Extreme calorie restriction in yeast retentostats induces uniform non-quiescent growth arrest.

Non-dividing Saccharomyces cerevisiae cultures are highly relevant for fundamental and applied studies. However, cultivation conditions in which non-dividing cells retain substantial metabolic activity are lacking. Unlike stationary-phase (SP) batch cultures, the current experimental paradigm for non-dividing yeast cultures, cultivation under extreme calorie restriction (ECR) in retentostat enables non-dividing yeast cells to retain substantial metabolic activity and to prevent rapid cellular deterioration. Distribution of F-actin structures and single-cell copy numbers of specific transcripts revealed that cultivation under ECR yields highly homogeneous cultures, in contrast to SP cultures that differentiate into quiescent and non-quiescent subpopulations. Combined with previous physiological studies, these results indicate that yeast cells subjected to ECR survive in an extended G1 phase. This study demonstrates that yeast cells exposed to ECR differ from carbon-starved cells and offer a promising experimental model for studying non-dividing, metabolically active, and robust eukaryotic cells.

Growth inhibition of S. cerevisiae, B. subtilis, and E. coli by lignocellulosic and fermentation products.

This paper describes the effect of several inhibiting components on three potential hosts for the bio-based production of methyl propionate, namely, wild-type Escherichia coli and Bacillus subtilis, and evolved Saccharomyces cerevisiae IMS0351. The inhibition by the lignocellulose-derived products 5-hydroxymethyl-2-furaldehyde, vanillin, and syringaldehyde and the fermentation products 2-butanol, 2-butanone, methyl propionate, and ethyl acetate has been assessed for these strains in defined medium. Multiple screenings were performed using small-scale cultures in both shake flasks and microtiter plates. Technical drawbacks revealed the limited applicability of the latter in this study. The microbial growth was characterized by means of a lag-time model, and the inhibitory thresholds were determined using product-inhibition models. The lignocellulose-derived products were found to be highly inhibitory, and none of the strains could grow in the presence of 2.0 g L-1 of product. From the fermentation products tested, methyl propionate had the most severe impact resulting in complete inhibition of all the strains when exposed to concentrations in the range of 12-18 g L-1. In general, S. cerevisiae and B. subtilis were comparatively more tolerant than E. coli to all the fermentation products, despite E. coli's lower sensitivity towards vanillin. The results suggest that, overall, the strains investigated have good potential to be engineered and further established as hosts for the bio-based production of methyl esters.

In vivo kinetic analysis of the penicillin biosynthesis pathway using PAA stimulus response experiments.

In this study we combined experimentation with mathematical modeling to unravel the in vivo kinetic properties of the enzymes and transporters of the penicillin biosynthesis pathway in a high yielding Penicillium chrysogenum strain. The experiment consisted of a step response experiment with the side chain precursor phenyl acetic acid (PAA) in a glucose-limited chemostat. The metabolite data showed that in the absence of PAA all penicillin pathway enzymes were expressed, leading to the production of a significant amount of 6-aminopenicillanic acid (6APA) as end product. After the stepwise perturbation with PAA, the pathway produced PenG within seconds. From the extra- and intracellular metabolite measurements, hypotheses for the secretion mechanisms of penicillin pathway metabolites were derived. A dynamic model of the penicillin biosynthesis pathway was then constructed that included the formation and transport over the cytoplasmic membrane of pathway intermediates, PAA and the product penicillin-G (PenG). The model parameters and changes in the enzyme levels of the penicillin biosynthesis pathway under in vivo conditions were simultaneously estimated using experimental data obtained at three different timescales (seconds, minutes, hours). The model was applied to determine changes in the penicillin pathway enzymes in time, calculate fluxes and analyze the flux control of the pathway. This led to a reassessment of the in vivo behavior of the pathway enzymes and in particular Acyl-CoA:Isopenicillin N Acyltransferase (AT).

Mathematical model of adherent Vero cell growth and poliovirus production in animal component free medium.

Sabin-IPV (or sIPV, inactivated polio vaccine based on attenuated Sabin strains) is anticipated to replace the oral polio vaccine for the endgame in polio eradication. Optimization of sIPV production will lead to a better economically feasible vaccine. To assist process optimization, we studied Sabin type 1 poliovirus (PV) infection kinetics on Vero cells in controlled bioreactor vessels. The aim of our study was to develop a descriptive mathematical model able to capture the dynamics of adherent Vero cell growth and PV infection kinetics in animal component free medium. The model predicts the cell density, metabolites profiles, and viral yields in time. We found that the multiplicity of infection (MOI) and the time of infection (TOI) within the investigated range did not affect maximal PV yields, but they did affect the process time. The latter may be reduced by selecting a low TOI and a high MOI. Additionally, we present a correlation between viral titers and D-antigen, a measure for immunogenicity, of Sabin type 1 PV. The developed model is adequate for further studies of the cell metabolism and infection kinetics and may be used to identify control strategies to increase viral productivity. Increased viral yields reduce costs of polio vaccines with large implications on public health.

pH-dependent uptake of fumaric acid in Saccharomyces cerevisiae under anaerobic conditions.

