The dynamics of the macromolecular composition of biomass

Detailed analysis of metabolism reveals growth-rate-promoting interactions between Anaerostipes caccae and Bacteroides spp

INTRODUCTION

Human gut microbiota species which are next-generation probiotics (NGPs) candidates are of high interest as they have shown the potential to treat intestinal inflammation and other diseases. Unfortunately, these species are often not robust enough for large-scale cultivation, especially in maintaining diversity in co-culture production.

OBJECTIVES

In this study, we describe interactions between human gut microbiota species in the cultivation process with unique substrates. We also demonstrated that it is possible to change the species ratio in co-culture by changing the ratio of carbon sources.

METHODS

We screened 25 different bacterial species based on their metabolic capabilities. After evaluating unique substrate possibilities, we chose Anaerostipes caccae (A. caccae), Bacteroides thetaiotaomicron (B. thetaiotaomicron), and Bacteroides vulgatus (B. vulgatus) as subjects for further study. D-sorbitol, D-xylose, and D-galacturonic acid were selected as substrates for A. caccae, B. thetaiotaomicron, and B. vulgatus respectively. All three species were cultivated as both monocultures and in co-cultures in serial batch fermentations in an isothermal microcalorimeter.

RESULTS

Positive interactions were detected between the species in both co-cultures (A. caccae + B. thetaiotaomicron; A. caccae + B. vulgatus) resulting in higher heat production compared to the sum of the monocultures. The same positive cross-feeding interactions took place in larger-scale cultivation experiments. We confirmed acetate and lactate cross-feeding between A. caccae and B. thetaiotaomicron with flux balance analysis (FBA).

CONCLUSION

Changing the ratio of the selected carbon sources in the medium changed the species ratio accordingly. Such robustness is the basis for developing more efficient industrial co-culture processes including the production of NGPs.

An optimal growth law for RNA composition and its partial implementation through ribosomal and tRNA gene locations in bacterial genomes

The distribution of cellular resources across bacterial proteins has been quantified through phenomenological growth laws. Here, we describe a complementary bacterial growth law for RNA composition, emerging from optimal cellular resource allocation into ribosomes and ternary complexes. The predicted decline of the tRNA/rRNA ratio with growth rate agrees quantitatively with experimental data. Its regulation appears to be implemented in part through chromosomal localization, as rRNA genes are typically closer to the origin of replication than tRNA genes and thus have increasingly higher gene dosage at faster growth. At the highest growth rates in E. coli, the tRNA/rRNA gene dosage ratio based on chromosomal positions is almost identical to the observed and theoretically optimal tRNA/rRNA expression ratio, indicating that the chromosomal arrangement has evolved to favor maximal transcription of both types of genes at this condition.

Unlike the proteome composition, RNA composition is often assumed to be independent of growth rate in bacteria, despite experimental evidence for a growth rate dependence in many microbes. In this work, we derived a growth-rate dependent optimal tRNA/rRNA concentration ratio by minimizing the combined costs of ribosome and ternary complex at the required protein production rate. The predicted optimal tRNA/rRNA expression ratio, which is a monotonically decreasing function of growth rate, agrees with experimental data for E. coli and other fast-growing microbes. This indicates the existing of an RNA composition growth law. Due to the presence of partially replicated chromosomes, gene dosage is higher for those genes whose DNA is replicated earlier, an effect that becomes stronger at higher growth rates. Because rRNA genes are located closer to origin of replication than tRNA genes in fast-growing species, the tRNA/rRNA gene dosage ratio scales with growth rate in the same direction as the optimal tRNA/rRNA expression ratio. Thus, it appears that the RNA growth law is–at least in part–implemented simply through the genomic positions of tRNA and rRNA genes. This finding indicates that growth rate-dependent optimal resource allocation can influence the genomic organization in bacteria.

