Understanding the adaptive growth strategy of Lactobacillus plantarum by in silico optimisation

An experimental and in silico analysis of Lacticaseibacillus paracasei isolated from whey shows an association between lactate production and amino acid catabolism

The production of lactic acid from agroindustry waste products, such as whey, heavily relies on microorganisms within the genusLactobacillus. In this work, a genome-scale metabolic model was implemented from Vinay-Lara (iLca334_548), improved adding some enzymatic reactions and used to analyse metabolic fluxes ofLacticaseibacillus paracasei, which is aLactobacillusstrain isolated from whey used in the large-scale production of lactic acid. Overall, the highest rate of lactic acid productivity was 2.9 g l-1h-1, which equates to a dilution rate of 0.125 h-1, when continuous culture conditions were established. Restrictions on lactic acid production caused by exchange reactions, complex culture medium and intracellular metabolite concentrations were considered and included in the model. In total, theiLca334_548 model consisted of 1046 reactions and 959 metabolites, and flow balance analysis better predicted lactate flux than biomass. The distribution of fluxes exhibited an increase in lactate formation as biomass decreased. This finding is supported by the reactions carried out by glyceraldehyde 3-phosphate dehydrogenase, pyruvate formate lyase and ribose-5-phosphate isomerase, corroborating the modelled phenotype with experimental data. In conclusion, there is potential for the improvement of lactate production in a complex media by amino acid catabolism, especially when lactate is derived from pyruvate.

Enhancement of Ethanol Production Using a Hybrid of Firefly Algorithm and Dynamic Flux Balance Analysis

Many high-demand industrial products are generated by microorganisms, including fuels, food, vitamins, and other chemicals. Metabolic engineering is the method of circumventing cellular control to manufacture a desirable product or to create a new product that the host cells do not normally need to produce. One of the objectives of microorganism metabolic engineering is to maximise the production of a desired product. However, owing to the structure of the regulatory cellular and metabolic network, identifying specific genes to be knocked out is difficult. The development of optimization algorithms often confronts issues such as easily trapping in local maxima and handling multivariate and multimodal functions inefficiently. To predict the gene knockout list that can generate high yields of desired product, a hybrid of Firefly Algorithm and Dynamic Flux Balance Analysis (FADFBA) is proposed. This paper focuses on the ethanol production of Escherichia coli (E. coli). The findings of the experiments include gene lists, ethanol production, growth rate, and the performance of FADFBA.

Lactic Acid Bacteria Exopolysaccharides Producers: A Sustainable Tool for Functional Foods

Lactic acid bacteria (LAB) used in the food industry, mainly for the production of dairy products, are able to synthetize exopolysaccharides (EPS). EPS play a central role in the assessment of rheological and sensory characteristics of dairy products since they positively influence texture and organoleptic properties. Besides these, EPS have gained relevant interest for pharmacological and nutraceutical applications due to their biocompatibility, non-toxicity and biodegradability. These bioactive compounds may act as antioxidant, cholesterol-lowering, antimicrobial and prebiotic agents. This review provides an overview of exopolysaccharide-producing LAB, with an insight on the factors affecting EPS production, their dairy industrial applications and health benefits.

Understanding FBA Solutions under Multiple Nutrient Limitations

Genome-scale stoichiometric modeling methods, in particular Flux Balance Analysis (FBA) and variations thereof, are widely used to investigate cell metabolism and to optimize biotechnological processes. Given (1) a metabolic network, which can be reconstructed from an organism’s genome sequence, and (2) constraints on reaction rates, which may be based on measured nutrient uptake rates, FBA predicts which reactions maximize an objective flux, usually the production of cell components. Although FBA solutions may accurately predict the metabolic behavior of a cell, the actual flux predictions are often hard to interpret. This is especially the case for conditions with many constraints, such as for organisms growing in rich nutrient environments: it remains unclear why a certain solution was optimal. Here, we rationalize FBA solutions by explaining for which properties the optimal combination of metabolic strategies is selected. We provide a graphical formalism in which the selection of solutions can be visualized; we illustrate how this perspective provides a glimpse of the logic that underlies genome-scale modeling by applying our formalism to models of various sizes.

Pangenome analyses of LuxS-coding genes and enzymatic repertoires in cocoa-related lactic acid bacteria

Lactobacillaceae presents potential for interspecific Quorum Sensing (QS) in spontaneous cocoa fermentation, correlated with high abundance of luxS. Three Brazilian isolates from cocoa fermentation were characterized by Whole Genome Sequencing and luxS gene was surveyed in their genomes, in comparison with public databases. They were classified as Lactiplantibacillus plantarum, Limosilactobacillus fermentum and Pediococcus acidilactici. LuxS genes were conserved in core genomes of the novel isolates, but in some non-cocoa related Lactic Acid Bacteria (LAB) it was accessory and plasmid-borne. The conservation and horizontal acquisition of luxS reinforces that QS is determinant for bacterial adaptation in several environments, especially taking into account the luxS has been correlated with modulation of bacteriocin production, stress tolerance and biofilm formation. Therefore, in this paper, new clade and species-specific primers were designed for future application for screening of luxS gene in LAB to evaluate the adaptive potential to diverse food fermentations.

Lifestyle, metabolism and environmental adaptation in Lactococcus lactis

Lactococcus lactis serves as a paradigm organism for the lactic acid bacteria (LAB). Extensive research into the molecular biology, metabolism and physiology of several model strains of this species has been fundamental for our understanding of the LAB. Genomic studies have provided new insights into the species L. lactis, including the resolution of the genetic basis of its subspecies division, as well as the control mechanisms involved in the fine-tuning of growth rate and energy metabolism. In addition, it has enabled novel approaches to study lactococcal lifestyle adaptations to the dairy application environment, including its adjustment to near-zero growth rates that are particularly relevant in the context of cheese ripening. This review highlights various insights in these areas and exemplifies the strength of combining experimental evolution with functional genomics and bacterial physiology research to expand our fundamental understanding of the L. lactis lifestyle under different environmental conditions.

