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The photocatalytic decomposition of ethylene in a spiral-type reactor was investigated to develop a method for preserving the quality of agricultural products. Ethylene was injected or discharged from an apple into a 0.016 m3 closed container and was photocatalytically decomposed in the reactor at 25 °C. The results indicated that ethylene was decomposed and became undetectable (the amount of ethylene was lower than the detection limit of 40 ppb), which is useful for preserving the quality of agricultural products over long periods of time. For the demonstration experiment, we placed an apple, a persimmon, a cucumber, a cabos (Citrus sphaerocarpa), and a banana in a 0.117 m3 closed container at 25 °C. When the air inside the container was not photocatalytically treated, the concentration of ethylene increased up to 2.7 ppm. This lowered the quality of the persimmon, cucumber, and apple significantly. In contrast, when the air inside the container was photocatalytically treated, they remained fresh for at least nine days. Methane was simultaneously generated as a byproduct. It was hypothesized that methane was the product of the photocatalytic decomposition of some unknown chemical compound released from the analyzed agricultural products; however, we were unable to identify the chemical compound. Computer simulations using experimentally determined parameter values suggested that the decomposition of ethylene begins before or as soon as agricultural products are stored in closed rooms, such as truck containers. In conclusion, the photocatalytic decomposition of ethylene using a spiral-type reactor is effective for preserving the quality of agricultural products.

The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale metabolic profiles under different biological or environmental conditions. Time series data are useful for predicting the most likely metabolic pathways because they provide important information regarding the accumulation of metabolites, which implies causal relationships in the metabolic reaction network. Considerable effort has been undertaken to utilize these data for constructing a mathematical model merging system properties and quantitatively characterizing a whole metabolic system in toto. However, there are technical difficulties between benchmarking the provision and utilization of data. Although, hundreds of metabolites can be measured, which provide information on the metabolic reaction system, simultaneous measurement of thousands of metabolites is still challenging. In addition, it is nontrivial to logically predict the dynamic behaviors of unmeasurable metabolite concentrations without sufficient information on the metabolic reaction network. Yet, consolidating the advantages of advancements in both metabolomics and mathematical modeling remain to be accomplished. This review outlines the conceptual basis of and recent advances in technologies in both the research fields. It also highlights the potential for constructing a large-scale mathematical model by estimating model parameters from time series metabolome data in order to comprehensively understand metabolism at the systems level.

The present work investigates the mechanism of the photocatalytic and adsorptive treatment of an aqueous DNP (2,4-dinitrophenol) solution in an annular-flow reactor installed with a TiO 2 /AC-PET film (a polyethylene terephthalate film adhesively fixing activated carbon particles covered with a thin and porous film of titanium dioxide). Unfortunately, the experimental result indicates that it is impossible to correctly measure the time courses of product ion concentrations because they are adsorbed onto or desorbed from AC particles. Therefore, a computer simulation methodology using mathematical models is introduced in order to elucidate the treatment mechanism. Treatments of aqueous DNP solutions using the TiO 2 -PET film reveal that the diffusion of DNP from a bulk solution to a TiO 2 film is based on the gradient of DNP concentration, generated by a rapid adsorption of DNP onto AC and photocatalytic reaction, in the very neighborhood of TiO 2 film, and this diffusion increases the DNP concentration at the surface of TiO 2 film, thereby enhancing the rate of photocatalytic decomposition. Moreover, it is found that the TiO 2 /AC-PET film can lower the burden of the adsorption of DNP onto AC compared with the AC-PET film because a part of DNP molecules are photocatalytically decomposed and the percentage of this decomposition is increased at a lower linear velocity. In conclusion, the mathematical model taking into consideration a film-diffusional effect can successfully explain the complicated mechanism of the treatment of an aqueous DNP solution using the TiO 2 /AC-PET film.

This paper investigates the accuracy of a matrix method proposed by other researchers to calculate time-varying flux control coefficients (dynamic FCCs) from elasticity coefficients by means of summation and connectivity theorems in the framework of metabolic control analysis. A mathematical model for the fed-batch penicillin V fermentation process is used as a case example for discussion. Calculated results reveal that this method produces significant calculation errors because the theorems are essentially valid only in steady state, although it may provide rough time-transient behaviors of FCCs. Strictly, therefore, dynamic FCCs should be directly calculated from the differential equations for metabolite concentrations and sensitivities.

