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京都大学 / 新制・課程博士 / 博士(医学) / 甲第23769号 / 医博第4815号 / 新制||医||1056(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 中本 裕士, 教授 大森 孝一, 教授 上杉 志成 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

<p> A model of two predators competing for the same prey also involving predation interaction between the two predators is considered. Coexistence in forms of equilibria and periodic orbits is obtained by using bifurcation and dynamical systems theory. Global dynamics is obtained by studying the survival functions and persistence is obtained by using a theorem of Freedman and Waltman. Finally, numerical results for a specific example demonstrate the above. A Hopf bifurcation at the interior equilibrium and its unstable periodic orbit are observed.</p> / Thesis / Master of Science (MSc)

No / The recent development of information techniques, especially the state-of-the-art “big data” solutions, enables the extracting, gathering, and processing large amount of toxicity information from multiple sources. Facilitated by this technology advance, a framework named integrated testing strategies (ITS) has been proposed in the predictive toxicology domain, in an effort to intelligently jointly use multiple heterogeneous toxicity data records (through data fusion, grouping, interpolation/extrapolation etc.) for toxicity assessment. This will ultimately contribute to accelerating the development cycle of chemical products, reducing animal use, and decreasing development costs. Most of the current study in ITS is based on a group of consensus processes, termed weight of evidence (WoE), which quantitatively integrate all the relevant data instances towards the same endpoint into an integrated decision supported by data quality. Several WoE implementations for the particular case of toxicity data fusion have been presented in the literature, which are collectively studied in this paper. Noting that these uncertainty handling methodologies are usually not simply developed from conventional probability theory due to the unavailability of big datasets, this paper first investigates the mathematical foundations of these approaches. Then, the investigated data integration models are applied to a representative case in the predictive toxicology domain, with the experimental results compared and analysed.

Caulobacter crescentus goes through a classic dimorphic cell division cycle to adapt to the stringent environment and reduce intraspecific competition. Caulobacter mother cell gives rise to two progenies with distinct morphology - a motile swarmer cell equipped with a flagellum and a sessile stalked cell equipped with a stalk. Because of the nature of dimorphic lifestyle, Caulobacter becomes a model bacterium to study the cell differentiation, signalling transduction, stress response, and asymmetry development of prokaryotes. The dimorphic cell cycle of Caulobacter is driven by the elaborate spatiotemporal organization of regulatory molecules through regulations of synthesis, degradation, phosphorelay, and localization. There is a wealth of experimental observations about gene/protein interactions and localizations accumulated in recent decades, while several mathematical models have been proposed to study the cell cycle progression in Caulobacter. However, the specific control mechanisms of stress response and spatial asymmetry establishment are yet clearly elucidated, while these mechanisms are of fundamental importance to understanding the bacterial survival strategy and developing the microbial industry. Here we utilize mathematical modeling to study the regulatory network of cell cycle control in C. crescentus, focusing on the stress response and asymmetry development. First, we investigate the starvation response of Caulobacter through the connection of phosphotransferase systems (PTS) and guanine nucleotide-based second messenger system. We have developed a mathematical model to capture the temporal dynamics of vital regulatory second messengers, c-di-GMP (cdG) and guanosine pentaphosphate or tetraphosphate (pppGpp or ppGpp), under normal and stressful conditions. This research suggests that the RelA-SpoT homolog enzymes have the potential to effectively influence the cell cycle in response to nutrition changes by regulating cdG and (p)ppGpp levels. We further integrate the second messenger network into a temporal cell cycle model to investigate molecular mechanisms underlying responses of Caulobacter to nutrition starvation. Our model suggests that the cdG-relevant starvation signal is essential but not sufficient to robustly arrest the cell cycle of Caulobacter. We also demonstrate that there may be unknown pathway(s) reducing CtrA under starvation conditions, which results in delayed cytokinesis in starved stalked cells. The cell cycle development of Caulobacter is determined by the periodical activation and deactivation of the master regulator CtrA. cdG is an essential component of the ClpXP pro- tease complex, which is specifically responsible for the degradation of CtrA. We propose a mathematical model for the hierarchical assembly of ClpXP complexes, together with modeling DNA replication, transcription, and protein interactions, to characterize the Caulobacter cell cycle. Our model suggests that the ClpXP-based proteolysis system contributes to the timing and robustness of the cell cycle progression. Furthermore, we construct a spatiotemporal model with Turing-pattern mechanism to study the morphogenesis and asymmetry establishment during the cell cycle of Caulobacter. We apply reaction-diffusion equations to capture the spatial dynamics of scaffolding proteins PodJ, PopZ, and SpmX, which organize two distinct poles of Caulobacter. The spatial regulations influence the activity and distribution of key cell cycle regulators, governing the dimorphic lifestyle of Caulobacter. Our model captures major spatiotemporal experimental observations of wild-type and mutant cells. It provides predictions of novel mutant strains and explains the spatial regulatory mechanisms of bacterial cell cycle progression. / Doctor of Philosophy / Cell is the basic unit of life that undergoes a process called 'cell cycle' consisting of DNA replication and cell division to exhibit various functions, abilities, and behaviors. The cell cycle is well organized by complex regulations in time and space that determine when and where changes take place. The regulations behind cell cycle development play important roles for living organisms but are not fully understood. In this dissertation, we utilize mathematical models and focus on a model bacterium, Caulobacter crescentus, to capture characteristics of cell cycle and study the underlying regulations. Caulobacter is widely distributed in freshwater, including environments with poor nutrients. It divides asymmetrically, generating a pair of daughter cells with different appearances and replicative potentials. Therefore, Caulobacter population has the flexibility to save energy by halting DNA replication and to reduce the competition with siblings by settling into different places. We utilize the nature of the asymmetrical division of Caulobacter to quantitatively investigate the control mechanisms of cell cycle development, including how cells detect and respond to external cues and develop different organelles at specific times and locations.