Lactic Acid Bacteria in Wine: Technological Advances and Evaluation of Their Functional Role

Currently, the main role of Lactic Acid Bacteria (LAB) in wine is to conduct the malolactic fermentation (MLF). This process can increase wine aroma and mouthfeel, improve microbial stability and reduce the acidity of wine. A growing number of studies support the appreciation that LAB can also significantly, positively and negatively, contribute to the sensorial profile of wine through many different enzymatic pathways. This is achieved either through the synthesis of compounds such as diacetyl and esters or by liberating bound aroma compounds such as glycoside-bound primary aromas and volatile thiols which are odorless in their bound form. LAB can also liberate hydroxycinnamic acids from their tartaric esters and have the potential to break down anthocyanin glucosides, thus impacting wine color. LAB can also produce enzymes with the potential to help in the winemaking process and contribute to stabilizing the final product. For example, LAB exhibit peptidolytic and proteolytic activity that could break down the proteins causing wine haze, potentially reducing the need for bentonite addition. Other potential contributions include pectinolytic activity, which could aid juice clarification and the ability to break down acetaldehyde, even when bound to SO2, reducing the need for SO2 additions during winemaking. Considering all these findings, this review summarizes the novel enzymatic activities of LAB that positively or negatively affect the quality of wine. Inoculation strategies, LAB improvement strategies, their potential to be used as targeted additions, and technological advances involving their use in wine are highlighted along with suggestions for future research.

Unlocking Elementary Conversion Modes: ecmtool Unveils All Capabilities of Metabolic Networks

The metabolic capabilities of cells determine their biotechnological potential, fitness in ecosystems, pathogenic threat levels, and function in multicellular organisms. Their comprehensive experimental characterization is generally not feasible, particularly for unculturable organisms. In principle, the full range of metabolic capabilities can be computed from an organism's annotated genome using metabolic network reconstruction. However, current computational methods cannot deal with genome-scale metabolic networks. Part of the problem is that these methods aim to enumerate all metabolic pathways, while computation of all (elementally balanced) conversions between nutrients and products would suffice. Indeed, the elementary conversion modes (ECMs, defined by Urbanczik and Wagner) capture the full metabolic capabilities of a network, but the use of ECMs has not been accessible until now. We explain and extend the theory of ECMs, implement their enumeration in ecmtool, and illustrate their applicability. This work contributes to the elucidation of the full metabolic footprint of any cell.

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Elementary conversion modes (ECMs) specify all metabolic capabilities of any organism

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Ecmtool computes all ECMs from a reconstructed metabolic network

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ECM enumeration enables metabolic characterization of larger networks than ever

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Focusing on ECMs between relevant metabolites even enables genome-scale enumeration

Understanding the metabolic capabilities of cells is of profound importance. Microbial metabolism shapes global cycles of elements and cleans polluted soils. Human and pathogen metabolism affects our health. Recent advances allow for automatic reconstruction of reaction networks for any organism, which is already used in synthetic biology, (food) microbiology, and agriculture to compute optimal yields from resources to products. However, computational tools are limited to optimal states or subnetworks, leaving many capabilities of organisms hidden. Our program, ecmtool, creates a blueprint of any organism's metabolic functionalities, drastically improving insights obtained from genome sequences. Ecmtool may become essential in exploratory research, especially for studying cells that are not culturable in laboratory conditions. Ideally, elementary conversion mode enumeration will someday be a standard step after metabolic network reconstruction, achieving the metabolic characterization of all known organisms.

An insight to flux-balance analysis for biochemical networks

Systems biology is one of the integrated ways to study biological systems and is more favourable than the earlier used approaches. It includes metabolic pathway analysis, modelling, and regulatory as well as signal transduction for getting insights into cellular behaviour. Among the various techniques of modelling, simulation, analysis of networks and pathways, flux-based analysis (FBA) has been recognised because of its extensibility as well as simplicity. It is widely accepted because it is not like a mechanistic simulation which depends on accurate kinetic data. The study of fluxes through the network is informative and can give insights even in the absence of kinetic data. FBA is one of the widely used tools to study biochemical networks and needs information of reaction stoichiometry, growth requirements, specific measurement parameters of the biological system, in particular the reconstruction of the metabolic network for the genome-scale, many of which have already been built previously. It defines the boundaries of flux distributions which are possible and achievable with a defined set of genes. This review article gives an insight into FBA, from the extension of flux balancing to mathematical representation followed by a discussion about the formulation of flux-balance analysis problems, defining constraints for the stoichiometry of the pathways and the tools that can be used in FBA such as FASIMA, COBRA toolbox, and OptFlux. It also includes broader areas in terms of applications which can be covered by FBA as well as the queries which can be addressed through FBA.

Analysis of differential gene expression and alternative splicing is significantly influenced by choice of reference genome

RNA-seq analysis has enabled the evaluation of transcriptional changes in many species including nonmodel organisms. However, in most species only a single reference genome is available and RNA-seq reads from highly divergent varieties are typically aligned to this reference. Here, we quantify the impacts of the choice of mapping genome in rice where three high-quality reference genomes are available. We aligned RNA-seq data from a popular productive rice variety to three different reference genomes and found that the identification of differentially expressed genes differed depending on which reference genome was used for mapping. Furthermore, the ability to detect differentially used transcript isoforms was profoundly affected by the choice of reference genome: Only 30% of the differentially used splicing features were detected when reads were mapped to the more commonly used, but more distantly related reference genome. This demonstrated that gene expression and splicing analysis varies considerably depending on the mapping reference genome, and that analysis of individuals that are distantly related to an available reference genome may be improved by acquisition of new genomic reference material. We observed that these differences in transcriptome analysis are, in part, due to the presence of single nucleotide polymorphisms between the sequenced individual and each respective reference genome, as well as annotation differences between the reference genomes that exist even between syntenic orthologs. We conclude that even between two closely related genomes of similar quality, using the reference genome that is most closely related to the species being sampled significantly improves transcriptome analysis.