Modeling Host-Pathogen Interaction to Elucidate the Metabolic Drug Response of Intracellular Mycobacterium tuberculosis

Little is known about the metabolic state of Mycobacterium tuberculosis (Mtb) inside the phagosome, a compartment inside phagocytes for killing pathogens and other foreign substances. We have developed a combined model of Mtb and human metabolism, sMtb-RECON and used this model to predict the metabolic state of Mtb during infection of the host. Amino acids are predicted to be used for energy production as well as biomass formation. Subsequently we assessed the effect of increasing dosages of drugs targeting metabolism on the metabolic state of the pathogen and predict resulting metabolic adaptations and flux rerouting through various pathways. In particular, the TCA cycle becomes more important upon drug application, as well as alanine, aspartate, glutamate, proline, arginine and porphyrin metabolism, while glycine, serine, and threonine metabolism become less important. We modeled the effect of 11 metabolically active drugs. Notably, the effect of eight could be recreated and two major profiles of the metabolic state were predicted. The profiles of the metabolic states of Mtb affected by the drugs BTZ043, cycloserine and its derivative terizidone, ethambutol, ethionamide, propionamide, and isoniazid were very similar, while TMC207 is predicted to have quite a different effect on metabolism as it inhibits ATP synthase and therefore indirectly interferes with a multitude of metabolic pathways.

Prediction of metabolic fluxes from gene expression data with Huber penalty convex optimization function

As one of the critical parameters of a metabolic pathway, the metabolic flux in a metabolic network serves as an essential role in physiology and pathology. Constraint-based metabolic models are the widely used frameworks for predicting metabolic fluxes in genome-scale metabolic networks. Integrating the transcriptomic data into the constraint-based metabolic models can effectively predict context-specific fluxes across different conditions. However, these methods always need user-defined thresholds to identify the expression levels of metabolic genes or restrain the rate of biomass production, and the predictive results are sensitive to the thresholds. In this work, we present the Huber penalty convex optimization function (HPCOF) combined with the flux minimization principle to predict metabolic fluxes. Our HPCOF method integrates gene expression profiles into the genome-scale metabolic models (GEMs) to reduce the sensitivity to outliers, and uses continuous expression data to avoid selection of arbitrary threshold parameters. In the case studies of Saccharomyces cerevisiae (S. cerevisiae) and Escherichia coli (E. coli) strains under different conditions, the results show that our HPCOF method has a better fit to the experimentally measured values, and has a higher Pearson correlation coefficient, a smaller P-value and a lower sum of squared error than other methods. The HPCOF code can be freely downloaded from for academic users.

Fine-tuning the P. pastoris iMT1026 genome-scale metabolic model for improved prediction of growth on methanol or glycerol as sole carbon sources

The methylotrophic yeast Pichia pastoris (Komagataella spp.) is widely used as cell factory for recombinant protein production. In the past recent years, important breakthroughs in the systems-level quantitative analysis of its physiology have been achieved. This wealth of information has allowed the development of genome-scale metabolic models, which make new approaches possible for host cell and bioprocess engineering. Nevertheless, the predictive accuracy of the previous consensus model required to be upgraded and validated with new experimental data sets for P. pastoris growing on glycerol or methanol as sole carbon sources, two of the most relevant substrates for this cell factory. In this study, we have characterized P. pastoris growing in chemostat cultures using glycerol or methanol as sole carbon sources over a wide range of growth rates, thereby providing physiological data on the effect of growth rate and culture conditions on biomass macromolecular and elemental composition. In addition, these data sets were used to improve the performance of the P. pastoris consensus genomic-scale metabolic model iMT1026. Thereupon, new experimentally determined bounds, including the representation of biomass composition for these growth conditions, have been incorporated. As a result, here, we present version 3 (v3.0) of the consensus P. pastoris genome-scale metabolic model as an update of the iMT1026 model. The v3.0 model was validated for growth on glycerol and methanol as sole carbon sources, demonstrating improved prediction capabilities over an extended substrate range including two biotechnologically relevant carbon sources.