Anatase titanium dioxide (TiO 2 ) in particle sizes of roughly 0.5–20 μm was prepared from amorphous TiO 2 in an aqueous H 2 O 2 solution by heating at 90 °C for 9 h and directly deposited on a PET film. On the other hand, granular activated carbon (AC) particles in sizes of 1–2 mm in diameter were adhesively deposited on a PET film, and their surfaces were also coated with TiO 2 . The resulting three preparations (TiO 2 -, AC-, and TiO 2 /AC-PET films) were set up in an annular-flow reactor to treat aqueous solutions of 2,4-dinitrophenol (DNP) in a batch-recirculation mode. The rate of DNP adsorption onto the TiO 2 /AC-PET film without UV irradiation was almost the same as that onto the AC-PET film, indicating that the attraction of DNP to AC was not lowered in the presence of TiO 2 film. Observation of SEM photographs suggests that this result is attributed to the porous structure of the thin TiO 2 film covering AC particles. The rate of DNP removal by the TiO 2 -AC PET film under UV irradiation was 2.9 times higher than that by the TiO 2 -PET film under UV irradiation, and was 1.1 times higher than the rate of DNP adsorption onto the AC-PET film. The rate of DNP removal by the AC-PET film decreased by 40% after six runs, while that by the TiO 2 /AC-PET film decreased by 22%. Durable experiments using the TiO 2 /AC-PET and AC-PET films clarified that the lifetime of the TiO 2 /AC-PET film is at least two times longer than that of the AC-PET film. This result suggests that DNP molecules are photocatalytically decomposed when passing through the porous TiO 2 -PET film, which lessens a burden of DNP adsorption on AC. Moreover, the DNP treatments in the batch-recirculation flow system suggested that the TiO 2 /AC-PET film saturated with DNP can be successfully regenerated at 60 °C.

Metabolite concentrations in cells are governed by enzyme kinetics in the metabolic reaction system. One can analyze how these concentrations depend on system variables such as enzyme activities by computing system sensitivities, which generally vary with time. Dynamic sensitivities, i.e., time-varying sensitivities, reflect the time-dependent response of the metabolic reaction network to perturbations. Unfortunately, dynamic sensitivity profiles are not commonly used in the analysis of metabolic reaction systems. In the present study, we demonstrate the use of dynamic logarithmic gains, i.e., normalized time-varying sensitivities, to gain insights into the dynamic behavior of metabolic networks. A biosynthetic reaction model of aromatic amino acids proposed by other researchers is used as a case study. The model system is analyzed using the dynamic logarithmic gains in parallel with simulations of the time-transient behavior of metabolite concentrations and metabolic fluxes. The result indicates that the influences of independent variables are most pronounced just after perturbations and the effects of perturbations on metabolite concentration at early times can be larger than those at steady state. These findings suggest that it is important to perform dynamic sensitivity analysis in addition to steady-state analysis. Furthermore, the rankings of the bottleneck ranking indicators, defined as the product of dynamic logarithmic gain and metabolite concentration, for three desired amino acids reveal that the degree of bottleneck for each enzyme changes with time. In conclusion, the dynamic logarithmic gains are not only useful for analyzing metabolic reaction systems but also can offer additional insights on the transient behavior of the system over steady state sensitivities, leading to a proper design of metabolic systems., Mathematical Biosciences, 269, ISSN:0025-5564, ISSN:1879-3134

Recent development of high-throughput analytical techniques has made it possible to qualitatively identify a number of metabolites simultaneously. Correlation and multivariate analyses such as principal component analysis have been widely used to analyse those data and evaluate correlations among the metabolic profiles. However, these analyses cannot simultaneously carry out identification of metabolic reaction networks and prediction of dynamic behaviour of metabolites in the networks. The present study, therefore, proposes a new approach consisting of a combination of statistical technique and mathematical modelling approach to identify and predict a probable metabolic reaction network from time-series data of metabolite concentrations and simultaneously construct its mathematical model. Firstly, regression functions are fitted to experimental data by the locally estimated scatter plot smoothing method. Secondly, the fitted result is analysed by the bivariate Granger causality test to determine which metabolites cause the change in other metabolite concentrations and remove less related metabolites. Thirdly, S-system equations are formed by using the remaining metabolites within the framework of biochemical systems theory. Finally, parameters including rate constants and kinetic orders are estimated by the Levenberg-Marquardt algorithm. The estimation is iterated by setting insignificant kinetic orders at zero, i.e., removing insignificant metabolites. Consequently, a reaction network structure is identified and its mathematical model is obtained. Our approach is validated using a generic inhibition and activation model and its practical application is tested using a simplified model of the glycolysis of Lactococcus lactis MG1363, for which actual time-series data of metabolite concentrations are available. The results indicate the usefulness of our approach and suggest a probable pathway for the production of lactate and acetate. The results also indicate that the approach pinpoints a probable strong inhibition of lactate on the glycolysis pathway.