The Earth’s oblique rotation results in changes in light and temperature across the day and time of year. Living organisms evolved rhythmic behaviours to anticipate these changes and execute appropriate responses at particular times. The current paradigm for the biological clocks in several branches of life is an underlying biochemical oscillator mainly composed by a network of repressive transcription factors. The slow decay in their activity is fundamental for generating anticipatory dynamics. Interestingly, these dynamics can be well appreciated when the biological system is left under constant environmental conditions, where oscillation of several physiological readouts persists with a period close to 24 hours, hence the term “circadian clocks”, circa=around dian=day. In plants the model species Arabidopsis thaliana has served as an invaluable tool for analysing the genetics, biochemical, developmental, and physiological effects of the oscillator. Many of these experimental results have been integrated in mechanistic and mathematical theories for the circadian oscillator. These models predict the timing of gene expression and protein presence in several genetic backgrounds and photoperiodic conditions. The aim of this work is the introduction of a correct mass scale for both the RNA transcript and protein variables of the clock models. The new mass scale is first introduced using published RNA data in absolute units, from qRT-PCR. This required reinterpreting several assumptions of an established clock model (P2011), resulting in an updated version named U2017. I evaluate the performance of the U2017 model in using data in absolute mass units, for the first time for this clock system. Introducing absolute units for the protein variables takes place by generating hypothetical protein data from the existing qRT-PCR data and comparing a data-driven model with western blot data from the literature. I explore the consequences of these predicted protein numbers for the model’s dynamics. The process required a meta-analysis of plant parameter values and genomic information, to interpret the biological relevance of the updated protein parameters. The predicted protein amounts justify, for example, the revised treatment of the Evening Complex in the U2017 model, compared to P2011. The difficulties of introducing absolute units for the protein components are discussed and components for experimental quantification are proposed. Validating the protein predictions required a new methodology for absolute quantification. The methodology is based on translational fusions with a luciferase reporter than has been little used in plants, NanoLUC. Firstly, the characterisation of NanoLUC as a new circadian reporter was explored using the clock gene BOA. The results show that this new system is a robust, sensitive and automatable approach for addressing quantitative biology questions. I selected five clock proteins CCA1, LHY, PRR7, TOC1 and LUX for absolute quantification using the new NanoLUC methodology. Functionality of translation fusions with NanoLUC was assessed by complementation experiments. The closest complementing line for each gene was selected to generate protein time series data. Absolute protein quantities were determined by generation of calibration curves using a recombinant NanoLUC standard. The developed methodology allows absolute quantification comparable to the calibrated qRT-PCR data. These experimental results test the predicted protein amounts and represent a technical resource to understand protein dynamics of Arabidopsis’ circadian oscillator quantitatively. The new experimental, meta-analysis and modelling results in absolute units allows future researchers to incorporate further, quantitative biochemical data.