Microbial production of C(4) dicarboxylic acids from renewable resources has gained renewed interest. The yeast Saccharomyces cerevisiae is known as a robust microorganism and is able to grow at low pH, which makes it a suitable candidate for biological production of organic acids. However, a successful metabolic engineering approach for overproduction of organic acids requires an incorporation of a proper exporter to increase the productivity. Moreover, low-pH fermentations, which are desirable for facilitating the downstream processing, may cause back diffusion of the undissociated acid into the cells with simultaneous active export, thereby creating an ATP-dissipating futile cycle. In this work, we have studied the uptake of fumaric acid in S. cerevisiae in carbon-limited chemostat cultures under anaerobic conditions. The effect of the presence of fumaric acid at different pH values (3 to 5) has been investigated in order to obtain more knowledge about possible uptake mechanisms. The experimental results showed that at a cultivation pH of 5.0 and an external fumaric acid concentration of approximately 0.8 mmol · liter(-1), the fumaric acid uptake rate was unexpectedly high and could not be explained by diffusion of the undissociated form across the plasma membrane alone. This could indicate the presence of protein-mediated import. At decreasing pH levels, the fumaric acid uptake rate was found to increase asymptotically to a maximum level. Although this observation is in accordance with protein-mediated import, the presence of a metabolic bottleneck for fumaric acid conversion under anaerobic conditions could not be excluded.

Microfluidic Platform with Serpentine Geometry Providing Chaotic Mixing in Induction Time Experiments.

We present a droplet microfluidic platform mixing the contents of the droplet chaotically in microfluidic induction time measurements, a promising method for quantifying nucleation kinetics with minute amounts of solute. The nucleation kinetics of aqueous potassium chloride droplets dispersed in mineral oil without surfactants is quantified in the presence and absence of chaotic mixing. We demonstrate the ability of the proposed platform to dictate droplet size, to provide a homogeneous temperature distribution, and to chaotically mix the droplet contents. Chaotic mixing in induction time measurements is facilitated by the motion of droplets through serpentine micromixer bends, while the extent of mixing is controlled by how much droplets move. Different nucleation kinetics are observed in experiments where the droplets are static, mixed, and in motion. We hypothesize that the droplet motion induces formation of a thin-liquid Bretherton film surrounding the droplets. The thin film shields droplets from solid boundaries that are more efficient heteronucleant surfaces compared to liquid-liquid interfaces. We observed that repeated microfluidic induction time measurements, particularly with moving droplets, produce significantly distinct cumulative nucleation probability curves, indicating that the measured nucleation kinetics depend strongly on the details of the experimental procedure, which we discuss in detail. Finally, we compare the microfluidic experiments to well-mixed, milliliter volume, turbidity-based measurements in the context of classic nucleation theory.

Cytosolic NADPH balancing in Penicillium chrysogenum cultivated on mixtures of glucose and ethanol.

The in vivo flux through the oxidative branch of the pentose phosphate pathway (oxPPP) in Penicillium chrysogenum was determined during growth in glucose/ethanol carbon-limited chemostat cultures, at the same growth rate. Non-stationary (13)C flux analysis was used to measure the oxPPP flux. A nearly constant oxPPP flux was found for all glucose/ethanol ratios studied. This indicates that the cytosolic NADPH supply is independent of the amount of assimilated ethanol. The cofactor assignment in the model of van Gulik et al. (Biotechnol Bioeng 68(6):602-618, 2000) was supported using the published genome annotation of P. chrysogenum. Metabolic flux analysis showed that NADPH requirements in the cytosol remain nearly the same in these experiments due to constant biomass growth. Based on the cytosolic NADPH balance, it is known that the cytosolic aldehyde dehydrogenase in P. chrysogenum is NAD(+) dependent. Metabolic modeling shows that changing the NAD(+)-aldehyde dehydrogenase to NADP(+)-aldehyde dehydrogenase can increase the penicillin yield on substrate.

Genome-derived minimal metabolic models for Escherichia coli MG1655 with estimated in vivo respiratory ATP stoichiometry.