Systems Biology - A Guide for Understanding and Developing Improved Strains of Lactic Acid Bacteria

Lactic Acid Bacteria (LAB) are extensively employed in the production of various fermented foods, due to their safe status, ability to affect texture and flavor and finally due to the beneficial effect they have on shelf-life. More recently, LAB have also gained interest as production hosts for various useful compounds, particularly compounds with sensitive applications, such as food ingredients and therapeutics. As for all industrial microorganisms, it is important to have a good understanding of the physiology and metabolism of LAB in order to fully exploit their potential, and for this purpose, many systems biology approaches are available. Systems metabolic engineering, an approach that combines optimization of metabolic enzymes/pathways at the systems level, synthetic biology as well as in silico model simulation, has been used to build microbial cell factories for production of biofuels, food ingredients and biochemicals. When developing LAB for use in foods, genetic engineering is in general not an accepted approach. An alternative is to screen mutant libraries for candidates with desirable traits using high-throughput screening technologies or to use adaptive laboratory evolution to select for mutants with special properties. In both cases, by using omics data and data-driven technologies to scrutinize these, it is possible to find the underlying cause for the desired attributes of such mutants. This review aims to describe how systems biology tools can be used for obtaining both engineered as well as non-engineered LAB with novel and desired properties.

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.

Genomic resequencing combined with quantitative proteomic analyses elucidate the survival mechanisms of Lactobacillus plantarum P-8 in a long-term glucose-limited experiment

Lactobacillus plantarum, commonly isolated from plant material, is widely used to produce various types of fermented foods. However, nutrient-limiting conditions are often encountered during industrial applications. The present study aimed to investigate the response of L. plantarum P-8 to glucose-limited conditions in a long-term experiment. Genotypic and proteomic changes in L. plantarum P-8 were monitored over 3 years in glucose-limited and glucose-normal media using whole-genome resequencing and tandem mass tag-based quantitative proteomic analysis. Results showed that L. plantarum employed numerous survival mechanisms, including alteration of the cell envelope, activation of the PTS system, accumulation and consumption of amino acids, increase in the metabolism of carbohydrates (via glycolysis, citric acid cycle, and pyruvate metabolism), and increase in the production of ATP in response to glucose starvation. This study demonstrates the feasibility of experimental evolution of L. plantarum P-8, while whole-genome resequencing of adapted isolates provided clues toward bacterial functions involved and a deeper mechanistic understanding of the adaptive response of L. plantarum to glucose-limited conditions.

SIGNIFICANCE

We have conducted a 3-year experiment monitoring genotypic and proteomic changes in Lactobacillus plantarum P-8 in glucose-limited and glucose-normal media. Whole-genome resequencing and tandem mass tag-based quantitative proteomics were performed for analyzing genomic evolution of L. plantarum P-8 in glucose-limited and glucose-normal conditions. In addition, differential expressed proteins in all generations between these two conditions were identified and functions of these proteins specific to L group were predicted. L. plantarum employed numerous survival mechanisms, including alteration of the cell envelope, activation of the PTS system, accumulation and consumption of amino acids, increase in the metabolism of carbohydrates (glycolysis, citric acid cycle, and pyruvate metabolism), and increase in the production of ATP in response to glucose starvation.

Cyanobacteria and Microalgae: Thermoeconomic Considerations in Biofuel Production

In thermodynamics, the useful work in any process can be evaluated by using the exergy quantity. The analyses of irreversibility are fundamental in the engineering design and in the productive processes’ development in order to obtain the economic growth. Recently, the use has been improved also in the thermodynamic analysis of the socio-economic context. Consequently, the exergy lost is linked to the energy cost required to maintain the productive processes themselves. The fundamental role of the fluxes and the interaction between systems and their environment is highlighted. The equivalent wasted primary resource value for the work-hour is proposed as an indicator to support the economic considerations on the biofuel production by using biomass and bacteria. The equivalent wasted primary resource value for the work-hour is proposed as an indicator to support the economic considerations of the biofuel production by using biomass and bacteria. Moreover, the technological considerations can be developed by using the exergy inefficiency. Consequently, bacteria use can be compared with other means of biofuel production, taking into account both the technologies and the economic considerations. Cyanobacteria results as the better organism for biofuel production.

Comparison of different approaches for identifying subnetworks in metabolic networks

A metabolic network model provides a computational framework for studying the metabolism of a cell at the system level. The organization of metabolic networks has been investigated in different studies. One of the organization aspects considered in these studies is the decomposition of a metabolic network. The decompositions produced by different methods are very different and there is no comprehensive evaluation framework to compare the results with each other. In this study, these methods are reviewed and compared in the first place. Then they are applied to six different metabolic network models and the results are evaluated and compared based on two existing and two newly proposed criteria. Results show that no single method can beat others in all criteria but it seems that the methods introduced by Guimera and Amaral and Verwoerd do better on among metabolite-based methods and the method introduced by Sridharan et al. does better among reaction-based ones. Also, the methods are applied to several artificial networks, each constructed from merging a few KEGG pathways. Then, their capability to recover those pathways are compared. Results show that among metabolite-based methods, the method of Guimera and Amaral does better again, however, no notable difference between the performances of reaction-based methods was detected.

Application of directed evolution to develop ethanol tolerant Oenococcus oeni for more efficient malolactic fermentation

Malolactic fermentation (MLF) is an important step in winemaking, which can be notoriously unreliable due to the fastidious nature of Oenococcus oeni. This study aimed to use directed evolution (DE) to produce a more robust strain of O. oeni having the ability to withstand high ethanol concentrations. DE involves an organism mutating and potentially adapting to a high stress environment over the course of extended cultivation. A continuous culture of O. oeni was established and exposed to progressively increasing ethanol content such that after approximately 330 generations, an isolate from this culture was able to complete MLF in high ethanol content medium earlier than its parent. The ethanol tolerance of a single isolate, A90, was tested to confirm the phenotype and its fermentation performance in wine. In order to investigate the genotypic differences in the evolved strain that led to the ethanol tolerance phenotype, the relative expression of a number of known stress response genes was compared between SB3 and A90. Notably, there was increase in hsp18 expression in 20% (v/v) ethanol by both strains with A90 exhibiting a higher degree of expression. This study is the first to use directed evolution for O. oeni strain improvement and confirms that this technique can be used successfully for the development of new candidate strains for the wine industry. This study also adds to the current knowledge on the genetic basis of ethanol tolerance in this bacterium.

