances in studies of the mechanism of citric acid production by white koji fungus.

Making Traditional Japanese Distilled Liquor, Shochu and Awamori, and the Contribution of White and Black Koji Fungi

The traditional Japanese single distilled liquor, which uses koji and yeast with designated ingredients, is called "honkaku shochu." It is made using local agricultural products and has several types, including barley shochu, sweet potato shochu, rice shochu, and buckwheat shochu. In the case of honkaku shochu, black koji fungus (Aspergillus luchuensis) or white koji fungus (Aspergillus luchuensis mut. kawachii) is used to (1) saccharify the starch contained in the ingredients, (2) produce citric acid to prevent microbial spoilage, and (3) give the liquor its unique flavor. In order to make delicious shochu, when cultivating koji fungus during the shochu production process, we use a unique temperature control method to ensure that these three important elements, which greatly affect the taste of the produced liquor, are balanced without any excess or deficiency. This review describes in detail the production method of honkaku shochu, a distilled spirit unique to Japan and whose market is expected to expand worldwide, with special attention paid to the koji fungi cultivation step. Furthermore, we describe the history of the koji fungi used today in the production of shochu, and we provide a thorough explanation of the characteristics of each koji fungi. We also report the latest research progress on this topic.

Aspergillus niger citrate exporter revealed by comparison of two alternative citrate producing conditions

Currently, there is no consensus regarding the mechanism underlying Aspergillus niger citrate biosynthesis and secretion. We hypothesise that depending on the experimental setup, extracellular citrate accumulation can have fundamentally different underlying transcriptomic landscapes. We show that varying the amount and type of supplement of an arginine auxotrophic A. niger strain results in transcriptional down-regulation of citrate metabolising enzymes in the condition in which more citrate is accumulated extracellularly. This contrasts with the transcriptional adaptations when increased citrate production is triggered by iron limitation. By combining gene expression data obtained from these two very distinct experimental setups with hidden Markov models and transporter homology approaches, we were able to compile a shortlist of the most likely citrate transporter candidates. Two candidates (An17g01710 and An09g06720m.01) were heterologously expressed in the yeast Saccharomyces cerevisiae, and one of the resultant mutants showed the ability to secrete citrate. Our findings provide steps in untangling the complex interplay of different mechanisms underlying A. niger citrate accumulation, and we demonstrate how a comparative transcriptomics approach complemented with further bioinformatics analyses can be used to pinpoint a fungal citrate exporter.

LaeA Controls Citric Acid Production through Regulation of the Citrate Exporter-Encoding cexA Gene in Aspergillus luchuensis mut. kawachii

The putative methyltransferase LaeA is a global regulator of metabolic and development processes in filamentous fungi. We characterized the homologous laeA genes of the white koji fungus Aspergillus luchuensis mut. kawachii (A. kawachii) to determine their role in citric acid hyperproduction. The ΔlaeA strain exhibited a significant reduction in citric acid production. Cap analysis gene expression (CAGE) revealed that laeA is required for the expression of a putative citrate exporter-encoding cexA gene, which is critical for citric acid production. Deficient citric acid production by a ΔlaeA strain was rescued by the overexpression of cexA to a level comparable with that of a cexA-overexpressing ΔcexA strain. In addition, chromatin immunoprecipitation coupled with quantitative PCR (ChIP-qPCR) analysis indicated that LaeA regulates the expression of cexA via methylation levels of the histones H3K4 and H3K9. These results indicate that LaeA is involved in citric acid production through epigenetic regulation of cexA in A. kawachii IMPORTANCE A. kawachii has been traditionally used for production of the distilled spirit shochu in Japan. Citric acid produced by A. kawachii plays an important role in preventing microbial contamination during the shochu fermentation process. This study characterized homologous laeA genes; using CAGE, complementation tests, and ChIP-qPCR, it was found that laeA is required for citric acid production through the regulation of cexA in A. kawachii The epigenetic regulation of citric acid production elucidated in this study will be useful for controlling the fermentation processes of shochu.

