The physical understanding of biological processes such as transcription requires the knowledge of double-stranded DNA (dsDNA) physics. A notable thermo- dynamic property of dsDNA is its denaturation, at the melting temperature, in which it unwinds into two single-stranded DNAs via the formation of denat- uration bubbles (segment of consecutive unpaired base-pairs). The dynamics of denaturation has been studied so far at the base-pair (bp) scale, ignoring conformational chain degrees of freedom. These studies do not explain the very long closure times of 20 to 100 s, measured by Altan-Bonnet et al., of 18 bps long bubbles at room temperature. In this thesis, we study the closure of pre-equilibrated large bubbles, by using Brownian dynamics simulations of two simple DNA coarse- grained models. We show that the closure occurs via two steps: rst, a fast zipping of the initial bubble occurs until a meta-stable state is reached, due to the large bending and twisting energies stored in the bubble. Then, the meta-stable bubble closes either via rotational di usion of the sti side arms until their alignment, or bubble di usion until it reaches the chain end, or locally by thermal activation, depending on the DNA length and elastic moduli. We show that the physical mechanism behind these long timescales is therefore the dynamical coupling between base-pair and chain degrees of freedom.

En 1889, le clinicien Stefen Paget proposa l'hypothèse de " la graine et du terroir " pour expliquer la progression tumorale. La graine - la tumeur - ne peut pousser que dans le bon terroir - le microenvironnement tumoral. Durant le développement du cancer, la tumeur et ce microenvironnement sont en constant dialogue biochimique. Nous nous sommes intéressés durant cette thèse à l'hypothèse de Paget d'un point de vue mécanique : le terroir mécanique peut-il influencer la progression tumorale ? Nous avons choisi une approche in vitro afin de découpler les influences biochimiques et mécaniques. Nous avons travaillé avec des sphéroïdes multicellulaires, petites boules de cellules tumorales qui imitent remarquablement bien une tumeur. Nous avons développé une méthodologie nous permettant d'exercer des contraintes mécaniques sur des sphéroïdes, et nous avons étudié l'influence de telles contraintes. Nous avons montré qu'une contrainte mécanique influence le développement tumoral. Une contrainte mécanique réduit fortement la croissance d'un sphéroïde, et ce de manière réversible. Le volume des cellules au centre est diminué, ce qui semble déclencher un arrêt de la prolifération. Nous avons également montré que la division cellulaire est source de flux cellulaires à l'intérieur d'un sphéroïde, et que ces flux peuvent être inhibés par l'application d'une contrainte. Enfin, nous avons observé qu'une contrainte mécanique peut promouvoir l'envahissement d'une matrice de collagène par un sphéroïde. En conclusion, qu'un dialogue mécanique restreigne la progression tumorale ou la promeuve, il en est un élément intrinsèque.

During development, cells consume energy, divide, rearrange, and die. Bulk properties such as viscosity and elasticity emerge from cell-scale mechanics and dynamics. Order appears, for example in patterns of hair outgrowth, or in the predominately hexagonal pattern of cell boundaries in the wing of a fruit fly. In the past fifty years, much progress has been made in understanding tissues as living materials. However, the physical mechanisms underlying tissue-scale behaviour are not completely understood. Here we apply theories from statistical physics and fluid dynamics to understand mechanics and order in two-dimensional tissues. We restrict our attention to the mechanics and dynamics of cell boundaries and vertices, and to planar polarity, a type of long-ranged order visible in anisotropic patterns of proteins and hair outgrowth. Our principle tool for understanding mechanics and dynamics is a vertex model where cell shapes are represented using polygons. We analytically derive the ground-state diagram of this vertex model, finding it to be dominated by the geometric requirement that cells be polygons, and the topological requirement that those polygons tile the plane. We present a simplified algorithm for cell division and growth, and furthermore derive a dynamic equation for the vertex model, which we use to demonstrate the emergence of quasistatic behaviour in the limit of slow growth. All our results relating to the vertex model are consistent with and build off past calculations and experiments. To investigate the emergence of planar polarity, we develop quantification methods for cell flow and planar polarity based on confocal microscope images of developing fly wings. We analyze cell flow using a velocity gradient tensor, which is uniquely decomposed into terms corresponding to local compression, shear, and rotations. We argue that a pattern in an inhomogeneously flowing tissue will necessarily be reorganized, motivating a hydrodynamic theory of polarity reorientation. Using such a coarse-grained theory of polarity reorientation, we show that the quantified patterns of shear and rotation in the wing are consistent with the observed polarity reorganization, and conclude that cell flow reorients planar polarity in the wing of the fruit fly. Finally, we present a cell-scale model of planar polarity based on the vertex model, unifying the themes of this thesis.