Metabolic network models describing growth of Escherichia coli on glucose, glycerol and acetate were derived from a genome scale model of E. coli. One of the uncertainties in the metabolic networks is the exact stoichiometry of energy generating and consuming processes. Accurate estimation of biomass and product yields requires correct information on the ATP stoichiometry. The unknown ATP stoichiometry parameters of the constructed E. coli network were estimated from experimental data of eight different aerobic chemostat experiments carried out with E. coli MG1655, grown at different dilution rates (0.025, 0.05, 0.1, and 0.3 h(-1)) and on different carbon substrates (glucose, glycerol, and acetate). Proper estimation of the ATP stoichiometry requires proper information on the biomass composition of the organism as well as accurate assessment of net conversion rates under well-defined conditions. For this purpose a growth rate dependent biomass composition was derived, based on measurements and literature data. After incorporation of the growth rate dependent biomass composition in a metabolic network model, an effective P/O ratio of 1.49 +/- 0.26 mol of ATP/mol of O, K(X) (growth dependent maintenance) of 0.46 +/- 0.27 mol of ATP/C-mol of biomass and m(ATP) (growth independent maintenance) of 0.075 +/- 0.015 mol of ATP/C-mol of biomass/h were estimated using a newly developed Comprehensive Data Reconciliation (CDR) method, assuming that the three energetic parameters were independent of the growth rate and the used substrate. The resulting metabolic network model only requires the specific rate of growth, micro, as an input in order to accurately predict all other fluxes and yields.

Dynamic gene expression regulation model for growth and penicillin production in Penicillium chrysogenum.

As is often the case for microbial product formation, the penicillin production rate of Penicillium chrysogenum has been observed to be a function of the growth rate of the organism. The relation between the biomass specific rate of penicillin formation (q(p)) and growth rate (mu) has been measured under steady state conditions in carbon limited chemostats resulting in a steady state q(p)(mu) relation. Direct application of such a relation to predict the rate of product formation during dynamic conditions, as they occur, for example, in fed-batch experiments, leads to errors in the prediction, because q(p) is not an instantaneous function of the growth rate but rather lags behind because of adaptational and regulatory processes. In this paper a dynamic gene regulation model is presented, in which the specific rate of penicillin production is assumed to be a linear function of the amount of a rate-limiting enzyme in the penicillin production pathway. Enzyme activity assays were performed and strongly indicated that isopenicillin-N synthase (IPNS) was the main rate-limiting enzyme for penicillin-G biosynthesis in our strain. The developed gene regulation model predicts the expression of this rate limiting enzyme based on glucose repression, fast decay of the mRNA encoding for the enzyme as well as the decay of the enzyme itself. The gene regulation model was combined with a stoichiometric model and appeared to accurately describe the biomass and penicillin concentrations for both chemostat steady-state as well as the dynamics during chemostat start-up and fed-batch cultivation.

Model reduction and a priori kinetic parameter identifiability analysis using metabolome time series for metabolic reaction networks with linlog kinetics.

In this work, we present a time-scale analysis based model reduction and parameter identifiability analysis method for metabolic reaction networks. The method uses the information obtained from short term chemostat perturbation experiments. We approximate the time constant of each metabolite pool by their turn-over time and classify the pools accordingly into two groups: fast and slow pools. We performed a priori model reduction, neglecting the dynamic term of the fast pools. By making use of the linlog approximative kinetics, we obtained a general explicit solution for the fast pools in terms of the slow pools by elaborating the degenerate algebraic system resulting from model reduction. The obtained relations yielded also analytical relations between a subset of kinetic parameters. These relations also allow to realize an analytical model reduction using lumped reaction kinetics. After solving these theoretical identifiability problems and performing model reduction, we carried out a Monte Carlo approach to study the practical identifiability problems. We illustrated the methodology on model reduction and theoretical/practical identifiability analysis on an example system representing the glycolysis in Saccharomyces cerevisiae cells.

Isotopic non-stationary 13C gluconate tracer method for accurate determination of the pentose phosphate pathway split-ratio in Penicillium chrysogenum.