Kinetic modeling of Stickland reactions-coupled methanogenesis for a methanogenic culture

Studying amino acid catabolism-coupled methanogenesis is the important standpoints to decipher the metabolic behavior of a methanogenic culture. l-Glycine and l-alanine are acted as sole carbon and nitrogen sources for acidogenic bacteria. One amino acid is oxidized and another one is reduced for acetate production via pyruvate by oxidative deamination process in the Stickland reactions. Herein, we have developed a kinetic model for the Stickland reactions-coupled methanogenesis (SRCM) and simulated objectively to maximize the rate of methane production. We collected the metabolic information from enzyme kinetic parameters for amino acid catabolism of Clostridium acetobutylicum ATCC 824 and methanogenesis of Methanosarcina acetivorans C2A. The SRCM model of this study consisted of 18 reactions and 61 metabolites with enzyme kinetic parameters derived experimental data. The internal or external metabolic flux rate of this system found to control the acidogenesis and methanogenesis in a methanogenic culture. Using the SRCM model, flux distributions were calculated for each reaction and metabolite in order to maximize the methane production rate from the glycine–alanine pair. Results of this study, we demonstrated the metabolic behavior, metabolite pairing while mutually interact, and advantages of syntrophic metabolism of amino acid-directed methane production in a methanogenic starter culture.

Mitochondrial Citrate Transporters CtpA and YhmA Are Required for Extracellular Citric Acid Accumulation and Contribute to Cytosolic Acetyl Coenzyme A Generation in Aspergillus luchuensis mut. kawachii

Aspergillus luchuensis mut. kawachii (A. kawachii) produces a large amount of citric acid during the process of fermenting shochu, a traditional Japanese distilled spirit. In this study, we characterized A. kawachii CtpA and YhmA, which are homologous to the yeast Saccharomyces cerevisiae mitochondrial citrate transporters Ctp1 and Yhm2, respectively. CtpA and YhmA were purified from A. kawachii and reconstituted into liposomes. The proteoliposomes exhibited only counterexchange transport activity; CtpA transported citrate using countersubstrates, especially cis-aconitate and malate, whereas YhmA transported citrate using a wider variety of countersubstrates, including citrate, 2-oxoglutarate, malate, cis-aconitate, and succinate. Disruption of ctpA and yhmA caused deficient hyphal growth and conidium formation with reduced mycelial weight-normalized citrate production. Because we could not obtain a ΔctpA ΔyhmA strain, we constructed an S-tagged ctpA (ctpA-S) conditional expression strain in the ΔyhmA background using the Tet-On promoter system. Knockdown of ctpA-S in ΔyhmA resulted in a severe growth defect on minimal medium with significantly reduced acetyl coenzyme A (acetyl-CoA) and lysine levels, indicating that double disruption of ctpA and yhmA leads to synthetic lethality; however, we subsequently found that the severe growth defect was relieved by addition of acetate or lysine, which could remedy the acetyl-CoA level. Our results indicate that CtpA and YhmA are mitochondrial citrate transporters involved in citric acid production and that transport of citrate from mitochondria to the cytosol plays an important role in acetyl-CoA biogenesis in A. kawachii IMPORTANCE Citrate transport is believed to play a significant role in citrate production by filamentous fungi; however, details of the process remain unclear. This study characterized two citrate transporters from Aspergillus luchuensis mut. kawachii Biochemical and gene disruption analyses showed that CtpA and YhmA are mitochondrial citrate transporters required for normal hyphal growth, conidium formation, cytosolic acetyl-CoA synthesis, and citric acid production. The characteristics of fungal citrate transporters elucidated in this study will help expand our understanding of the citrate production mechanism and facilitate the development and optimization of industrial organic acid fermentation processes.

Systems metabolic engineering for citric acid production by Aspergillus niger in the post-genomic era

Citric acid is the world’s largest consumed organic acid and is widely used in beverage, food and pharmaceutical industries. Aspergillus niger is the main industrial workhorse for citric acid production. Since the release of the genome sequence, extensive multi-omic data are being rapidly obtained, which greatly boost our understanding of the citric acid accumulation mechanism in A. niger to a molecular and system level. Most recently, the rapid development of CRISPR/Cas9 system facilitates highly efficient genome-scale genetic perturbation in A. niger. In this review, we summarize the impact of systems biology on the citric acid molecular regulatory mechanisms, the advances in metabolic engineering strategies for enhancing citric acid production and discuss the development and application of CRISPR/Cas9 systems for genome editing in A. niger. We believe that future systems metabolic engineering efforts will redesign and engineer A. niger as a highly optimized cell factory for industrial citric acid production.