No / Achieving an environmentally friendly fuel with respect to minimum sulfur compounds has recently became a significant issue for petroleum refining industries. This paper focuses on investigating oxidative desulfurization (ODS) process for removal of sulfur compounds found in light gas oil (LGO) in a batch reactor (at different reaction temperatures and batch time) using a novel nano-catalyst based on 4% iron oxide (Fe2O3) as an active component. Precipitation and Impregnation methods are used to prepare the nano-gamma alumina (γ-alumina) and to generate the new synthetic homemade nanocatalyst. A mathematical model is formulated for the ODS process to estimate the optimal kinetic parameters within gPROMS package. An excellent consistency with the experimental data of all runs with error less than 5% have obtained. The optimization results display that the new nanocatalyst prepared here is effective in removing more than 97% of the sulfur compounds from LGO resulting in a cleaner fuel. / The authors thank Petroleum Research and Development Center, The Iraqi Ministry of Oil /Baghdad, IRAQ for its financial support.

Yes / The oxidative desulphurization (ODS) of light gas oil (LGO) is investigated with an in-house designed cobalt 11 oxide loaded on alumina (γ-Al2O3) catalyst in the presence of air as oxidizing agent under moderate operating 12 conditions (temperature from 403 to 473 K, LHSV from 1 to 3 hr-1, initial concentration from 500 to 1000 13 ppm). Incipient Wetness Impregnation method (IWI) of cobalt oxide over gamma alumina (2% Co3O4/γ-14 Al2O3) is used for the preparation of the catalyst. The optimal design of experiments is studied to evaluate the 15 effects of a number of process variables namely temperature, liquid hourly space velocity (LHSV) and 16 concentration of dibenzothiophene and their optimal values were found to be 473 K, 1hr-1 and 1000 ppm 17 respectively. For conversion dibenzothiophene to sulphone and sulphoxide, the results indicates that the 18 Incipient Wetness Impregnation (IWI) is suitable to prepare this type of the catalyst. Based on the 19 experiments, mathematical models that represent a three phase reactor for describing the behavior of the ODS 20 process are developed. 21 In order to develop a useful model for simulation, control, design and scale-up of the oxidation process, 22 accurate evaluation of important process parameters such as reaction rate parameters is absolutely essential. 23 For this purpose, the parameter estimation technique available in gPROMS (general Process Modelling 24 System) software is employed in this work. With the estimated process parameters further simulations of the 25 process is carried out and the concentration profiles of dibenzothiophene within the reactor are generated.

