This thesis explores the use of computational chemistry and machine learning techniques to aid in the design of Metal-Organic Frameworks (MOFs) for use in postcombustion carbon capture and storage (PoC-CCS). PoC-CCS is an ongoing field of research which aims to selectively remove carbon dioxide, an important greenhouse gas, from the exhaust of fossil-fuel burning powerplants. By using a suite of advanced simulation techniques, high-throughput screenings were performed on thousands of MOFs to study their behaviour in a pressure swing adsorption (PSA) system. To develop a comprehensive picture of a material’s performance, the behaviour of individual gas molecules within the pores of the crystal structures to the material’s performance in industrial scale PSA columns was evaluated. To study the behaviour of individual gas molecules within the pores of a MOF, a new algorithm which can accurately determine the locations of gas binding sites was developed. This algorithm, which relies on probability distributions generated through grand canonical Monte Carlo simulations (GCMC), was optimized for CO2 with the goal of use in high-throughput screening. By tuning the user-controlled parameters for a desired gas, this algorithm, which was named the Guest Atom Localization Algorithm (GALA), was shown to accurately reproduce experimentally determined binding sites while being run in a high-throughput manner with no user intervention. Studying MOFs at the pore or crystal scale in this manner provides valuable insights into the behaviour of gases within the materials. A major shortcoming, however, is the lack of direct insight into the material’s behaviour in industrial systems. Materials scientists and MOF chemists have historically focused on a set of performance metrics measured at this scale; however, no clear connection can be made between such metrics and the performance of that sorbent material in a PSA column. To bridge this gap between MOF chemists and the process engineers studying the PSA systems, a large-scale screening of MOFs was performed using a sophisticated PSA simulator designed to reproduce the performance of an 80 kg PSA column. By supplying isotherms obtained using GCMC simulations to be used as inputs into the PSA simulator, a multi-scale high-throughput screening of MOFs for PoC-CCS was performed for the first time under coal-fired powerplant conditions. This multi-scale screening provided the ideal conditions to study the materials science performance metrics and their relationships to industrial PSA performance. To study this relationship, a series of machine learning and artificial intelligence techniques were employed. The primary goal was to extract important relationships between the materials science and industrial PSA performance metrics, with a secondary goal of developing a predictive model which could be used to accelerate the pace of materials discovery. Through the use of machine learning, several metrics were identified which could be used to predict whether a material could meet the minimum target of 95 % purity of captured CO2, and 90 % removal (or recovery) of CO2 from the flue gas stream. Among them was the isotherm parameters for N2, the most abundant species in the flue gas. This finding was significant as to date the focus among MOF chemists studying the PoC-CCS system was placed primarily on the CO2 metrics, with N2 only implicitly considered when calculating the CO2/N2 selectivity. Although several metrics were identified which could predict the purity and recovery targets, none of the conventional metrics tested could be used to estimate the energetic cost of capture or the size of the capture plant, both important considerations in evaluating the cost of capture. The relationship between N2 binding within the pores of the MOF and its ability to meet the purity-recovery targets was explored using GALA. Using a Tanimoto similarity metric and the ratio of single component and competitive loadings, the CO2 and N2 binding environments were studied. It was determined that when the N2 binding environment was significantly altered by the presence of CO2, the material was more likely to meet the purity-recovery targets. Further analysis found that this change in binding environments was correlated to a reduced N2 uptake in the presence of CO2, implying that the competition for binding sites within the pores of the MOF is an important indicator for the material’s ability to meet the purity-recovery target. For the first time, a direct relationship between the behaviour of individual gas molecules to industrial PSA performance can be reported. Although the PSA simulator used throughout this work has proven to be a powerful tool for materials discovery, several shortcomings still exist. The first is the method used by the simulator to predict the loadings at various points within the column. This method relies on single component isotherm data despite the ability of GCMC to simulate multi-component isotherms. An alternative method to using single component isotherms was proposed which relies on multi-component isotherm data and a linear interpolation model. The existing method was compared to the new proposed interpolation method, and it was found that the loadings predicted using the interpolation method were more accurate. The second shortcoming of the PSA simulator is the computational expense associated with the optimizations. Using the PSA simulator, a single material may take up to a week to be fully optimized on a high-performance computing cluster. To increase the pace of materials discovery, a surrogate model was developed using the data accumulated over the course of the work presented in this thesis. Using artificial neural networks, a suite of models was developed which reproduces the outputs of the PSA simulator and is able to optimize a single MOF in a matter of minutes. This suite of models, known as the Fossil Fuel Combustion for Carbon Capture and Storage (FoCAS) was used to perform a screening of over 4,000 materials.