The effect of ambient pH modulation on ochratoxin A accumulation by Aspergillus carbonarius

Aspergillus carbonarius, the main cause of severe post-harvest decay of vine fruit, is considered the major source of ochratoxin A (OTA) contamination of grapes and derived products. The factors inducing OTA accumulation by A. carbonarius and its contribution to pathogenicity remain unclear. Present findings indicate that the production of organic acids, such as D-gluconic acid (GLA) and citric acid, by A. carbonarius in the growth medium or in the decayed fruit tissue was directly related to ambient pH reduction. Under these conditions, induced transcript expression of genes involved in OTA biosynthesis occurred concurrently with mycotoxin accumulation. The high accumulation of OTA during acidification process raised the question of its importance in host-pathogen interactions during the fungal colonisation. Treatment of colonised grapes with sodium bicarbonate reduced accumulation of organic acid and OTA with a concomitant reduction in decay development, suggesting that tissue acidification is a significant factor in A. carbonarius pathogenicity. The present findings suggest that ambient pH is a regulatory signal for induction of mycotoxin production by A. carbonarius under the dynamic nutritional growth conditions occurring in culture. Yet the molecular mechanisms of OTA biosynthesis induction during colonisation of the acidic host environment are still unclear and should be further investigated.

The best models of metabolism

Biochemical systems are among of the oldest application areas of mathematical modeling. Spanning a time period of over one hundred years, the repertoire of options for structuring a model and for formulating reactions has been constantly growing, and yet, it is still unclear whether or to what degree some models are better than others and how the modeler is to choose among them. In fact, the variety of options has become overwhelming and difficult to maneuver for novices and experts alike. This review outlines the metabolic model design process and discusses the numerous choices for modeling frameworks and mathematical representations. It tries to be inclusive, even though it cannot be complete, and introduces the various modeling options in a manner that is as unbiased as that is feasible. However, the review does end with personal recommendations for the choices of default models. WIREs Syst Biol Med 2017, 9:e1391. doi: 10.1002/wsbm.1391 For further resources related to this article, please visit the WIREs website.

Experimental design data for the biosynthesis of citric acid using Central Composite Design method

In the present investigation, we report that statistical design and optimization of significant variables for the microbial production of citric acid from sucrose in presence of filamentous fungi A. niger NCIM 705. Various combinations of experiments were designed with Central Composite Design (CCD) of Response Surface Methodology (RSM) for the production of citric acid as a function of six variables. The variables are; initial sucrose concentration, initial pH of medium, fermentation temperature, incubation time, stirrer rotational speed, and oxygen flow rate. From experimental data, a statistical model for this process has been developed. The optimum conditions reported in the present article are initial concentration of sucrose of 163.6 g/L, initial pH of medium 5.26, stirrer rotational speed of 247.78 rpm, incubation time of 8.18 days, fermentation temperature of 30.06 °C and flow rate of oxygen of 1.35 lpm. Under optimum conditions the predicted maximum citric acid is 86.42 g/L. The experimental validation carried out under the optimal values and reported citric acid to be 82.0 g/L. The model is able to represent the experimental data and the agreement between the model and experimental data is good.

Computational fluid dynamic (CFD) modeling of simultaneous extraction and fermentation process in a single sugar beet cossette

For simulations of flow and microbial conversion reactions, related to modeling of simultaneous extraction and fermentation process in a single sugar beet cossette a software package OpenFOAM was used. The mass transfer of the components (sucrose, glucose, fructose and ethanol) in the studied system was controlled by the convection and diffusion processes. Microbial conversion rates and yield coefficients were experimentally determined and/or estimated by mathematical simulation. Dimensions of the model sugar beet cossette (SBC) were: average length of cosettes 40.10 mm, average thickness 3.32 mm and average width 3.5 mm, and represented in the model as a square-shape cross-section mathematical simulation. Dimensions of the model sugar beet cossette (SBC) were: average length of cosettes 40.10 mm, average thickness 3.32 mm and average width 3.5 mm, and represented in the model as a square-shape cross-section used to study the mass transfer and microbial conversion rates on the scale of single sugar beet cossette in the short time scales (up to 25 s). This model can be used for simulation of extractant flow around single sugar beet cossette as well as for description of simultaneous extraction and fermentation process in the studied system.

Deletion of glucose oxidase changes the pattern of organic acid production in Aspergillus carbonarius

Aspergillus carbonarius has potential as a cell factory for the production of different organic acids. At pH 5.5, A.carbonarius accumulates high amounts of gluconic acid when it grows on glucose based medium whereas at low pH, it produces citric acid. The conversion of glucose to gluconic acid is carried out by secretion of the enzyme, glucose oxidase. In this work, the gene encoding glucose oxidase was identified and deleted from A. carbonarius with the aim of changing the carbon flux towards other organic acids. The effect of genetic engineering was examined by testing glucose oxidase deficient (Δgox) mutants for the production of different organic acids in a defined production medium. The results obtained showed that the gluconic acid accumulation was completely inhibited and increased amounts of citric acid, oxalic acid and malic acid were observed in the Δgox mutants.