Next generation microbiological risk assessment meta-omics: The next need for integration

The development of a multi-omics approach has provided a new approach to the investigation of microbial communities allowing an integration of data, which can be used to better understand the behaviour of and interactions between community members. Metagenomics, metatranscriptomics, metaproteomics and metabolomics have the potential of producing a large amount of data in a very short time, however an important challenge is how to exploit and interpret these data to assist risk managers in food safety and quality decisions. This can be achieved by integrating multi-omics data in microbiological risk assessment. In this paper we identify limitations and challenges of the multi-omics approach, underlining promising potentials, but also identifying gaps, which should be addressed for its full exploitation. A view on how this new way of investigation will impact the traditional microbiology schemes in the food industry is also presented.

Constraint-based stoichiometric modelling from single organisms to microbial communities

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

Unraveling Mg2+-RNA binding with atomistic molecular dynamics

Interaction with divalent cations is of paramount importance for RNA structural stability and function. We report here a detailed molecular dynamics study of all the possible binding sites for Mg2+ on an RNA duplex, including both direct (inner sphere) and indirect (outer sphere) binding. In order to tackle sampling issues, we develop a modified version of bias-exchange metadynamics, which allows us to simultaneously compute affinities with previously unreported statistical accuracy. Results correctly reproduce trends observed in crystallographic databases. Based on this, we simulate a carefully chosen set of models that allows us to quantify the effects of competition with monovalent cations, RNA flexibility, and RNA hybridization. Our simulations reproduce the decrease and increase of Mg2+ affinity due to ion competition and hybridization, respectively, and predict that RNA flexibility has a site-dependent effect. This suggests a nontrivial interplay between RNA conformational entropy and divalent cation binding.

Mathematical models for explaining the Warburg effect: a review focussed on ATP and biomass production

For producing ATP, tumour cells rely on glycolysis leading to lactate to about the same extent as on respiration. Thus, the ATP synthesis flux from glycolysis is considerably higher than in the corresponding healthy cells. This is known as the Warburg effect (named after German biochemist Otto H. Warburg) and also applies to striated muscle cells, activated lymphocytes, microglia, endothelial cells and several other cell types. For similar phenomena in several yeasts and many bacteria, the terms Crabtree effect and overflow metabolism respectively, are used. The Warburg effect is paradoxical at first sight because the molar ATP yield of glycolysis is much lower than that of respiration. Although a straightforward explanation is that glycolysis allows a higher ATP production rate, the question arises why cells do not re-allocate protein to the high-yield pathway of respiration. Mathematical modelling can help explain this phenomenon. Here, we review several models at various scales proposed in the literature for explaining the Warburg effect. These models support the hypothesis that glycolysis allows for a higher proliferation rate due to increased ATP production and precursor supply rates.

Integrating highly quantitative proteomics and genome-scale metabolic modeling to study pH adaptation in the human pathogen Enterococcus faecalis

Genome-scale metabolic models represent the entirety of metabolic reactions of an organism based on the annotation of the respective genome. These models commonly allow all reactions to proceed concurrently, disregarding the fact that at no point all proteins will be present in a cell. The metabolic reaction space can be constrained to a more physiological state using experimentally obtained information on enzyme abundances. However, high-quality, genome-wide protein measurements have been challenging and typically transcript abundances have been used as a surrogate for protein measurements. With recent developments in mass spectrometry-based proteomics, exemplified by SWATH-MS, the acquisition of highly quantitative proteome-wide data at reasonable throughput has come within reach. Here we present methodology to integrate such proteome-wide data into genome-scale models. We applied this methodology to study cellular changes in Enterococcus faecalis during adaptation to low pH. Our results indicate reduced proton production in the central metabolism and decreased membrane permeability for protons due to different membrane composition. We conclude that proteomic data constrain genome-scale models to a physiological state and, in return, genome-scale models are useful tools to contextualize proteomic data.

Protein backbone ensemble generation explores the local structural space of unseen natural homologs

MOTIVATION

Mutations in homologous proteins affect changes in the backbone conformation that involve a complex interplay of forces which are difficult to predict. Protein design algorithms need to anticipate these backbone changes in order to accurately calculate the energy of the structure given an amino acid sequence, without knowledge of the final, designed sequence. This is related to the problem of predicting small changes in the backbone between highly similar sequences.

RESULTS

We explored the ability of the Rosetta suite of protein design tools to move the backbone from its position in one structure (template) to its position in a close homologous structure (target) as a function of the diversity of a backbone ensemble constructed using the template structure, the percent sequence identity between the template and target, and the size of local zone being considered in the ensemble. We describe a pareto front in the likelihood of moving the backbone toward the target as a function of ensemble diversity and zone size. The equations and protocols presented here will be useful for protein design.

AVAILABILITY AND IMPLEMENTATION

PyRosetta scripts available at www.bioinfo.rpi.edu/bystrc/downloads.html#ensemble

CONTACT

bystrc@rpi.edu.

Simulating cyanobacterial phenotypes by integrating flux balance analysis, kinetics, and a light distribution function

BACKGROUND

Genome-scale models (GSMs) are widely used to predict cyanobacterial phenotypes in photobioreactors (PBRs). However, stoichiometric GSMs mainly focus on fluxome that result in maximal yields. Cyanobacterial metabolism is controlled by both intracellular enzymes and photobioreactor conditions. To connect both intracellular and extracellular information and achieve a better understanding of PBRs productivities, this study integrates a genome-scale metabolic model of Synechocystis 6803 with growth kinetics, cell movements, and a light distribution function. The hybrid platform not only maps flux dynamics in cells of sub-populations but also predicts overall production titer and rate in PBRs.

RESULTS

Analysis of the integrated GSM demonstrates several results. First, cyanobacteria are capable of reaching high biomass concentration (>20 g/L in 21 days) in PBRs without light and CO2 mass transfer limitations. Second, fluxome in a single cyanobacterium may show stochastic changes due to random cell movements in PBRs. Third, insufficient light due to cell self-shading can activate the oxidative pentose phosphate pathway in subpopulation cells. Fourth, the model indicates that the removal of glycogen synthesis pathway may not improve cyanobacterial bio-production in large-size PBRs, because glycogen can support cell growth in the dark zones. Based on experimental data, the integrated GSM estimates that Synechocystis 6803 in shake flask conditions has a photosynthesis efficiency of ~2.7 %.