The implications of reduced metabolic rate in a resource-limited coral

Many organisms exhibit depressed metabolism when resources are limited, a change that makes it possible to balance an energy budget. For symbiotic reef corals, daily cycles of light and periods of intense cloud cover can be chronic causes of food limitation through reduced photosynthesis. Furthermore, coral bleaching is common in present day reefs, creating a context in which metabolic depression could have beneficial value to corals. In the present study, corals (massive Porites) were exposed to an extreme case of resource limitation by starving them of food and light for 20 d. When resources were limited, the corals depressed area-normalized respiration to 37% of initial rates, coral biomass declined to 64% of initial amounts, yet the corals continued to produce skeletal mass. However, the declines in biomass cannot account for the declines in area-normalized respiration, as mass-specific respiration declined to 30% of initial rates. Thus, these corals appear to be capable of metabolic depression. It is possible that some coral species are better able to depress metabolic rates, such variation could explain differential survival during conditions that limit resources (e.g., shading). Furthermore, we found that maintenance of existing biomass, in part, supports the production of skeletal mass. This association could be explained if maintenance supplies needed energy (e.g., ATP) or inorganic carbon (i.e., CO2) that otherwise limits the production of skeletal mass. Finally, the observed metabolic depression can be explained as change in pool sizes, and does not require a change in metabolic rules.

Dynamic energy budget modeling reveals the potential of future growth and calcification for the coccolithophore Emiliania huxleyi in an acidified ocean

Ocean acidification is likely to impact the calcification potential of marine organisms. In part due to the covarying nature of the ocean carbonate system components, including pH and CO2 and CO3(2-) levels, it remains largely unclear how each of these components may affect calcification rates quantitatively. We develop a process-based bioenergetic model that explains how several components of the ocean carbonate system collectively affect growth and calcification rates in Emiliania huxleyi, which plays a major role in marine primary production and biogeochemical carbon cycling. The model predicts that under the IPCC A2 emission scenario, its growth and calcification potential will have decreased by the end of the century, although those reductions are relatively modest. We anticipate that our model will be relevant for many other marine calcifying organisms, and that it can be used to improve our understanding of the impact of climate change on marine systems.

Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism

Constraint-based models of metabolism are a widely used framework for predicting flux distributions in genome-scale biochemical networks. The number of published methods for integration of transcriptomic data into constraint-based models has been rapidly increasing. So far the predictive capability of these methods has not been critically evaluated and compared. This work presents a survey of recently published methods that use transcript levels to try to improve metabolic flux predictions either by generating flux distributions or by creating context-specific models. A subset of these methods is then systematically evaluated using published data from three different case studies in E. coli and S. cerevisiae. The flux predictions made by different methods using transcriptomic data are compared against experimentally determined extracellular and intracellular fluxes (from 13C-labeling data). The sensitivity of the results to method-specific parameters is also evaluated, as well as their robustness to noise in the data. The results show that none of the methods outperforms the others for all cases. Also, it is observed that for many conditions, the predictions obtained by simple flux balance analysis using growth maximization and parsimony criteria are as good or better than those obtained using methods that incorporate transcriptomic data. We further discuss the differences in the mathematical formulation of the methods, and their relation to the results we have obtained, as well as the connection to the underlying biological principles of metabolic regulation.

Reconciling in vivo and in silico key biological parameters of Pseudomonas putida KT2440 during growth on glucose under carbon-limited condition

Background

Genome scale metabolic reconstructions are developed to efficiently engineer biocatalysts and bioprocesses based on a rational approach. However, in most reconstructions, due to the lack of appropriate measurements, experimentally determined growth parameters are simply taken from literature including other organisms, which reduces the usefulness and suitability of these models. Pseudomonas putida KT2440 is an outstanding biocatalyst given its versatile metabolism, its ability to generate sufficient energy and turnover of NADH and NAD. To apply this strain optimally in industrial production, a previously developed genome-scale metabolic model (iJP815) was experimentally assessed and streamlined to enable accurate predictions of the outcome of metabolic engineering approaches.

Results

To substantially improve the accuracy of the genome scale model (iJP815), continuous bioreactor cultures on a mineral medium with glucose as a sole carbon source were carried out at different dilution rates, which covered pulling analysis of the macromolecular composition of the biomass. Besides, the maximum biomass yield (on substrate) of 0.397 g_DCW · g_glc^-1, the maintenance coefficient of 0.037 g_glc · g_DCW^-1 · h^-1 and the maximum specific growth rate of 0.59 h^-1 were determined. Only the DNA fraction increased with the specific growth rate. This resulted in reliable estimation for the Growth-Associated Maintenance (GAM) of 85 mmol_ATP · g_DCW^-1 and the Non Growth-Associated Maintenance (NGAM) of 3.96 mmol_ATP · g_DCW^-1 · h^-1. Both values were found significantly different from previous assignment as a consequence of a lower yield and higher maintenance coefficient than originally assumed. Contrasting already published ¹³C flux measurements and the improved model allowed for constraining the solution space, by eliminating futile cycles. Furthermore, the model predictions were compared with transcriptomic data at overall good consistency, which helped to identify missing links.