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.

Both decrease in ACL1 gene expression and increase in ICL1 gene expression in marine-derived yeast Yarrowia lipolytica expressing INU1 gene enhance citric acid production from inulin

In this study, some of the ATP-citrate lyase genes (ACL1) were deleted and the copy number of the iso-citrate lyase gene (ICL1) was increased in the marine-derived yeast Yarrowia lipolytica SWJ-1b displaying the recombinant inulinase. It was found that lipid content and iso-citric acid in the transformant 30 obtained were greatly reduced and citric acid production was greatly enhanced. It was also found that the ACL1 gene expression and ATP-citrate lyase activity in the transformant 30 were declined and the ICL1 gene expression and iso-citrate lyase activity were promoted. During the 2-l fermentation, 84.0 g/l of citric acid and 1.8 g/l of iso-citric acid in the fermented medium were attained from 10.0 % of inulin by the transformant 30 within 214 h. The results showed that only 0.36 % of the residual reducing sugar and 1.0 % of the residual total sugar were left in the fermented medium, suggesting that 89.6 % of the total sugar was used for citric acid production and cell growth by the transformant 30.

Biochemical Systems Theory: A Review

Biochemical systems theory (BST) is the foundation for a set of analytical andmodeling tools that facilitate the analysis of dynamic biological systems. This paper depicts major developments in BST up to the current state of the art in 2012. It discusses its rationale, describes the typical strategies and methods of designing, diagnosing, analyzing, and utilizing BST models, and reviews areas of application. The paper is intended as a guide for investigators entering the fascinating field of biological systems analysis and as a resource for practitioners and experts.

Identifying the preferred subset of enzymatic profiles in nonlinear kinetic metabolic models via multiobjective global optimization and Pareto filters

Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the ethanol production in the fermentation of Saccharomyces cerevisiae.

Multi-objective optimization of enzyme manipulations in metabolic networks considering resilience effects

BACKGROUND

Improving the synthesis rate of desired metabolites in metabolic systems is one of the main tasks in metabolic engineering. In the last decade, metabolic engineering approaches based on the mathematical optimization have been used extensively for the analysis and manipulation of metabolic networks. Experimental evidence shows that mutants reflect resilience phenomena against gene alterations. Although researchers have published many studies on the design of metabolic systems based on kinetic models and optimization strategies, almost no studies discuss the multi-objective optimization problem for enzyme manipulations in metabolic networks considering resilience phenomenon.

RESULTS

This study proposes a generalized fuzzy multi-objective optimization approach to formulate the enzyme intervention problem for metabolic networks considering resilience phenomena and cell viability. This approach is a general framework that can be applied to any metabolic networks to investigate the influence of resilience phenomena on gene intervention strategies and maximum target synthesis rates. This study evaluates the performance of the proposed approach by applying it to two metabolic systems: S. cerevisiae and E. coli. Results show that the maximum synthesis rates of target products by genetic interventions are always over-estimated in metabolic networks that do not consider the resilience effects.

CONCLUSIONS

Considering the resilience phenomena in metabolic networks can improve the predictions of gene intervention and maximum synthesis rates in metabolic engineering. The proposed generalized fuzzy multi-objective optimization approach has the potential to be a good and practical framework in the design of metabolic networks.

Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models

Background

Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization.

Results

Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity.

Conclusions

Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task.

Morphology of filamentous fungi: linking cellular biology to process engineering using Aspergillus niger

In various biotechnological processes, filamentous fungi, e.g. Aspergillus niger, are widely applied for the production of high value-added products due to their secretion efficiency. There is, however, a tangled relationship between the morphology of these microorganisms, the transport phenomena and the related productivity. The morphological characteristics vary between freely dispersed mycelia and distinct pellets of aggregated biomass. Hence, advantages and disadvantages for mycel or pellet cultivation have to be balanced out carefully. Due to this inadequate understanding of morphogenesis of filamentous microorganisms, fungal morphology, along with reproducibility of inocula of the same quality, is often a bottleneck of productivity in industrial production. To obtain an optimisation of the production process it is of great importance to gain a better understanding of the molecular and cell biology of these microorganisms as well as the approaches in biochemical engineering and particle technique, in particular to characterise the interactions between the growth conditions, cell morphology, spore-hyphae-interactions and product formation. Advances in particle and image analysis techniques as well as micromechanical devices and their applications to fungal cultivations have made available quantitative morphological data on filamentous cells. This chapter provides the ambitious aspects of this line of action, focussing on the control and characterisation of the morphology, the transport gradients and the approaches to understand the metabolism of filamentous fungi. Based on these data, bottlenecks in the morphogenesis of A. niger within the complex production pathways from gene to product should be identified and this may improve the production yield.