CONCLUSIONS

The multiple-scale integrated GSM, which examines both intracellular and extracellular domains, can be used to predict production yield/rate/titer in large-size PBRs. More importantly, genetic engineering strategies predicted by a traditional GSM may work well only in optimal growth conditions. In contrast, the integrated GSM may reveal mutant physiologies in diverse bioreactor conditions, leading to the design of robust strains with high chances of success in industrial settings.

Systems Biology of Microbial Exopolysaccharides Production

Exopolysaccharides (EPSs) produced by diverse group of microbial systems are rapidly emerging as new and industrially important biomaterials. Due to their unique and complex chemical structures and many interesting physicochemical and rheological properties with novel functionality, the microbial EPSs find wide range of commercial applications in various fields of the economy such as food, feed, packaging, chemical, textile, cosmetics and pharmaceutical industry, agriculture, and medicine. EPSs are mainly associated with high-value applications, and they have received considerable research attention over recent decades with their biocompatibility, biodegradability, and both environmental and human compatibility. However, only a few microbial EPSs have achieved to be used commercially due to their high production costs. The emerging need to overcome economic hurdles and the increasing significance of microbial EPSs in industrial and medical biotechnology call for the elucidation of the interrelations between metabolic pathways and EPS biosynthesis mechanism in order to control and hence enhance its microbial productivity. Moreover, a better understanding of biosynthesis mechanism is a significant issue for improvement of product quality and properties and also for the design of novel strains. Therefore, a systems-based approach constitutes an important step toward understanding the interplay between metabolism and EPS biosynthesis and further enhances its metabolic performance for industrial application. In this review, primarily the microbial EPSs, their biosynthesis mechanism, and important factors for their production will be discussed. After this brief introduction, recent literature on the application of omics technologies and systems biology tools for the improvement of production yields will be critically evaluated. Special focus will be given to EPSs with high market value such as xanthan, levan, pullulan, and dextran.

Integrated microfluidic approach for quantitative high-throughput measurements of transcription factor binding affinities

Protein binding to DNA is a fundamental process in gene regulation. Methodologies such as ChIP-Seq and mapping of DNase I hypersensitive sites provide global information on this regulation in vivo In vitro methodologies provide valuable complementary information on protein-DNA specificities. However, current methods still do not measure absolute binding affinities. There is a real need for large-scale quantitative protein-DNA affinity measurements. We developed QPID, a microfluidic application for measuring protein-DNA affinities. A single run is equivalent to 4096 gel-shift experiments. Using QPID, we characterized the different affinities of ATF1, c-Jun, c-Fos and AP-1 to the CRE consensus motif and CRE half-site in two different genomic sequences on a single device. We discovered that binding of ATF1, but not of AP-1, to the CRE half-site is highly affected by its genomic context. This effect was highly correlated with ATF1 ChIP-seq and PBM experiments. Next, we characterized the affinities of ATF1 and ATF3 to 128 genomic CRE and CRE half-site sequences. Our affinity measurements explained that in vivo binding differences between ATF1 and ATF3 to CRE and CRE half-sites are partially mediated by differences in the minor groove width. We believe that QPID would become a central tool for quantitative characterization of biophysical aspects affecting protein-DNA binding.

Fine-tuning citrate synthase flux potentiates and refines metabolic innovation in the Lenski evolution experiment

Evolutionary innovations that enable organisms to colonize new ecological niches are rare compared to gradual evolutionary changes in existing traits. We discovered that key mutations in the gltA gene, which encodes citrate synthase (CS), occurred both before and after Escherichia coli gained the ability to grow aerobically on citrate (Cit(+) phenotype) during the Lenski long-term evolution experiment. The first gltA mutation, which increases CS activity by disrupting NADH-inhibition of this enzyme, is beneficial for growth on the acetate and contributed to preserving the rudimentary Cit(+) trait from extinction when it first evolved. However, after Cit(+) was refined by further mutations, this potentiating gltA mutation became deleterious to fitness. A second wave of beneficial gltA mutations then evolved that reduced CS activity to below the ancestral level. Thus, dynamic reorganization of central metabolism made colonizing this new nutrient niche contingent on both co-opting and overcoming a history of prior adaptation.

Estimating Metabolic Fluxes Using a Maximum Network Flexibility Paradigm

Motivation

Genome-scale metabolic networks can be modeled in a constraint-based fashion. Reaction stoichiometry combined with flux capacity constraints determine the space of allowable reaction rates. This space is often large and a central challenge in metabolic modeling is finding the biologically most relevant flux distributions. A widely used method is flux balance analysis (FBA), which optimizes a biologically relevant objective such as growth or ATP production. Although FBA has proven to be highly useful for predicting growth and byproduct secretion, it cannot predict the intracellular fluxes under all environmental conditions. Therefore, alternative strategies have been developed to select flux distributions that are in agreement with experimental “omics” data, or by incorporating experimental flux measurements. The latter, unfortunately can only be applied to a limited set of reactions and is currently not feasible at the genome-scale. On the other hand, it has been observed that micro-organisms favor a suboptimal growth rate, possibly in exchange for a more “flexible” metabolic network. Instead of dedicating the internal network state to an optimal growth rate in one condition, a suboptimal growth rate is used, that allows for an easier switch to other nutrient sources. A small decrease in growth rate is exchanged for a relatively large gain in metabolic capability to adapt to changing environmental conditions.

Results

Here, we propose Maximum Metabolic Flexibility (MMF) a computational method that utilizes this observation to find the most probable intracellular flux distributions. By mapping measured flux data from central metabolism to the genome-scale models of Escherichia coli and Saccharomyces cerevisiae we show that i) indeed, most of the measured fluxes agree with a high adaptability of the network, ii) this result can be used to further reduce the space of feasible solutions iii) this reduced space improves the quantitative predictions made by FBA and contains a significantly larger fraction of the measured fluxes compared to the flux space that was reduced by a uniform sampling approach and iv) MMF can be used to select reactions in the network that contribute most to the steady-state flux space. Constraining the selected reactions improves the quantitative predictions of FBA considerably more than adding an equal amount of flux constraints, selected using a more naïve approach. Our method can be applied to any cell type without requiring prior information.

Availability

MMF is freely available as a MATLAB plugin at: http://cs.ru.nl/~wmegchel/mmf.