Conclusions

By careful interpretation of growth stoichiometry and kinetics when grown in the presence of glucose, this work reports on an accurate genome scale metabolic model of Pseudomonas putida, providing a solid basis for its use in designing superior strains for biocatalysis. By consideration of substrate specific variation in stoichiometry and kinetics, it can be extended to other substrates and new mutants.

Adaptation of Phaeobacter inhibens DSM 17395 to growth with complex nutrients

Phaeobacter inhibens DSM 17395, a member of the Roseobacter clade, was studied for its adaptive strategies to complex and excess nutrient supply, here mimicked by cultivation with Marine Broth (MB). During growth in process-controlled fermenters, P. inhibens DSM 17395 grew faster (3.6-fold higher μmax ) and reached higher optical densities (2.2-fold) with MB medium, as compared to the reference condition of glucose-containing mineral medium. Apparently, in the presence of MB medium, metabolism was tuned to maximize growth rate at the expense of efficiency. Comprehensive proteomic analysis of cells harvested at ½ ODmax identified 1783 (2D DIGE, membrane and extracellular protein-enriched fractions, shotgun) different proteins (50.5% coverage), 315 (based on 2D DIGE) of which displayed differential abundance profiles. Moreover, 145 different metabolites (intra- and extracellular combined) were identified, almost all of which (140) showed abundance changes. During growth with MB medium, P. inhibens DSM 17395 specifically formed the various proteins required for utilization of phospholipids and several amino acids, as well as for gluconeogenesis. Metabolic tuning on amino acid utilization is also reflected by massive discharge of urea to dispose the cell of excess ammonia. Apparently, P. inhibens DSM 17395 modulated its metabolism to simultaneously utilize diverse substrates from the complex nutrient supply.

Predicting population dynamics from the properties of individuals: a cross-level test of dynamic energy budget theory

Individual-based models (IBMs) are increasingly used to link the dynamics of individuals to higher levels of biological organization. Still, many IBMs are data hungry, species specific, and time-consuming to develop and analyze. Many of these issues would be resolved by using general theories of individual dynamics as the basis for IBMs. While such theories have frequently been examined at the individual level, few cross-level tests exist that also try to predict population dynamics. Here we performed a cross-level test of dynamic energy budget (DEB) theory by parameterizing an individual-based model using individual-level data of the water flea, Daphnia magna, and comparing the emerging population dynamics to independent data from population experiments. We found that DEB theory successfully predicted population growth rates and peak densities but failed to capture the decline phase. Further assumptions on food-dependent mortality of juveniles were needed to capture the population dynamics after the initial population peak. The resulting model then predicted, without further calibration, characteristic switches between small- and large-amplitude cycles, which have been observed for Daphnia. We conclude that cross-level tests help detect gaps in current individual-level theories and ultimately will lead to theory development and the establishment of a generic basis for individual-based models and ecology.

Dynamic energy budget approach to modeling mechanisms of CdSe quantum dot toxicity

A mechanistic model of bacterial growth based on dynamic energy budget (DEB) theory is utilized to investigate mechanisms of toxicity of CdSe quantum dots (QDs). The model of QD toxicity is developed by extending a previously published DEB model of cadmium ion toxicity to include a separate model of QD toxic action. The extension allows for testing whether toxicity from QD exposure can be explained fully by dissolved cadmium exposure only, or if the separate effects of QDs need to be taken into account as well. Two major classes of QD toxicity mechanisms are considered: acclimation expressed through initial retardation of growth, and three separate metabolic effects that can be a result of QDs either reversibly or irreversibly associating with the cell. The model is consistent with the data, and is able to distinguish toxic effects due to QD nano-particles from the effects due to cadmium ions. Results suggest that, in contrast to ionic exposure where required acclimation remains constant as exposure increases, increase of the energy required for acclimation with exposure is the primary toxic effect of QDs. Reactive oxygen species measurements help conclude that increase in energetic cost of maintenance processes such as cellular repair and maintenance of cross-membrane gradients is the most important of the three metabolic effects of QD toxicity.