L'hadronthérapie est une technique de traitement de cancer basée sur l'utilisation de particules lourdes chargées. Les caractéristiques physiques de ces particules permettent un ciblage plus précis des tumeurs et une efficacité biologique supérieure à celle des photons et des électrons. Ce travail de thèse traite la problématique de la modélisation des effets biologiques induits par ce type de particules. Une partie de ce travail est consacrée à l'analyse de l'outil de simulation Monte‐Carlo " Geant4 ", utilisé pour simuler la phase physique de l'interaction des particules avec le milieu biologique. Nous avons évalué la capacité de " Geant4 " à simuler la distribution microscopique des dépôts d'énergie des particules chargées et confronté ces résultats à ceux d'un autre code de simulation dédié aux applications de radiobiologie. L'autre partie du travail est dédiée à l'étude de deux modèles radiobiologiques basés sur deux approches différentes qui sont le modèle LEM (Local Effect Model) basé sur une approche de trace amorphe et le modèle MKM (Microdosimetric Kinetic Model) basé sur une approche microdosimétrique. Une analyse théorique des deux modèles est effectuée ainsi qu'une comparaison de leurs concepts. Ensuite, nous nous sommes focalisé sur le modèle microdosimétrique " MKM " que nous avons analysé de manière plus approfondie. Enfin, nous avons appliqué le modèle MKM pour reproduire les résultats expérimentaux d'irradiation cellulaire obtenus au GANIL avec des ions carbone et argon sur des cellules tumorales (lignées SCC61 et SQ20B) de radiosensibilité différente.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第23929号 / 工博第5016号 / 新制||工||1783(附属図書館) / 京都大学大学院工学研究科化学工学専攻 / (主査)教授 山本 量一, 教授 宮原 稔, 教授 安達 泰治 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

In the mammalian embryo, between 3.5 and 4.5 days after fertilisation, the cells of the inner cell mass evolve from a uniform aggregate to an ordered structure with two distinct tissue layers - the primitive endoderm and epiblast. It was originally assumed that cells differentiated to form these layers in situ, but more recent evidence suggests that both cell types arise scattered throughout the inner cell mass, and it is thus proposed that the tissue layers self-organise by physical mechanisms after the specification of the two cell types. We have developed a computational model based on the subcellular element method to combine theoretical and experimental work and elucidate the mechanisms that drive this self-organisation. The subcellular element method models each cell as a cloud of infinitesimal points that interact with their nearest neighbours by local forces. Our method is built around the introduction of a tensile cortex in each cell by identifying boundary elements and using a Delaunay triangulation to define a network of forces that act within this boundary layer. Once the cortex has been established, we allow the tension in the network to vary locally at interfaces, modelling the exclusion of myosin at cell-cell interfaces and consequent reduction in tension. The model is validated by testing the simulated interfaces in cell doublets and comparing to experimental data and previous theoretical work. Furthermore, we introduce dynamic tension to model blebbing in primitive endoderm cells. We investigate the effects of cortical tension, differential interfacial tension, and blebbing on interfaces, rearrangement, and sorting. By establishing quantitative measurements of sorting we produce phase diagrams of sorting magnitude given system parameters and find that robust sorting in a 30 cell aggregate is best achieved by a combination of differential interfacial tension and blebbing.

<p>Intracellular motions are important signatures of living tissues, and intracellular dynamics reflect overall cell function and health. Traditional microscopy methods can track 2D cellular motions but do not provide an ensemble evaluation of intracellular activity. Biodynamic imaging (BDI) is a unique 3D imaging technique based on the phase shifts of dynamic light scattering and is highly sensitive to intracellular dynamics in living cells and their changes. This makes BDI a versatile tool to evaluate many different types of samples under various scenarios, and BDI has the potential to improve patient diagnosis and to provide valuable information for health care research. This may include evaluating sample activity, profiling patient chemotherapy response, and studying drug mechanisms. This thesis discusses the theory and modeling of BDI, the construction of BDI systems, sample heterogeneity analysis (TDSI), and the use of BDI to study cytoskeletal drug mechanisms, improve embryo selection and select therapies in pre-clinical trials.</p>