Improving Oenococcus oeni to overcome challenges of wine malolactic fermentation

Oenococcus oeni is crucial for winemaking, bringing stabilization, deacidification, and sensory impacts through malolactic fermentation (MLF) to most wine styles. The poor nutritional make-up of wine together with typically low processing temperatures and pH and high ethanol content and sulfur dioxide (SO2) hinder O. oeni growth and activity. Production delays and interventions with starter cultures and nutritional supplements have significant cost and quality implications; thus, optimization of O. oeni has long been a priority. A range of optimization strategies, some guided by detailed characterization of O. oeni, have been exploited. Varying degrees of success have been seen with classical strain selection, mutagenesis, gene recombination, genome shuffling, and, most recently, directed evolution (DE). The merits, limitations, and future prospects of each are discussed.

Microbial bioinformatics for food safety and production

In the production of fermented foods, microbes play an important role. Optimization of fermentation processes or starter culture production traditionally was a trial-and-error approach inspired by expert knowledge of the fermentation process. Current developments in high-throughput 'omics' technologies allow developing more rational approaches to improve fermentation processes both from the food functionality as well as from the food safety perspective. Here, the authors thematically review typical bioinformatics techniques and approaches to improve various aspects of the microbial production of fermented food products and food safety.

Systems modeling approaches for microbial community studies: from metagenomics to inference of the community structure

Microbial communities play important roles in health, industrial applications and earth's ecosystems. With current molecular techniques we can characterize these systems in unprecedented detail. However, such methods provide little mechanistic insight into how the genetic properties and the dynamic couplings between individual microorganisms give rise to their dynamic activities. Neither do they give insight into what we call “the community state”, that is the fluxes and concentrations of nutrients within the community. This knowledge is a prerequisite for rational control and intervention in microbial communities. Therefore, the inference of the community structure from experimental data is a major current challenge. We will argue that this inference problem requires mathematical models that can integrate heterogeneous experimental data with existing knowledge. We propose that two types of models are needed. Firstly, mathematical models that integrate existing genomic, physiological, and physicochemical information with metagenomics data so as to maximize information content and predictive power. This can be achieved with the use of constraint-based genome-scale stoichiometric modeling of community metabolism which is ideally suited for this purpose. Next, we propose a simpler coarse-grained model, which is tailored to solve the inference problem from the experimental data. This model unambiguously relate to the more detailed genome-scale stoichiometric models which act as heterogeneous data integrators. The simpler inference models are, in our opinion, key to understanding microbial ecosystems, yet until now, have received remarkably little attention. This has led to the situation where the modeling of microbial communities, using only genome-scale models is currently more a computational, theoretical exercise than a method useful to the experimentalist.

Using a genome-scale metabolic model of Enterococcus faecalis V583 to assess amino acid uptake and its impact on central metabolism

Increasing antibiotic resistance in pathogenic bacteria necessitates the development of new medication strategies. Interfering with the metabolic network of the pathogen can provide novel drug targets but simultaneously requires a deeper and more detailed organism-specific understanding of the metabolism, which is often surprisingly sparse. In light of this, we reconstructed a genome-scale metabolic model of the pathogen Enterococcus faecalis V583. The manually curated metabolic network comprises 642 metabolites and 706 reactions. We experimentally determined metabolic profiles of E. faecalis grown in chemically defined medium in an anaerobic chemostat setup at different dilution rates and calculated the net uptake and product fluxes to constrain the model. We computed growth-associated energy and maintenance parameters and studied flux distributions through the metabolic network. Amino acid auxotrophies were identified experimentally for model validation and revealed seven essential amino acids. In addition, the important metabolic hub of glutamine/glutamate was altered by constructing a glutamine synthetase knockout mutant. The metabolic profile showed a slight shift in the fermentation pattern toward ethanol production and increased uptake rates of multiple amino acids, especially l-glutamine and l-glutamate. The model was used to understand the altered flux distributions in the mutant and provided an explanation for the experimentally observed redirection of the metabolic flux. We further highlighted the importance of gene-regulatory effects on the redirection of the metabolic fluxes upon perturbation. The genome-scale metabolic model presented here includes gene-protein-reaction associations, allowing a further use for biotechnological applications, for studying essential genes, proteins, or reactions, and the search for novel drug targets.

FCDECOMP: Decomposition of metabolic networks based on flux coupling relations

A metabolic network model provides a computational framework to study the metabolism of a cell at the system level. Due to their large sizes and complexity, rational decomposition of these networks into subsystems is a strategy to obtain better insight into the metabolic functions. Additionally, decomposing metabolic networks paves the way to use computational methods that will be otherwise very slow when run on the original genome-scale network. In the present study, we propose FCDECOMP decomposition method based on flux coupling relations (FCRs) between pairs of reaction fluxes. This approach utilizes a genetic algorithm (GA) to obtain subsystems that can be analyzed in isolation, i.e. without considering the reactions of the original network in the analysis. Therefore, we propose that our method is useful for discovering biologically meaningful modules in metabolic networks. As a case study, we show that when this method is applied to the metabolic networks of barley seeds and yeast, the modules are in good agreement with the biological compartments of these networks.

Systems-level modeling of mycobacterial metabolism for the identification of new (multi-)drug targets

Systems-level metabolic network reconstructions and the derived constraint-based (CB) mathematical models are efficient tools to explore bacterial metabolism. Approximately one-fourth of the Mycobacterium tuberculosis (Mtb) genome contains genes that encode proteins directly involved in its metabolism. These represent potential drug targets that can be systematically probed with CB models through the prediction of genes essential (or the combination thereof) for the pathogen to grow. However, gene essentiality depends on the growth conditions and, so far, no in vitro model precisely mimics the host at the different stages of mycobacterial infection, limiting model predictions. These limitations can be circumvented by combining expression data from in vivo samples with a validated CB model, creating an accurate description of pathogen metabolism in the host. To this end, we present here a thoroughly curated and extended genome-scale CB metabolic model of Mtb quantitatively validated using 13C measurements. We describe some of the efforts made in integrating CB models and high-throughput data to generate condition specific models, and we will discuss challenges ahead. This knowledge and the framework herein presented will enable to identify potential new drug targets, and will foster the development of optimal therapeutic strategies.