Modeling phenotypic metabolic adaptations of Mycobacterium tuberculosis H37Rv under hypoxia

The ability to adapt to different conditions is key for Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), to successfully infect human hosts. Adaptations allow the organism to evade the host immune responses during acute infections and persist for an extended period of time during the latent infectious stage. In latently infected individuals, estimated to include one-third of the human population, the organism exists in a variety of metabolic states, which impedes the development of a simple strategy for controlling or eradicating this disease. Direct knowledge of the metabolic states of M. tuberculosis in patients would aid in the management of the disease as well as in forming the basis for developing new drugs and designing more efficacious drug cocktails. Here, we propose an in silico approach to create state-specific models based on readily available gene expression data. The coupling of differential gene expression data with a metabolic network model allowed us to characterize the metabolic adaptations of M. tuberculosis H37Rv to hypoxia. Given the microarray data for the alterations in gene expression, our model predicted reduced oxygen uptake, ATP production changes, and a global change from an oxidative to a reductive tricarboxylic acid (TCA) program. Alterations in the biomass composition indicated an increase in the cell wall metabolites required for cell-wall growth, as well as heightened accumulation of triacylglycerol in preparation for a low-nutrient, low metabolic activity life style. In contrast, the gene expression program in the deletion mutant of dosR, which encodes the immediate hypoxic response regulator, failed to adapt to low-oxygen stress. Our predictions were compatible with recent experimental observations of M. tuberculosis activity under hypoxic and anaerobic conditions. Importantly, alterations in the flow and accumulation of a particular metabolite were not necessarily directly linked to differential gene expression of the enzymes catalyzing the related metabolic reactions.

Mycobacterium tuberculosis latently infects one-third of the human population and is responsible for millions of deaths worldwide every year. The ability of the pathogen to persist in the human population stems from its capacity to adapt to host-induced stresses and adjust its metabolism to different host environments. We have developed a novel model to interpret M. tuberculosis H37Rv metabolic adjustment by combining gene transcription data with a genome-scale metabolic network model. Using our model, we were able to identify the changes in the metabolic program associated with hypoxia, predict phenotypic change, and determine the critical metabolic enzymes and pathways that are required for pathogen survival. In particular, we predicted the switch in the tricarboxylic acid cycle from an oxidative to a reductive path. The altered importance of different metabolites and pathways under hypoxic conditions may provide guidance for designing novel, adjuvant drug therapies for clearing persistent and latent infections.

Modeling physiological processes that relate toxicant exposure and bacterial population dynamics

Quantifying effects of toxicant exposure on metabolic processes is crucial to predicting microbial growth patterns in different environments. Mechanistic models, such as those based on Dynamic Energy Budget (DEB) theory, can link physiological processes to microbial growth.Here we expand the DEB framework to include explicit consideration of the role of reactive oxygen species (ROS). Extensions considered are: (i) additional terms in the equation for the "hazard rate" that quantifies mortality risk; (ii) a variable representing environmental degradation; (iii) a mechanistic description of toxic effects linked to increase in ROS production and aging acceleration, and to non-competitive inhibition of transport channels; (iv) a new representation of the "lag time" based on energy required for acclimation. We estimate model parameters using calibrated Pseudomonas aeruginosa optical density growth data for seven levels of cadmium exposure. The model reproduces growth patterns for all treatments with a single common parameter set, and bacterial growth for treatments of up to 150 mg(Cd)/L can be predicted reasonably well using parameters estimated from cadmium treatments of 20 mg(Cd)/L and lower. Our approach is an important step towards connecting levels of biological organization in ecotoxicology. The presented model reveals possible connections between processes that are not obvious from purely empirical considerations, enables validation and hypothesis testing by creating testable predictions, and identifies research required to further develop the theory.