Modelling metabolic networks using power-laws and S-systems

Mathematical modelling has great potential in biochemical network analysis because, in contrast with the unaided human mind, mathematics has no problems keeping track of hundreds of interacting variables that affect each other in intricate ways. The scalability of mathematical models, together with their ability to capture all imaginable non-linear responses, allows us to explore the dynamics of complicated pathway systems, to study what happens if a metabolite, gene or enzyme is altered, and to optimize biochemical systems, for instance toward the goal of increased yield of some desired organic compound. Before we can utilize models for such purposes, we must define their mathematical structure and identify suitable parameter values. Because nature has not provided us with guidelines for selecting the best model design, the choice of the most useful model is not trivial. In the present chapter I show that power-law modelling within BST (Biochemical Systems Theory) offers guidance for model selection, construction and analysis that is otherwise difficult to find.

Filamentous fungi for production of food additives and processing aids

Filamentous fungi are metabolically versatile organisms with a very wide distribution in nature. They exist in association with other species, e.g. as lichens or mycorrhiza, as pathogens of animals and plants or as free-living species. Many are regarded as nature's primary degraders because they secrete a wide variety of hydrolytic enzymes that degrade waste organic materials. Many species produce secondary metabolites such as polyketides or peptides and an increasing range of fungal species is exploited commercially as sources of enzymes and metabolites for food or pharmaceutical applications. The recent availability of fungal genome sequences has provided a major opportunity to explore and further exploit fungi as sources of enzymes and metabolites. In this review chapter we focus on the use of fungi in the production of food additives but take a largely pre-genomic, albeit a mainly molecular, view of the topic.

Design of Metabolic Engineering Strategies for Maximizing l-(-)-Carnitine Production by Escherichia coli. Integration of the Metabolic and Bioreactor Levels

In this work metabolic engineering strategies for maximizing L-(-)-carnitine production by Escherichia coli based on the Biochemical System Theory and the Indirect Optimization Method are presented. The model integrates the metabolic and the bioreactor levels using power-law formalism. Based on the S-system model, the Indirect Optimization Method was applied, leading to profiles of parameter values that are compatible with both the physiology of the cells and the bioreactor system operating conditions. This guarantees their viability and fitness and yields higher rates of L-(-)-carnitine production. Experimental results using a high cell density reactor were compared with optimized predictions from the Indirect Optimization Method. When two parameters (the dilution rate and the initial crotonobetaine concentration) were directly changed in the real experimental system to the prescribed optimum values, the system showed better performance in L-(-)-carnitine production (74% increase in production rate), in close agreement with the model's predictions. The model shows control points at macroscopic (reactor operation) and microscopic (molecular) levels where conversion and productivity can be increased. In accordance with the optimized solution, the next logical step to improve the L-(-)-carnitine production rate will involve metabolic engineering of the E. coli strain by overexpressing the carnitine transferase, CaiB, activity and the protein carrier, CaiT, responsible for substrate and product transport in and out of the cell. By this means it is predicted production may be enhanced by up to three times the original value.

Coordination of the dynamics of yeast sphingolipid metabolism during the diauxic shift

BACKGROUND

The diauxic shift in yeast requires cells to coordinate a complicated response that involves numerous genes and metabolic processes. It is unknown whether responses of this type are mediated in vivo through changes in a few "key" genes and enzymes, which are mathematically characterized by high sensitivities, or whether they are based on many small changes in genes and enzymes that are not particularly sensitive. In contrast to global assessments of changes in gene or protein interaction networks, we study here control aspects of the diauxic shift by performing a detailed analysis of one specific pathway-sphingolipid metabolism-which is known to have signaling functions and is associated with a wide variety of stress responses.