Implications of new research and technologies for malolactic fermentation in wine

The initial conversion of grape must to wine is an alcoholic fermentation (AF) largely carried out by one or more strains of yeast, typically Saccharomyces cerevisiae. After the AF, a secondary or malolactic fermentation (MLF) which is carried out by lactic acid bacteria (LAB) is often undertaken. The MLF involves the bioconversion of malic acid to lactic acid and carbon dioxide. The ability to metabolise L-malic acid is strain specific, and both individual Oenococcus oeni strains and other LAB strains vary in their ability to efficiently carry out MLF. Aside from impacts on acidity, LAB can also metabolise other precursors present in wine during fermentation and, therefore, alter the chemical composition of the wine resulting in an increased complexity of wine aroma and flavour. Recent research has focused on three main areas: enzymatic changes during MLF, safety of the final product and mechanisms of stress resistance. This review summarises the latest research and technological advances in the rapidly evolving study of MLF and investigates the directions that future research may take.

Biotechnological production of value-added carotenoids from microalgae: Emerging technology and prospects

We recently evaluated the relationship between abiotic environmental stresses and lutein biosynthesis in the green microalga Dunaliella salina and suggested a rational design of stress-driven adaptive evolution experiments for carotenoids production in microalgae. Here, we summarize our recent findings regarding the biotechnological production of carotenoids from microalgae and outline emerging technology in this field. Carotenoid metabolic pathways are characterized in several representative algal species as they pave the way for biotechnology development. The adaptive evolution strategy is highlighted in connection with enhanced growth rate and carotenoid metabolism. In addition, available genetic modification tools are described, with emphasis on model species. A brief discussion on the role of lights as limiting factors in carotenoid production in microalgae is also included. Overall, our analysis suggests that light-driven metabolism and the photosynthetic efficiency of microalgae in photobioreactors are the main bottlenecks in enhancing biotechnological potential of carotenoid production from microalgae.

Genome-scale metabolic reconstructions of Bifidobacterium adolescentis L2-32 and Faecalibacterium prausnitzii A2-165 and their interaction

Background

The gut microbiota plays an important role in human health and disease by acting as a metabolic organ. Metagenomic sequencing has shown how dysbiosis in the gut microbiota is associated with human metabolic diseases such as obesity and diabetes. Modeling may assist to gain insight into the metabolic implication of an altered microbiota. Fast and accurate reconstruction of metabolic models for members of the gut microbiota, as well as methods to simulate a community of microorganisms, are therefore needed. The Integrated Microbial Genomes (IMG) database contains functional annotation for nearly 4,650 bacterial genomes. This tremendous new genomic information adds new opportunities for systems biology to reconstruct accurate genome scale metabolic models (GEMs).

Results

Here we assembled a reaction data set containing 2,340 reactions obtained from existing genome-scale metabolic models, where each reaction is assigned with KEGG Orthology. The reaction data set was then used to reconstruct two genome scale metabolic models for gut microorganisms available in the IMG database Bifidobacterium adolescentis L2-32, which produces acetate during fermentation, and Faecalibacterium prausnitzii A2-165, which consumes acetate and produces butyrate. F. prausnitzii is less abundant in patients with Crohn’s disease and has been suggested to play an anti-inflammatory role in the gut ecosystem. The B. adolescentis model, iBif452, comprises 699 reactions and 611 unique metabolites. The F. prausnitzii model, iFap484, comprises 713 reactions and 621 unique metabolites. Each model was validated with in vivo data. We used OptCom and Flux Balance Analysis to simulate how both organisms interact.

Conclusions

The consortium of iBif452 and iFap484 was applied to predict F. prausnitzii’s demand for acetate and production of butyrate which plays an essential role in colonic homeostasis and cancer prevention. The assembled reaction set is a useful tool to generate bacterial draft models from KEGG Orthology.

Metabolic responses of Lactobacillus plantarum strains during fermentation and storage of vegetable and fruit juices

Strains of Lactobacillus plantarum were grown and stored in cherry (ChJ), pineapple (PJ), carrot (CJ), and tomato (TJ) juices to mimic the chemical composition of the respective matrices. Wheat flour hydrolysate (WFH), whey milk (W), and MRS broth were also used as representatives of other ecosystems. The growth rates and cell densities of L. plantarum strains during fermentation (24 h at 30°C) and storage (21 days at 4°C) differed only in part, being mainly influenced by the matrix. ChJ and PJ were the most stressful juices for growth and survival. Overall, the growth in juices was negatively correlated with the initial concentration of malic acid and carbohydrates. The consumption of malic acid was noticeable for all juices, but mainly during fermentation and storage of ChJ. Decreases of branched-chain amino acids (BCAA)-with the concomitant increase of their respective branched alcohols-and His and increases of Glu and gamma-aminobutyric acid (GABA) were the main traits of the catabolism of free amino acids (FAA), which were mainly evident under less acidic conditions (CJ and TJ). The increase of Tyr was found only during storage of ChJ. Some aldehydes (e.g., 3-methyl-butanal) were reduced to the corresponding alcohols (e.g., 3-methyl-1-butanol). After both fermentation and storage, acetic acid increased in all fermented juices, which implied the activation of the acetate kinase route. Diacetyl was the ketone found at the highest level, and butyric acid increased in almost all fermented juices. Data were processed through multidimensional statistical analyses. Except for CJ, the juices (mainly ChJ) seemed to induce specific metabolic traits, which differed in part among the strains. This study provided more in-depth knowledge on the metabolic mechanisms of growth and maintenance of L. plantarum in vegetable and fruit habitats, which also provided helpful information to select the most suitable starters for fermentation of targeted matrices.

Flux modules in metabolic networks

The huge number of elementary flux modes in genome-scale metabolic networks makes analysis based on elementary flux modes intrinsically difficult. However, it has been shown that the elementary flux modes with optimal yield often contain highly redundant information. The set of optimal-yield elementary flux modes can be compressed using modules. Up to now, this compression was only possible by first enumerating the whole set of all optimal-yield elementary flux modes. We present a direct method for computing modules of the thermodynamically constrained optimal flux space of a metabolic network. This method can be used to decompose the set of optimal-yield elementary flux modes in a modular way and to speed up their computation. In addition, it provides a new form of coupling information that is not obtained by classical flux coupling analysis. We illustrate our approach on a set of model organisms.

