Peter J.T. Verheijen

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.

ON THE PERFORMANCE OF AN ON-LINE TIME-OF-FLIGHT MASS SPECTROMETER FOR AEROSOLS

Abstract An instrument has been developed to analyze the size and chemical composition of individual, air-borne particles. The particles are introduced into the vacuum chamber of a time-off-light mass spectrometer where they are detected and sized using an aerodynamic principle. After sizing, the particles are vaporized by an excimer laser and the resulting ions are analyzed. Experiments have shown that polystyrene particles 1 μm in diameter are not fully vaporized by the laser pulse. The instrument has been characterized and some examples of measurements on ambient and artificial aerosols are presented.

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, KX (growth dependent maintenance) of 0.46 ± 0.27 mol of ATP/C‐mol of biomass and mATP (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, µ, as an input in order to accurately predict all other fluxes and yields. Biotechnol. Bioeng. 2010;107: 369–381.

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 (qp) and growth rate (µ) has been measured under steady state conditions in carbon limited chemostats resulting in a steady state qp(µ) 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 qp 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. Biotechnol. Bioeng. 2010;106: 608–618.

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.

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 multiobjective 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.

The placement of two-stream and multi-stream heat ex-changers in an exisiting network through path analysis

Abstract A prescreening and decomposition method is presented to analyse heat exchanger networks for retrofitting. The method, called Path Analysis, selects and analyses fractions from the existing network, either by heuristics or by an algorithm. By comparison of all fractions, the critical parts of the network that should be adapted can be identified. The adaptations can be done independent of the remaining network. Thus Path Analysis enables a considerable reduction of the effort in retrofit design. Meanwhile the simplest network adaptations are favoured. Path Analysis is applied to several cases. The results for an aromatics case are presented. Using the right software tools, the engineering effort can be reduced considerably, compared with existing methods. Solutions tend to be less complex, while the profitability is sometimes higher than was expected from global analysis. With Path Analysis the retrofit design using new multi-stream heat exchangers proved to be straightforward.

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.

On-line measurement of particle size and composition

Abstract The Particle Technology Group at Delft University of Technology is developing an instrument for on-line, real-time measurement of size and chemical composition of individual aerosol particles. Particles are sampled from an aerosol by using aerosol beam techniques. Experimental results and a theoretical model for particle beams have been used to design an improved beam generator. The apparatus comprises an exchangeable capillary nozzle and three vacuum chambers in series. Collimated by the beam system the particles enter the ionization chamber of a time-of-flight mass spectrometer, where they can be vaporized and ionized by the pulse from a Q-switched YAG laser. Analysis of the generated ions with the mass spectrometer provides information on the chemical composition of the particles. This paper reports the first results obtained with a new experimental setup, based on earlier work. Mass spectra were observed for a static target and particles of sodium chloride. The results were compared with spectra obtained with a LAMMA-500

pH-Dependent Uptake of Fumaric Acid in Saccharomyces cerevisiae under Anaerobic Conditions

ABSTRACT Microbial production of C4 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.