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Une question centrale de la génétique moderne est de mieux comprendre comment la variation génétique présente au sein d’individus d’une même espèce influence la diversité phénotypique et l’évolution. La levure modèle Saccharomyces cerevisiae offre une occasion unique d’apporter des éléments de réponse à cette question à travers la dissection de l’architecture génétique de la variation de tolérance à des stress environnementaux à l’échelle d’une population. Mon étude révèle un niveau supplémentaire de complexité de la relation génotype-phénotype où finalement les caractères supposés les plus simples, dits Mendéliens (déterminisme strictement monogénique) peuvent se révéler être complexes (déterminisme multigénique) selon le fond génétique en raison de l’action de gènes modificateurs, d’interactions épistatiques et/ou de suppresseurs. Toutefois, les processus évolutifs peuvent être bien différents en fonction des espèces. Afin de mieux les décrypter, je me suis également intéressée à Lachancea kluyveri, une levure phylogénétiquement distante de S. cerevisiae. Cette espèce présente une diversité génétique plus élevée et constitue une ressource encore peu exploitée. L’exploration de la diversité phénotypique et la détermination de leurs origines génétiques initiées dans cette étude sont extrêmement prometteuses et apportent de solides fondations pour l’étude à la fois de l’architecture génétique des caractères et de l’évolution de la relation génotype-phénotype au sein de diverses espèces de levures. / A central issue of modern genetics is to better understand how genetic variations between individuals within a species influence the phenotypic diversity and the evolution. The budding yeast Saccharomyces cerevisiae as a model organism offers a unique opportunity to address this issue through the dissection of the genetic architecture of stress tolerance across a population. My study reveals an additional level of complexity of the genotype-phenotype relationship. Indeed, simple Mendelian traits (monogenic determinism) may become more complex (multigenic determinism) depending on genetic background due to the action of modifier genes, epistatic interactions and / or suppressors. However, evolutionary processes can be very different depending on the species. That is why a non-conventional yeast species namely Lachancea kluyveri (formerly S. kluyveri) was also studied. This species distantly related to S. cerevisiae has a higher genetic diversity and remains a relatively unexplored resource. The exploration of the phenotypic diversity and the determination of the genetic origins initiated in this study lay foundations for the analysis of the genetic architecture of traits and the evolution of the genotype-phenotype relationship within diverse yeast species.

Electrospun nanofibers have been researched extensively in the culturing of stem cells to understand their behavior since electrospun fibers mimic the native extracellular matrix (ECM) in many types of mammalian tissues. Here, electrospun nanofibers with defined structure (orientation/alignment) and pore size could significantly modulate human mesenchymal stem cell (hMSC) behavior. Controlling the fiber membrane pore size was predominantly influenced by the duration of electrospinning, while the alignment of the fiber membrane was determined by parallel electrode collector design. Electric field simulation data provided information on the electrostatic interactions in this electrospinning apparatus.hMSCs on small-sized pores (~3-10 µm²) tended to promote the cytoplasmic retention of Yes-associated protein (YAP), while larger pores (~30-45 µm²) promoted the nuclear activation of YAP. hMSCs also displayed architecture-mediated behavior, as the cells aligned along with the fiber membranes orientation. Additionally, fiber membranes affected nuclear size and shape, indicating changes in cytoskeletal tension, which coincided with YAP activity. The mechanistic understanding of hMSC behavior on defined nanofiber structures seeks to advance their translation into more clinical settings and increase biomanufacturing efficiencies.

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The analysis of wide-angle cellular light scattering patterns is a challenging problem. Small changes to the organization, orientation, shape, and optical properties of scatterers and scattering populations can significantly alter their complex two-dimensional scattering signatures. Because of this, it is difficult to find methods that can identify medically relevant cellular properties while remaining robust to experimental noise and sample-to-sample differences. It is an important problem. Recent work has shown that changes to the internal structure of cells---specifically, the distribution and aggregation of organelles---can indicate the progression of a number of common disorders, ranging from cancer to neurodegenerative disease, and can also predict a patient's response to treatments like chemotherapy. However, there is no direct analytical solution to the inverse wide-angle cellular light scattering problem, and available simulation and interpretation methods either rely on restrictive cell models, or are too computationally demanding for routine use. This dissertation addresses these challenges from a computational vantage point. First, it explores the theoretical limits and optical basis for wide-angle scattering pattern analysis. The result is a rapid new simulation method to generate realistic organelle scattering patterns without the need for computationally challenging or restrictive routines. Pattern analysis, image segmentation, machine learning, and iterative pattern classification methods are then used to identify novel relationships between wide-angle scattering patterns and the distribution of organelles (in this case mitochondria) within a cell. Importantly, this work shows that by parameterizing a scattering image it is possible to extract vital information about cell structure while remaining robust to changes in organelle concentration, effective size, and random placement. The result is a powerful collection of methods to simulate and interpret experimental light scattering signatures. This gives new insight into the theoretical basis for wide-angle cellular light scattering, and facilitates advances in real-time patient care, cell structure prediction, and cell morphology research.

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