RESULTS

The approach uses two components: publicly available sets of expression data of sphingolipid genes and a recently developed Generalized Mass Action (GMA) mathematical model of the sphingolipid pathway. In one line of exploration, we analyze the sensitivity of the model with respect to enzyme activities, and thus gene expression. Complementary to this approach, we convert the gene expression data into changes in enzyme activities and then predict metabolic consequences by means of the mathematical model. It was found that most of the sensitivities in the model are low in magnitude, but that some stand out as relatively high. This information was then deployed to test whether the cell uses a few of the very sensitive pathway steps to mount a response or whether the control is distributed throughout the pathway. Pilot experiments confirm qualitatively and in part quantitatively the predictions of a group of metabolite simulations.

CONCLUSION

The results indicate that yeast coordinates sphingolipid mediated changes during the diauxic shift through an array of small changes in many genes and enzymes, rather than relying on a strategy involving a few select genes with high sensitivity. This study also highlights a novel approach in coupling data mining with mathematical modeling in order to evaluate specific metabolic pathways.

Optimization of biotechnological systems through geometric programming

Background

In the past, tasks of model based yield optimization in metabolic engineering were either approached with stoichiometric models or with structured nonlinear models such as S-systems or linear-logarithmic representations. These models stand out among most others, because they allow the optimization task to be converted into a linear program, for which efficient solution methods are widely available. For pathway models not in one of these formats, an Indirect Optimization Method (IOM) was developed where the original model is sequentially represented as an S-system model, optimized in this format with linear programming methods, reinterpreted in the initial model form, and further optimized as necessary.

Results

A new method is proposed for this task. We show here that the model format of a Generalized Mass Action (GMA) system may be optimized very efficiently with techniques of geometric programming. We briefly review the basics of GMA systems and of geometric programming, demonstrate how the latter may be applied to the former, and illustrate the combined method with a didactic problem and two examples based on models of real systems. The first is a relatively small yet representative model of the anaerobic fermentation pathway in S. cerevisiae, while the second describes the dynamics of the tryptophan operon in E. coli. Both models have previously been used for benchmarking purposes, thus facilitating comparisons with the proposed new method. In these comparisons, the geometric programming method was found to be equal or better than the earlier methods in terms of successful identification of optima and efficiency.

Conclusion

GMA systems are of importance, because they contain stoichiometric, mass action and S-systems as special cases, along with many other models. Furthermore, it was previously shown that algebraic equivalence transformations of variables are sufficient to convert virtually any types of dynamical models into the GMA form. Thus, efficient methods for optimizing GMA systems have multifold appeal.

Metabolic flux analysis using stoichiometric models for Aspergillus niger: comparison under glucoamylase-producing and non-producing conditions

Aspergillus niger AB1.13 cultures with glucoamylase production (with D-glucose as substrate) and without glucoamylase production (with D-xylose as substrate) were characterized by metabolic flux analysis. Two comprehensive metabolic models for d-glucose- as well as for D-xylose-consumption were used to quantify and compare the metabolic fluxes through the central pathways of carbon metabolism at different pH-values. The models consist of the most relevant metabolic pathways for A. niger including glycolysis, pentose-phosphate pathway, citrate cycle, energy metabolism and anaplerotic reactions comprising two intracellular compartments, the cytoplasm and mitochondrion. When D-xylose was used as the sole carbon source, the relative flux of the substrate through the oxidative pentose-phosphate pathway (PPP) via G6P-dehydrogenase was unaffected by the pH-value of the culture medium. About 30% of D-xylose consumed was routed through the oxidative PPP. In contrast, the flux of D-glucose (i.e., under glucoamylase-producing conditions) through the oxidative PPP was remarkably higher and, in addition was significantly affected by the pH-value of the culture medium (40% at pH 5.5, 56% at pH 3.7, respectively). Summarizing, the flux through the PPP under glucoamylase producing conditions was 30-90% higher than for non-producing conditions.

Metabolic flux analysis for optimizing the specific growth rate of recombinant Aspergillus niger

A comprehensive metabolic network comprising three intracellular compartments (cytoplasm, mitochondrion and peroxisome) was developed for Aspergillus niger. The metabolic flux network includes carbohydrate and amino acid metabolism in both anabolic and catabolic reactions. Linear programming was used for the optimization of the specific growth rates in combination with 37 measured input and output fluxes of the key metabolites to evaluate corresponding intracellular flux distributions throughout the batch fermentations. Logarithmic sensitivity analysis revealed that the addition of proline, alanine and glutamate benefited growth in defined media. The experimental observations and flux analysis showed that tyrosine was a potential candidate for biomass production improvement. Model predictions was verified by conducting batch and fed-batch fermentations and it was found that the addition of the four amino acids according to the predetermined schedule resulted in a 44 and 41% improvements in biomass and recombinant protein productions, respectively.