Current (13)C labeling experiments for metabolic flux analysis (MFA) are mostly limited by either the requirement of isotopic steady state or the extremely high computational effort due to the size and complexity of large metabolic networks. The presented novel approach circumvents these limitations by applying the isotopic non-stationary approach to a local metabolic network. The procedure is demonstrated in a study of the pentose phosphate pathway (PPP) split-ratio of Penicillium chrysogenum in a penicillin-G producing chemostat-culture grown aerobically at a dilution rate of 0.06h(-1) on glucose, using a tracer amount of uniformly labeled [U-(13)C(6)] gluconate. The rate of labeling inflow can be controlled by using different cell densities and/or different fractions of the labeled tracer in the feed. Due to the simplicity of the local metabolic network structure around the 6-phosphogluconate (6pg) node, only three metabolites need to be measured for the pool size and isotopomer distribution. Furthermore, the mathematical modeling of isotopomer distributions for the flux estimation has been reduced from large scale differential equations to algebraic equations. Under the studied cultivation condition, the estimated split-ratio (41.2+/-0.6%) using the novel approach, shows statistically no difference with the split-ratio obtained from the originally proposed isotopic stationary gluconate tracing method.

Experimental design for evaluating WWTP data by linear mass balances.

A stepwise experimental design procedure to obtain reliable data from wastewater treatment plants (WWTPs) was developed. The proposed procedure aims at determining sets of additional measurements (besides available ones) that guarantee the identifiability of key process variables, which means that their value can be calculated from other, measured variables, based on available constraints in the form of linear mass balances. Among all solutions, i.e. all possible sets of additional measurements allowing the identifiability of all key process variables, the optimal solutions were found taking into account two objectives, namely the accuracy of the identified key variables and the cost of additional measurements. The results of this multi-objective optimization problem were represented in a Pareto-optimal front. The presented procedure was applied to a full-scale WWTP. Detailed analysis of the relation between measurements allowed the determination of groups of overlapping mass balances. Adding measured variables could only serve in identifying key variables that appear in the same group of mass balances. Besides, the application of the experimental design procedure to these individual groups significantly reduced the computational effort in evaluating available measurements and planning additional monitoring campaigns. The proposed procedure is straightforward and can be applied to other WWTPs with or without prior data collection.

Thermodynamic description of peptide adsorption on mixed-mode resins.

In this work the adsorption of tri-peptides on a mixed-mode resin was studied using isocratic pulse response experiments. Various salt concentration, temperature and pH combinations were used to measure retention times of several tri-peptides. The experiments were evaluated according to an extension of the stoichiometric displacement model and the steric mass action model of protein-ligand binding. The application of this model in the understanding of mixed mode adsorption process is discussed. A unique set of meaningful thermodynamic parameters was obtained for each resin-peptide-temperature and resin-peptide-pH combination. Finally it was shown that these thermodynamic parameters can be used in defining quantitative relationships within the framework of extra thermodynamic relationships.

Parameter identification of in vivo kinetic models: limitations and challenges.

Systems metabolic engineering of metabolic networks by genetic techniques requires kinetic equations for each enzyme present. In vitro studies of singular enzymes have limitations for predicting in vivo behavior, and in vivo experiments are constrained to retain viable cells. The estimation of kinetic parameters in vivo is a challenge due to the complexity of the internal cell environment. This concise review analyzes the limitations of in vitro and in vivo approaches, and shows that not all parameters can be determined and that multicollinearity exists. On the other hand, this review also shows that cell metabolism is adequately described with a smaller number of parameters and with approximative or reduced models. A major hurdle is the identification and quantification of allosteric effectors. Despite limitations, in vivo kinetic experiments are adequate in providing a quantitative description of the cell as a system.

Efficient calculation of compound similarity based on maximum common subgraphs and its application to prediction of gene transcript levels.

Properties of a chemical entity, both physical and biological, are related to its structure. Since compound similarity can be used to infer properties of novel compounds, in chemoinformatics much attention has been paid to ways of calculating structural similarity. A useful metric to capture the structural similarity between compounds is the relative size of the Maximum Common Subgraph (MCS). The MCS is the largest substructure present in a pair of compounds, when represented as graphs. However, in practice it is difficult to employ such a metric, since calculation of the MCS becomes computationally intractable when it is large. We propose a novel algorithm that significantly reduces computation time for finding large MCSs, compared to a number of state-of-the-art approaches. The use of this algorithm is demonstrated in an application predicting the transcriptional response of breast cancer cell lines to different drug-like compounds, at a scale which is challenging for the most efficient MCS-algorithms to date. In this application 714 compounds were compared.

How to obtain true and accurate rate-values.

In metabolic flux calculations, the uptake and secretion rates (for substrate, O(2), CO(2), growth, (by)-products) are essential to arrive at correct calculated fluxes. Surprisingly, a lot of research has been published on the methods of flux calculations, but much less attention has been spent on the methods to obtain accurate and true uptake and secretion rates which are used as input. Therefore, this contribution focuses on.