Impacts of metal oxide nanoparticles on marine phytoplankton

Information on the toxicity of environmentally relevant concentrations of nanoparticles in marine ecosystems is needed for informed regulation of these emerging materials. We tested the effects of two types of metal oxide nanoparticles, TiO(2) and ZnO, on population growth rates of four species of marine phytoplankton representing three major coastal groups (diatoms, chlorophytes, and prymnesiophytes). These metal oxide nanoparticles (NPs) are becoming common components in many industrial, household, and cosmetic products that are released into coastal ecosystems. Titania NPs showed no measurable effect on growth rates of any species, while ZnO NPs significantly depressed growth rate of all four species. ZnO NPs aggregated rapidly in seawater, forming particles >400 nm hydrodynamic diameter within 30 min, and dissolved quickly, reaching equilibrium concentrations within 12 h. Toxicity of ZnO NPs to phytoplankton was likely due to dissolution, release, and uptake of free zinc ions, but specific nanoparticulate effects may be difficult to disentangle from effects due to free zinc ions. A modeling approach based on a Dynamic Energy Budget (DEB) framework was used to estimate sublethal effects of the two NPs on phytoplankton populations. Concentrations that were estimated to have no effect on population growth (NEC) were (one standard error in parentheses) 428 (58) μg L(-1) ZnO for the diatom Skeletonema marinoi and 223 (56) μg L(-1) for Thalassiosira pseudonana. NEC could not be estimated for the other taxa but were within the range of 500-1000 μg L(-1). Our results suggest that effects of metal oxide NPs on marine organisms is likely to vary with particle type and organism taxonomy.

Dynamic-energy-budget-driven fruiting-body formation in myxobacteria

We develop an interacting particle model to simulate the life cycle of myxobacteria, which consists of two main stages--the swarming stage and the development (fruiting body formation) stage. As experiments have shown that the phase transition from swarming to development stage is triggered by starvation, we incorporate into the simulation a system of ordinary differential equations (ODEs) called the dynamic energy budget, which controls the uptake and use of energy by individuals. This inclusion successfully automates the phase transition in our simulation. Only one parameter, namely, the food density, controls the entire simulation of the life cycle.

Sublethal toxicant effects with dynamic energy budget theory: model formulation

We develop and test a general modeling framework to describe the sublethal effects of pollutants by adding toxicity modules to an established dynamic energy budget (DEB) model. The DEB model describes the rates of energy acquisition and expenditure by individual organisms; the toxicity modules describe how toxicants affect these rates by changing the value of one or more DEB parameters, notably the parameters quantifying the rates of feeding and maintenance. We investigate four toxicity modules that assume: (1) effects on feeding only; (2) effects on maintenance only; (3) effects on feeding and maintenance with similar values for the toxicity parameters; and (4) effects on feeding and maintenance with different values for the toxicity parameters. We test the toxicity modules by fitting each to published data on feeding, respiration, growth and reproduction. Among the pollutants tested are metals (mercury and copper) and various organic compounds (chlorophenols, toluene, polycyclic aromatic hydrocarbons, tetradifon and pyridine); organisms include mussels, oysters, earthworms, water fleas and zebrafish. In most cases, the data sets could be adequately described with any of the toxicity modules, and no single module gave superior fits to all data sets. We therefore propose that for many applications, it is reasonable to use the most general and parameter sparse module, i.e. module 3 that assumes similar effects on feeding and maintenance, as a default. For one example (water fleas), we use parameter estimates to calculate the impact of food availability and toxicant levels on the long term population growth rate.

Modeling Neisseria meningitidis B metabolism at different specific growth rates

Neisseria meningitidis is a human pathogen that can infect diverse sites within the human host. The major diseases caused by N. meningitidis are responsible for death and disability, especially in young infants. At the Netherlands Vaccine Institute (NVI) a vaccine against serogroup B organisms is currently being developed. This study describes the influence of the growth rate of N. meningitidis on its macro-molecular composition and its metabolic activity and was determined in chemostat cultures. In the applied range of growth rates, no significant changes in RNA content and protein content with growth rate were observed in N. meningitidis. The DNA content in N. meningitidis was somewhat higher at the highest applied growth rate. The phospholipid and lipopolysaccharide content in N. meningitidis changed with growth rate but no specific trends were observed. The cellular fatty acid composition and the amino acid composition did not change significantly with growth rate. Additionally, it was found that the PorA content in outer membrane vesicles was significantly lower at the highest growth rate. The metabolic fluxes at various growth rates were calculated using flux balance analysis. Errors in fluxes were calculated using Monte Carlo Simulation and the reliability of the calculated flux distribution could be indicated, which has not been reported for this type of analysis. The yield of biomass on substrate (Y(x/s)) and the maintenance coefficient (m(s)) were determined as 0.44 (+/-0.04) g g(-1) and 0.04 (+/-0.02) g g(-1) h(-1), respectively. The growth associated energy requirement (Y(x/ATP)) and the non-growth associated ATP requirement for maintenance (m(ATP)) were estimated as 0.13 (+/-0.04) mol mol(-1) and 0.43 (+/-0.14) mol mol(-1) h(-1), respectively. It was found that the split ratio between the Entner-Doudoroff and the pentose phosphate pathway, the sole glucose utilizing pathways in N. meningitidis, had a minor effect on ATP formation rate but a major effect on the fluxes going through for instance the citric-acid cycle. For this reason, we presented flux ranges for underdetermined parts of metabolic network rather than presenting single flux values, which is more commonly done in literature.