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L'objet de cette thèse est l'étude du processus métastatique par la confrontation de données in vivo chez la souris avec des modèles mathématiques. Plus précisément, des données longitudinales sur la masse métastatique totale combinées à des données IRM fournissant des informations sur le nombre et la taille des macrométastases ont été confrontées à un modèle décrivant l'évolution de la distribution en tailles des métastases par une équation aux dérivées partielles de populations structurées. La théorie sous-jacente au modèle, décrivant le processus métastatique par des métastases initiées par quelques cellules et croissant indépendamment les unes des autres, s'est révélée incapable de décrire les distributions de tailles métastatiques observées à l'IRM, suggérant la présence de phénomènes non pris en compte dans la théorie \standard" du développement métastatique. Ces résultats nous ont conduit à proposer des hypothèses expliquant les différences de distributions métastatiques entre le modèle et les données. Ces hypothèses ont été étudiées expérimentalement par nos collaborateurs biologistes mais également in silico à l'aide de modèles d'équations aux dérivées partielles décrivant la croissance de plusieurs métastases pouvant interagir spatialement. Les résultats obtenus à l'aide de notre approche de modélisation suggèrent des interactions jouant un rôle important dans la dynamique métastatique, comme l'agrégation de germes métastatiques ou l'attraction de cellules métastatiques par des foyers métastatiques déja existants. Une partie de cette thèse est également dédiée à l'analyse mathématique et numérique du nouveau modèle spatial introduit pour l'étude quantitative précédemment évoquée. Ce modèle mécanique décrit notamment l'effet de la pression sur la prolifération des cellules tumorales. Des résultats de convergence de la méthode numérique utilisée sont présentés, ainsi qu'une confrontation du modèle à des données de croissance de métastases pulmonaires. Enfin, une partie traitant des interactions métastases-microenvironnement est également présentée. Des études récentes ont en effet montré que certaines cellules progénitrices de la lignée hématopoïétique ou encore certaines cellules immunitaires pourraient jouer un r^ole important dans le développement métastatique. Au cours de cette thèse, ce phénomène appelé niche prémétastatique a été étudié dans la littérature biologique puis modélisé mathématiquement afin de mieux comprendre le rôle de cette niche dans la dynamique métastatique. / In this thesis, a quantitative study of the metastatic process in the mouse has been performed thanks to mathematical modeling. Precisely, longitudinal data of the total metastatic burden and MRI data on the macrometastatic size distribution are confronted to a mathematical model describing the metastatic process by the independent growths of metastatic foci starting from one or few cells. This \standard" theory, able to describe the dynamics of the total metastatic burden, is on the other hand unable to describe the observed metastatic size distributions. Indeed, this model predicts many small metastases, whereas the observed metastases are much larger and fewer. In order to explain these differences, we proposed two hypotheses that were not taken into account in the initial theory. In the first one, metastases that are growing in close vicinity could merge, resulting in one larger metastasis. In the second one, metastatic foci could attract arriving circulating tumor cells, resulting also in fewer foci but much larger ones. These hypotheses have been tested experimentally by our biologists collaborators, and in silico thanks to a spatial model of tumor growth. The results of this study show that the previously suggested phenomena could have a substantial impact on the number and the sizes of the metastatic foci during metastatic development. Another part of this thesis is devoted to the numerical and mathematical analysis of the previous spatial model. This model takes into account the effect of the pressure on the proliferation of tumor cells. Numerical convergence of the numerical method that has been used and data assimilation on imaging data of pulmonary metastases are presented. Finally, a last part deals with the interactions between metastasis and its supportive stroma. Recent studies shed light on the implication of hematopoietic progenitors in the formation of a permissive soil in the future metastatic site, a phenomenon so-called premetastatic niche. In this thesis, a mathematical model describing the premetastatic and metastatic dynamics is proposed to study quantitative aspects of this phenomenon.

The ability to adjust behaviors to a particular environment has been well documented across taxa. Our understanding of behavioral plasticity is largely based on experiments in which individuals have a single exposure to an environment. Observed behavioral changes are then traditionally measured in small windows of responsiveness in a single population or species. In this project I investigated the effects of prolonged exposure to predator cues in Gryllodes sigillatus and also tested for the presence of trans-generational effects of this prolonged exposure. I found there were no differences in anti-predator behaviors when measured in subsequent assays compared to control individuals. These results were paired with a comparison of differences in average expressions of behaviors, differences in variances, and behavioral correlations of five closely related cricket species, including G. sigillatus. Our results showed that species differ in average behaviors and plasticity but did not significantly differ in behavioral correlations.

Gene expression is a fundamental process in the cell and is made up of two parts – the information flow from DNA to RNA, and from RNA to protein. Here, we examined specific sub-processes in Escherichia coli gene expression using newly available tools that permit genome-wide analysis. We begin our studies measuring mRNA and protein abundances in single cells by single-molecule fluorescence microscopy, and then focus our attention to studying RNA generation and degradation by high throughput sequencing. The details of the dynamics of gene expression can be observed from fluctuations in mRNA and protein copy numbers in a cell over time, or the variations in copy numbers in an isogenic cell population. We constructed a yellow fluorescent fusion protein library in E. coli and measured protein and mRNA abundances in single cells. At below ten proteins per cell, a simple model of gene expression is sufficient to explain the observed distributions. At higher expression levels, the distributions are dominated by extrinsic noise, which is the systematic heterogeneity between cells. Unlike proteins which can be stable over many hours, mRNA is made and degraded on the order of minutes in E. coli. To measure the dynamics of RNA generation and degradation, we developed a protocol using high throughput sequencing to measure steady-state RNA abundances, RNA polymerase elongation rates and RNA degradation rates simultaneously with high nucleotide-resolution genome-wide. Our data shows that RNA has similar lifetime at all positions throughout the length of the transcript. We also find that our polymerase elongation rates measured in vivo on a chromosome are generally slower than rates measured on plasmids by other groups. Studying nascent RNA will allow further understanding of RNA generation and degradation. To this end, we have developed a labeling protocol with a nucleoside analog that is compatible with high throughput sequencing.