Core fluxome and metafluxome of lactic acid bacteria under simulated cocoa pulp fermentation conditions

In the present work, simulated cocoa fermentation was investigated at the level of metabolic pathway fluxes (fluxome) of lactic acid bacteria (LAB), which are typically found in the microbial consortium known to convert nutrients from the cocoa pulp into organic acids. A comprehensive (13)C labeling approach allowed to quantify carbon fluxes during simulated cocoa fermentation by (i) parallel (13)C studies with [(13)C6]glucose, [1,2-(13)C2]glucose, and [(13)C6]fructose, respectively, (ii) gas chromatography-mass spectrometry (GC/MS) analysis of secreted acetate and lactate, (iii) stoichiometric profiling, and (iv) isotopomer modeling for flux calculation. The study of several strains of L. fermentum and L. plantarum revealed major differences in their fluxes. The L. fermentum strains channeled only a small amount (4 to 6%) of fructose into central metabolism, i.e., the phosphoketolase pathway, whereas only L. fermentum NCC 575 used fructose to form mannitol. In contrast, L. plantarum strains exhibited a high glycolytic flux. All strains differed in acetate flux, which originated from fractions of citrate (25 to 80%) and corresponding amounts of glucose and fructose. Subsequent, metafluxome studies with consortia of different L. fermentum and L. plantarum strains indicated a dominant (96%) contribution of L. fermentum NCC 575 to the overall flux in the microbial community, a scenario that was not observed for the other strains. This highlights the idea that individual LAB strains vary in their metabolic contribution to the overall fermentation process and opens up new routes toward streamlined starter cultures. L. fermentum NCC 575 might be one candidate due to its superior performance in flux activity.

An optimization rule for in silico identification of targeted overproduction in metabolic pathways

In an extension of previous work, here we introduce a second-order optimization method for determining optimal paths from the substrate to a target product of a metabolic network, through which the amount of the target is maximum. An objective function for the said purpose, along with certain linear constraints, is considered and minimized. The basis vectors spanning the null space of the stoichiometric matrix, depicting the metabolic network, are computed, and their convex combinations satisfying the constraints are considered as flux vectors. A set of other constraints, incorporating weighting coefficients corresponding to the enzymes in the pathway, are considered. These weighting coefficients appear in the objective function to be minimized. During minimization, the values of these weighting coefficients are estimated and learned. These values, on minimization, represent an optimal pathway, depicting optimal enzyme concentrations, leading to overproduction of the target. The results on various networks demonstrate the usefulness of the methodology in the domain of metabolic engineering. A comparison with the standard gradient descent and the extreme pathway analysis technique is also performed. Unlike the gradient descent method, the present method, being independent of the learning parameter, exhibits improved results.

The ability of flux balance analysis to predict evolution of central metabolism scales with the initial distance to the optimum

The most powerful genome-scale framework to model metabolism, flux balance analysis (FBA), is an evolutionary optimality model. It hypothesizes selection upon a proposed optimality criterion in order to predict the set of internal fluxes that would maximize fitness. Here we present a direct test of the optimality assumption underlying FBA by comparing the central metabolic fluxes predicted by multiple criteria to changes measurable by a ¹³C-labeling method for experimentally-evolved strains. We considered datasets for three Escherichia coli evolution experiments that varied in their length, consistency of environment, and initial optimality. For ten populations that were evolved for 50,000 generations in glucose minimal medium, we observed modest changes in relative fluxes that led to small, but significant decreases in optimality and increased the distance to the predicted optimal flux distribution. In contrast, seven populations evolved on the poor substrate lactate for 900 generations collectively became more optimal and had flux distributions that moved toward predictions. For three pairs of central metabolic knockouts evolved on glucose for 600–800 generations, there was a balance between cases where optimality and flux patterns moved toward or away from FBA predictions. Despite this variation in predictability of changes in central metabolism, two generalities emerged. First, improved growth largely derived from evolved increases in the rate of substrate use. Second, FBA predictions bore out well for the two experiments initiated with ancestors with relatively sub-optimal yield, whereas those begun already quite optimal tended to move somewhat away from predictions. These findings suggest that the tradeoff between rate and yield is surprisingly modest. The observed positive correlation between rate and yield when adaptation initiated further from the optimum resulted in the ability of FBA to use stoichiometric constraints to predict the evolution of metabolism despite selection for rate.

The most common method of modeling genome-scale metabolism, flux balance analysis, involves using known stoichiometry to define feasible metabolic states and then choosing between these states by proposing that evolution has selected a metabolic flux that optimizes fitness. But does evolution optimize metabolism, and if so, what component of metabolism equates to fitness? We directly tested the underlying assumption of stoichiometric optimality by comparing predicted flux distributions with changes in fluxes that occurred following experimental evolution. Across three experiments ranging in length from a few hundred to fifty thousand generations, we found that substrate uptake – an input to the model – always increased, but supposed optimality criteria such as yield only increased sometimes. Despite this, there was a clear trend. Highly optimal ancestors evolved slightly lower yield in the course of increasing the overall rate, whereas more sub-optimal strains were able to increase both. These results suggest that flux balance analysis is capable of predicting either the initial metabolic behavior of strains or how they will evolve, but not both.

Responses to light intensity in a genome-scale model of rice metabolism

We describe the construction and analysis of a genome-scale metabolic model representing a developing leaf cell of rice (Oryza sativa) primarily derived from the annotations in the RiceCyc database. We used flux balance analysis to determine that the model represents a network capable of producing biomass precursors (amino acids, nucleotides, lipid, starch, cellulose, and lignin) in experimentally reported proportions, using carbon dioxide as the sole carbon source. We then repeated the analysis over a range of photon flux values to examine responses in the solutions. The resulting flux distributions show that (1) redox shuttles between the chloroplast, cytosol, and mitochondrion may play a significant role at low light levels, (2) photorespiration can act to dissipate excess energy at high light levels, and (3) the role of mitochondrial metabolism is likely to vary considerably according to the balance between energy demand and availability. It is notable that these organelle interactions, consistent with many experimental observations, arise solely as a result of the need for mass and energy balancing without any explicit assumptions concerning kinetic or other regulatory mechanisms.