Model-based rational strategy for chromatographic resin selection.

A model-based rational strategy for the selection of chromatographic resins is presented. The main question being addressed is that of selecting the most optimal chromatographic resin from a few promising alternatives. The methodology starts with chromatographic modeling,parameters acquisition, and model validation, followed by model-based optimization of the chromatographic separation for the resins of interest. Finally, the resins are rationally evaluated based on their optimized operating conditions and performance metrics such as product purity, yield, concentration, throughput, productivity, and cost. Resin evaluation proceeds by two main approaches. In the first approach, Pareto frontiers from multi-objective optimization of conflicting objectives are overlaid for different resins, enabling direct visualization and comparison of resin performances based on the feasible solution space. The second approach involves the transformation of the resin performances into weighted resin scores, enabling the simultaneous consideration of multiple performance metrics and the setting of priorities. The proposed model-based resin selection strategy was illustrated by evaluating three mixed mode adsorbents (ADH, PPA, and HEA) for the separation of a ternary mixture of bovine serum albumin, ovalbumin, and amyloglucosidase. In order of decreasing weighted resin score or performance, the top three resins for this separation were ADH [PPA[HEA. The proposed model-based approach could be a suitable alternative to column scouting during process development, the main strengths being that minimal experimentation is required and resins are evaluated under their ideal working conditions, enabling a fair comparison. This work also demonstrates the application of column modeling and optimization to mixed mode chromatography.

Cumulative bondomers: a new concept in flux analysis from 2D [13C,1H] COSY NMR data.

A well-established way of determining metabolic fluxes is to measure 2D [(13)C,(1)H] COSY NMR spectra of components of biomass grown on uniformly (13)C-labeled carbon sources. When using the entire set of measured data to simultaneously determine all fluxes in a proposed metabolic network model, the (13)C-labeling distribution in all measured compounds has to be simulated. This requires very large sets of isotopomer or cumomer balances. This article introduces the new concept of bondomers; entities that only vary in the numbers and positions of C-C bonds that have remained intact since the medium substrate molecule entered the metabolism. Bondomers are shown to have many analogies to isotopomers. One of these is that bondomers can be transformed to cumulative bondomers, just like isotopomers can be transformed to cumomers. Similarly to cumomers, cumulative bondomers allow an analytical solution of the entire set of balances describing a metabolic network. The main difference is that cumulative bondomer models are considerably smaller than corresponding cumomer models. This saves computational time, allows easier identifiability analysis, and yields new insights in the information content of 2D [(13)C,(1)H] COSY NMR data. We illustrate the theoretical concepts by means of a realistic example of the glycolytic and pentose phosphate pathways. The combinations of 2D [(13)C,(1)H] COSY NMR data that allow identification of all metabolic fluxes in these pathways are analyzed, and it is found that the NMR data contain less information than was previously expected.

Metabolic flux and metabolic network analysis of Penicillium chrysogenum using 2D [13C, 1H] COSY NMR measurements and cumulative bondomer simulation.

At present two alternative methods are available for analyzing the fluxes in a metabolic network: (1) combining measurements of net conversion rates with a set of metabolite balances including the cofactor balances, or (2) leaving out the cofactor balances and fitting the resulting free fluxes to measured (13)C-labeling data. In this study these two approaches are applied to the fluxes in the glycolysis and pentose phosphate pathway of Penicillium chrysogenum growing on either ammonia or nitrate as the nitrogen source, which is expected to give different pentose phosphate pathway fluxes. The presented flux analyses are based on extensive sets of 2D [(13)C, (1)H] COSY data. A new concept is applied for simulation of this type of (13)C-labeling data: cumulative bondomer modeling. The outcomes of the (13)C-labeling based flux analysis substantially differ from those of the pure metabolite balancing approach. The fluxes that are determined using (13)C-labeling data are shown to be highly dependent on the chosen metabolic network. Extending the traditional nonoxidative pentose phosphate pathway with additional transketolase and transaldolase reactions, extending the glycolysis with a fructose 6-phosphate aldolase/dihydroxyacetone kinase reaction sequence or adding a phosphoenolpyruvate carboxykinase reaction to the model considerably improves the fit of the measured and the simulated NMR data. The results obtained using the extended version of the nonoxidative pentose phosphate pathway model show that the transketolase and transaldolase reactions need not be assumed reversible to get a good fit of the (13)C-labeling data. Strict statistical testing of the outcomes of (13)C-labeling based flux analysis using realistic measurement errors is demonstrated to be of prime importance for verifying the assumed metabolic model.