Yes / Several new homemade nano-catalysts are prepared here to reduce sulfur compound found in light gas oil (LGO) utilizing the adsorption desulfurization technique. The effect of different support materials (Fe2O3, Cr2O3 and CdO) having the same particle size (20 nm) on the adsorptive desulfurization performance for loading 5% nickel sulfate (5 wt%NiO) as an active component for each catalyst, is studied. Oxidative desulfurization process (ODS) in a novel digital baffle batch reactor (DBBR) is used to evaluate the performance of the catalysts prepared. Moderate operating conditions are employed for the ODS process. The efficient new nano-catalysts with for the removal of sulfur are found to be 93.4%, 85.6% and 62.1% for NiO/Fe2O3, NiO/Cr2O3 and NiO/CdO, respectively at 175 deg C, 75 min and 2 ml of H2O2. The best kinetic model and the half-live period for the nano-catalysts related to the relevant reactions have also been investigated here.

Yes / Electricity providers aims to deliver uninterrupted electrical services to their customers at minimum cost while providing a satisfactory quality service. Therefore, the power system reliability is essential in power distribution network planning, design, and operation. This paper proposes a novel mathematical model to improve the reliability of reconfigurable distribution networks via investing and operating tie-lines. While the failure is being repaired, tie-lines allow the network operator to transfer loads from failed zones to healthy zones. Constructing new tie-lines could improve the network’s flexibility, aiming to reduce the cost of expected energy not supplied (EENS). The objective function of the proposed method is a trade-off between the investment cost of tie-lines construction in the planning stage, the cost of tie-lines operation (e.g., opening/closing), and the cost of EENS in the operational stage. The model simultaneously evaluates the best combination of investments and network configuration for each contingency while considering network constraints. A multistage mathematical model is developed as mixed-integer linear programming (MILP) to overcome the computational complexity and maintain solver traceability for utility-scale realistic networks. The model can handle the network reconfiguration (NR) considering N-x contingency analysis in the operation stage while deciding the investment in tie-lines in the planning stage. The optimal investment and operation in tie-lines, according to numerical results, can reduce the cost of the Distribution System (DS) while responding with contingencies by 51 to 70%.

In each individual cell, there are many signaling pathways that may interact or cross talk with each other. Especially, some can sense the same signal and go through different pathways but eventually converge at some points. Therefore repetitive signal stimulations may result in intricate cell responses, among which the priming effect has been extensively studied in monocytes and macrophages as it plays an unambiguously crucial role in immunological protection against pathogen infection. Priming basically describes the phenomena that host cells can launch a dramatically enhanced response to the second higher dose of stimulus if cells have been previously treated with a lower dose of identical stimulus. It was reported to be associated with many human immune diseases (such as rheumatoid arthritis and hepatitis) that are attracting more and more researches on the priming effect. It is undoubtable that many genes are involved in this complicated biological process. Microarray is one of the standard techniques that are applied to do the transcriptome profiling of cells under repetitive stimuli and reveal gene regulatory networks. Therefore a well-established pipeline to analyze microarray data is of special help to investigate the underlying mechanism of priming effect. In this research, we aimed to design a strategy that can be used to interpret microarray data and to propose gene candidates that potentially participate in priming effect. To confirm our analysis results, we used a detailed mathematical model to further demonstrate the mechanism of a specific case of priming effect in a computational perspective. / Master of Science