Organisms must constantly face and adapt to environmental change. Although unpredictable events may inevitably impose threats, temporally correlated changes may also provide opportunities from which an organism can profit. An evolutionarily successful microbe must collect enough information to distinguish threats from opportunities. Indeed, for nutrient transport, it is not clear how organisms distinguish one from the other. Fluctuations in nutrient levels can quickly render any transporter's capabilities obsolete. Identifying the environment's dynamic identity is therefore a highly valuable asset for a cell to elicit an accurate physiological response. Recent evidence suggests that the baker's yeast Saccharomyces cerevisiae can exert anticipatory responses to environmental shifts. Nevertheless, the mechanisms by which cells are able to incorporate information from the environment's dynamic features is not understood. A potential source of complex information processing is a highly intricate biochemical network that controls glucose transport. The understanding of this network, however, has revolved around its ability to adjust expression of 17 hexose transporter genes (HXT) to glucose levels. In this thesis, I postulate that instead the glucose sensing network is dynamically controlling the 7 major hexose transporters. By studying transporter dynamics in several scenarios, I provide substantial evidence for this hypothesis. I find that hexose transporters with similar reported affinities (Hxt2 and Hxt4) are robustly allocated to separate stages of growth for multiple initial glucose concentrations. Using single-cell studies, I show that Hxt4 expresses exclusively during glucose downshifts, in contrast with Hxt2. From multiple approaches, I demonstrate that Mig1 is mostly responsible for reporting on the time derivative of glucose, and harnessing it to differentially regulate both transporters. I also provide evidence for the roles of Rgt2 and Std1 in modulating long-term glucose repression of Hxt4. This work extends our ideas on the functionality of transport and gene regulation beyond the established steady-state models. The ability to decode environmental dynamics is likely to be present in other signaling systems and may impact a cell's decision to use fermentation - a decision which is of fundamental interest both for cancer research and for biotechnology.

<p>Michelle N. Ingle. M.S., Purdue University, December 2019. Computational Modeling of Transforming Growth Factor-β2 Receptor Complex Assembly. Major Professor: David M. Umulis.</p> <p> </p> <p> Transforming growth factor (TGF)-β1, TGF-β2, and TGF-β3 are secreted signaling proteins that play an essential role in tissue development, immune response, and physiological homeostasis. TGF-β ligands signal through a tetrameric complex made up of two type I receptors (TβRI) and two type II receptors (TβRII). Dysregulation of TGF-β signaling has been linked to uncontrolled cell proliferation and cancer metastasis. An accurate understanding of TGF-β’s receptor complex assembly pathway may allow for pharmacological intervention and/or preservation of proper TGF-β signaling.</p> <p> Amongst the ligand types, TGF-β1 and TGF-β3 are efficient signalers, presumably by strong binding to both type I and II receptors. However, TGF-β2 has a very weak affinity for TβRII and requires an additional membrane-bound protein called betaglycan (BG) to achieve similar levels of downstream signaling. While computational modeling has been performed on the signaling pathway of the TGF-β system, to date no computational modeling has aimed to decipher BG’s role in the potentiation of TGF-β2 signal. To determine the role of BG in selectively facilitating signaling by TGF-β2, we developed computational models with different assumptions based on the levels of cooperativity between receptor subtypes and types of BG behavior (No Receptor Recruitment model, Single-stage Receptor Recruitment model, and Two-stage Receptor Recruitment model). </p> <p> With each of the receptor recruitment models we hypothesized that BG uses two domains to successfully enhance TGF-β2 signaling. This model was first proposed in Villarreal et al., 2016 and is further investigated in this work using a two-step computational approach. First, a root mean square error (RMSE) calculation was performed between our computational models with no BG present and published experimental signaling data in cell lines with no BG present. Lower RMSE values indicate the simulated data is more representative of experimental signaling behavior when no BG is present. The second round of model validation was performed by adding BG into the simulations and comparing its behavior to experimentally determined and hypothesized behaviors of BG. </p> <p> In summary, the simulations indicate there may be more cooperative receptor recruitment present in the system then stated in literature. Furthermore, it appears that BG binding to TGF-β2 ligand through two domains provides an effective transfer mechanism that can be tuned to control differential signaling between TGF-β ligand subtypes. Experiments were then suggested in order to support or refute one of the models offered in this thesis. For the purpose of uncovering how BG enhances TGF-β2 signaling, the computational work performed in this thesis highlights the areas where researchers should focus their experimental efforts and provides a baseline model for further computational work in the TGF-β system.</p>

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18267号 / 工博第3859号 / 新制||工||1592(附属図書館) / 31125 / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 富田 直秀, 教授 安達 泰治, 教授 井手 亜里 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第22687号 / 医博第4631号 / 新制||医||1045(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 山下 潤, 教授 木村 剛, 教授 浅野 雅秀 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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