A kinetic inhibition mechanism for maintenance

To fulfil their maintenance costs, most species use mobile pools of metabolites (reserve) in favourable conditions, but can also use less mobile pools (structure) under food-limiting conditions. While some empirical models always pay maintenance costs from structure, the presence of reserve inhibits the use of structure for maintenance purposes. The standard dynamic energy budgets (DEB) model captures this by simply supplementing all costs that could not be paid from reserve with structure. This is less realistic at the biochemical level, and involves a sudden use of structure that can complicate the analysis of the model properties. We here propose a new inhibition formulation for the preferential use of reserve above structure in maintenance that avoids sudden changes in the metabolites use. It is based on the application of the theory for synthesizing units, which can easily become rather complex for demand processes, such as the maintenance. We found, however, a simple explicit expression for the use of reserve and structure for maintenance purposes and compared the numerical behaviour with that of a classical model in oscillating conditions, by using parameters values from a fit of the models to data on yeasts in a batch culture. We conclude that our model can better handle variable environments. This new inhibition formulation has a wide applicability in modelling metabolic processes.

Temperature affects stoichiometry and biochemical composition of Escherichia coli

Temperature is a master variable controlling biochemical processes in organisms, and its effects are manifested on many organizational levels in organisms and ecosystems. We examined the effects of temperature on the biochemical composition and stoichiometry of a model heterotrophic bacterium, Escherichia coli K-12, held at constant growth rate in chemostats. Increasing temperature led to increased cellular organic carbon (C) and organic nitrogen (N) with decreased phosphorus (P) content, leading to increased C/P and N/P biomass ratios. P content was related to cellular RNA, which is P-rich (9-10% by weight) and nonnucleic acid P (presumably composed of mostly phospholipids, intracellular phosphate, and polyphosphate). These results indicate that E. coli allocates an increased proportion of its P cell quota toward assembly (ribosomes) at low temperatures and an increasing proportion toward resource acquisition machinery (membranes) at higher temperatures. If these results are relevant to the behavior of prokaryotic heterotrophs in natural settings (the gut, soils, lakes, oceans, etc.), it suggests greater nutrient regeneration and less microbial nutrient retention as temperatures increase.

Changes of pentose phosphate pathway flux in vivo in Corynebacterium glutamicum during leucine-limited batch cultivation as determined from intracellular metabolite concentration measurements

Corynebacterium glutamicum is an important organism for the industrial production of amino acids such as lysine. In the present study time-dependent changes in the oxidative pentose phosphate pathway activity, an important site of NADPH regeneration in C. glutamicum, are investigated, whereby intracellular metabolite concentrations and specific enzyme activities in two isogenic leucine auxotrophic strains differing only in the regulation of their aspartate kinases were compared. After leucine limitation only the strain with a feedback-resistant aspartate kinase began to excrete lysine into the culture medium. Concomitantly, the intracellular NADPH to NADP concentration ratio increased from 2 to 4 in the non-producing strain, whereas it remained constant at about 1.2 in the lysine-producing strain. From these data the in'vivo flux through the pentose phosphate pathway was calculated. These results were used to approximate the total NADPH regeneration by glucose-6-phosphate dehydrogenase, 6-phosphogluconate dehydrogenase and isocitrate dehydrogenase, which agreed fairly well with the calculated demands for biomass formation and lysine biosynthesis. The analysis allowed to conclude that NADPH regeneration in the pentose phosphate pathway is essential for lysine biosynthesis in C. glutamicum.