During development, cells consume energy, divide, rearrange, and die. Bulk properties such as viscosity and elasticity emerge from cell-scale mechanics and dynamics. Order appears, for example in patterns of hair outgrowth, or in the predominately hexagonal pattern of cell boundaries in the wing of a fruit fly. In the past fifty years, much progress has been made in understanding tissues as living materials. However, the physical mechanisms underlying tissue-scale behaviour are not completely understood. Here we apply theories from statistical physics and fluid dynamics to understand mechanics and order in two-dimensional tissues. We restrict our attention to the mechanics and dynamics of cell boundaries and vertices, and to planar polarity, a type of long-ranged order visible in anisotropic patterns of proteins and hair outgrowth. Our principle tool for understanding mechanics and dynamics is a vertex model where cell shapes are represented using polygons. We analytically derive the ground-state diagram of this vertex model, finding it to be dominated by the geometric requirement that cells be polygons, and the topological requirement that those polygons tile the plane. We present a simplified algorithm for cell division and growth, and furthermore derive a dynamic equation for the vertex model, which we use to demonstrate the emergence of quasistatic behaviour in the limit of slow growth. All our results relating to the vertex model are consistent with and build off past calculations and experiments. To investigate the emergence of planar polarity, we develop quantification methods for cell flow and planar polarity based on confocal microscope images of developing fly wings. We analyze cell flow using a velocity gradient tensor, which is uniquely decomposed into terms corresponding to local compression, shear, and rotations. We argue that a pattern in an inhomogeneously flowing tissue will necessarily be reorganized, motivating a hydrodynamic theory of polarity reorientation. Using such a coarse-grained theory of polarity reorientation, we show that the quantified patterns of shear and rotation in the wing are consistent with the observed polarity reorganization, and conclude that cell flow reorients planar polarity in the wing of the fruit fly. Finally, we present a cell-scale model of planar polarity based on the vertex model, unifying the themes of this thesis.

In the run-up to biomechanical testing, fresh human tissue samples are often frozen in order to inhibit initial decomposition processes and to achieve a temporal independence of tissue acquisition from biomechanical testing. The aim of this study was to compare the mechanical properties of fresh tissue samples of the human iliotibial tract (IT) to fresh-frozen samples taken from the same IT and those modified with different concentrations of Dimethylsulfoxide (DMSO) prior to freezing. All samples were partial plastinated and destructive tensile tests were conducted with a uniaxial tensile test setup. A plastination technique already established in the laboratory was modified to improve the clamping behaviour of the samples. Material failure was caused by a gradual rupture of the load-bearing collagen fibre bundles. Contrary to our expectations, no significant difference was found between the tensile strength of fresh and fresh frozen specimens. The addition of 1 wt% DMSO did not increase the tensile strength compared to fresh-frozen samples; an addition of 10 wt% DMSO even resulted in a decrease. Based on our findings, the use of simple fresh-frozen specimens to determine the tensile strength is viable; however fresh specimens should be used to generate a complete property profile.

<p dir="ltr">In this dissertation, I use methods of theoretical physics to study principles governing the stochastic dynamics of living timekeepers in a few different contexts. First, focusing on the phenomenon of stochastic growth and division processes in the simplest living organism (the bacterial cell), I present a procedure for analyzing high-throughput, high-precision dynamic datasets to identify emergent simplicities, in particular scaling laws, that provide new insights into a long-standing problem (that of cell size homeostasis). Recasting the question from a stochastic, intergenerational viewpoint (i.e., one that considers the entire life histories of individual cells without recourse to a priori mechanistic assumptions), and taking advantage of identified emergent simplicities to achieve dimensional reduction of the problem, permits a reformulation that captures the inherent stochasticity of individual cells. Identification of discrete modes by which homeostasis is maintained---in particular, via reflexive (elastic) adaptation of cell size and reflective (plastic) adaptation of growth rate---provides important insights into key system constraints that govern living bacterial cells, with additional implications for the design of functional adaptive synthetic homeostats. The observation of non-Markovian dynamics in single-cell growth rates implies the existence of intergenerational memory and plastic adaptation in these simple organisms. I also present my work on the process of early endosomal maturation in human cell lines, multi- fork DNA replication in Escherichia coli cells, and a physics principle and theory predictions for emergent periodicity in a decentralized follow-the-leader dynamic in a collective of randomly signaling agents. This body of work provides mechanistic insights into how temporal organization in outcomes emerges despite the inherently stochastic nature of the constituent dynamics, with each system adopting its own mechanism to achieve this universal goal.</p>