Les conditions du corps humain ne sont pas reproduites fidèlement par la culture cellulaire traditionnelle en 2D. Dans cette thèse, des cultures cellulaires 3D sont réalisées dans une plateforme microfluidique hautement intégrée. Des cellules mammifères adhérentes sont encapsulées dans des gouttes immobilisées dans un tableau de pièges capillaires à haute densité. Dans chaque goutte, les cellules se réorganisent pour former un unique microtissu 3D et fonctionnel appelé sphéroïde. L'utilisation d'un hydrogel permet d'alonger le temps de culture et de perfuser le tableau avec des solutions aqueuses, par exemple pour de l'immuno-cyto-chimie. Un unique sphéroïde, viable, peut aussi être extrait de cette puce microfluidique. Des données quantitatives sont extraites à haut débit au niveau de la population, du sphéroïde (dizaines de miliers de sphéroïdes) et au niveau cellulaire emph{in situ} (centaines de miliers de cellules) grâce à de l'imagerie de fluorescence et au dévelopement d'un code d'analyse d'image. Une première preuve de concept a été obtenue en démontrant la viabilité, la prolifération et la fonctionalité de sphéroïdes d'hépatocytes et en les corrélant à des paramètres morphologiques. Ensuite, des aggrégats de cellules souches mésenchymales ont été produits et les hétérogénéités spatiales dans l'expression de protéines impliquées dans leurs propriétés thérapeutiques ont été étudiées. Enfin, cette technologie a été encore dévelopée pour permettre d'appliquer des conditions biochimiques différentes dans chaque goutte. La production et la culture de sphéroïdes dans cette plateforme microfluidique peut mener à des dévelopements importants dans beaucoup de domaines tels que l'analyse de la toxicité des médicaments, le criblage de médicaments à haut débit, le traitement personnalisé du cancer, l'ingénierie tissulaire ou la modélisation de maladies. / Conventional 2D cell culture fails to reproduce emph{in vivo} conditions. In this PhD thesis, 3D cell culture is implemented into a highly integrated microfluidic platform. Adherent mammalian cells are encapsulated in droplets immobilized on a high density array of capillary traps called anchors. In each droplet, the cells reorganize into a single functional 3D microtissue called spheroid. The use of an hydrogel allows to extend the culturing time in microdroplets and to perfuse the array with aqueous solutions, for instance for immuno-cyto-chemistry. A single and viable spheroid can also be selectively retrieved from the microfluidic chip. High throughput and quantitative data is extracted at the population, spheroid (tens of thousands of spheroids) and cellular level emph{in situ} (hundreds of thousands of cells) thanks to fluorescent imaging and a custom image analysis software. As a first proof of concept, the viability, proliferation and functionality of hp sh s were demonstrated and correlated with morphological parameters. Drug toxicity experiments were also performed on this liver model. Then, human mesenchymal stem cell aggregates were produced and the spatial heterogeneities of the expression of proteins involved in their therapeutic properties were investigated. Finally, this technology was further developed to enable applying different biochemical conditions in each droplet. The production and culture of spheroids in this microfluidic platform could lead to major advances in many fields such as drug toxicity, high throughput drug screening, personalized cancer treatment, tissue engineering or disease modeling.

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The alarming rise of antibacterial drug resistance and the dwindling supply of novel antibiotics highlight the need for innovative approaches in combating bacterial infections. Traditionally, antibacterial drug discovery campaigns have largely been conducted in rich media. Such growth conditions are not representative of the host environment and render many metabolic pathways, otherwise needed for survival and infection, dispensable. Such pathways have been overlooked in conventional drug discovery campaigns despite their validity as potential antibacterial targets. The work presented in this thesis focuses on the development and validation of a screening strategy for the identification and mechanism of action determination of novel inhibitors of metabolic pathways in bacteria under nutrient-limited conditions. This screen led to the identification of MAC168425, MAC173979 and MAC13772 as inhibitors that target glycine metabolism, p-aminobenzoic acid biosynthesis and biotin biosynthesis, respectively. Moreover, it established this approach as a general platform that can be applied for different organisms with synthetic or natural product libraries. Additional mechanistic studies of the biotin biosynthesis inhibitor, MAC13772, resulted in solving the crystal structure of BioA in complex with MAC13772. Analysis of the co-structure confirmed our proposed mode of inhibition and provided information for strategies for rational drug design. Investigation of the antibacterial activity of MAC13772 revealed its potency against a number of pathogens. Furthermore, we show how MAC13772 acts synergistically with rifampicin in clearing growing mycobacterial cultures. The potential of this inhibitor as a lead for preclinical pharmacokinetic studies and for antibacterial drug development is discussed. We also discuss our current efforts to develop a metabolomic platform for the characterization of novel antibacterials that can be used in concert with our current approach to chart the metabolic response of bacteria to chemical perturbants and to generate testable hypotheses regarding the mode of action of novel inhibitors of bacterial metabolism. / Thesis / Doctor of Philosophy (PhD)

京都大学 / 新制・課程博士 / 博士(医科学) / 甲第24532号 / 医科博第146号 / 新制||医科||10(附属図書館) / 京都大学大学院医学研究科医科学専攻 / (主査)教授 金子 新, 教授 上杉 志成, 教授 寺田 智祐 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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G-protein coupled receptors (GPCRs) play a key role in a broad range of biological processes by binding to a wide variety of signaling molecules, which have resulted in 34% of all FDA-approved drugs which target GPCRs. The human genome encodes for approximately 800 GPCR members of which about 140 non-olfactory receptors remain orphans with an unknown function and endogenous ligand. Despite prolonged efforts to deorphanize the unresolved receptors, they remain orphans until this day. By studying scientific publications, this thesis has clarified the challenges with the deorphanization of GPCRs to explain why there are still so many orphan GPCRs when they have confirmed involvement in so many human disorders.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第24879号 / 医博第5013号 / 新制||医||1068(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 後藤 慎平, 教授 渡邊 直樹, 教授 平井 豊博 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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