Le thème des travaux qui fait l'objet de ce mémoire de thèse s'inscrit dans le cadre de la caractérisation de systèmes biologiques par modèles non entiers. Cette thèse comporte deux parties qui reposent sur deux collaborations distinctes. La première s'appuie sur une collaboration avec le laboratoire Mouvement Adaptation Cognition de l'Université Bordeaux 2 et l'institut Magendie de l'Inserm. L'objectif de ce travail consiste à étudier l'influence la longueur du muscle sur sa dynamique dans les cas de variations statiques et dynamiques de cette grandeur. La deuxième collaboration est un projet original, en partenariat avec l'équipe Anesthésiologie-Réanimation II du CHU Haut-Lévêque ayant pour but l'identification de transfert thermique dans le poumon au cours d'opération à cœur ouvert, grâce à des mesures obtenues sur des poumons de mouton. / This PhD thesis deals with biological system characterization using fractional models. This study is divided in two parts stemming from two different cooperations. The first one involves the laboratoire Mouvement Adaption Cognition of Université Bordeaux 2 and the Institut Magendie of Inserm. The aim of this teamwork is to study the muscle length effect on its dynamic, considering static and dynamical length variations. The second collaboration involves the Anesthésiologie-Réanimation team of CHU Haut-Lévêque from Bordeaux. This research work aims at identifying models of thermal transfer inside the lungs during open-heart surgery.

Living cells and their components play a key role within biotechnology industry. Cell cultures and their products of interest are used for the design of vaccines as well as in the agro-alimentary field. In order to ensure optimal working of such bioprocesses, the understanding of the complex mechanisms which rule them is fundamental. Mathematical models may be helpful to grasp the biological phenomena which intervene in a bioprocess. Moreover, they allow prediction of system behaviour and are frequently used within engineering tools to ensure, for instance, product quality and reproducibility.<p> <p>Mathematical models of cell cultures may come in various shapes and be phrased with varying degrees of mathematical formalism. Typically, three main model classes are available to describe the nonlinear dynamic behaviour of such biological systems. They consist of macroscopic models which only describe the main phenomena appearing in a culture. Indeed, a high model complexity may lead to long numerical computation time incompatible with engineering tools like software sensors or controllers. The first model class is composed of the first principles or white box models. They consist of the system of mass balances for the main species (biomass, substrates, and products of interest) involved in a reaction scheme, i.e. a set of irreversible reactions which represent the main biological phenomena occurring in the considered culture. Whereas transport phenomena inside and outside the cell culture are often well known, the reaction scheme and associated kinetics are usually a priori unknown, and require special care for their modelling and identification. The second kind of commonly used models belongs to black box modelling. Black boxes consider the system to be modelled in terms of its input and output characteristics. They consist of mathematical function combinations which do not allow any physical interpretation. They are usually used when no a priori information about the system is available. Finally, hybrid or grey box modelling combines the principles of white and black box models. Typically, a hybrid model uses the available prior knowledge while the reaction scheme and/or the kinetics are replaced by a black box, an Artificial Neural Network for instance.<p><p>Among these numerous models, which one has to be used to obtain the best possible representation of a bioprocess? We attempt to answer this question in the first part of this work. On the basis of two simulated bioprocesses and a real experimental one, two model kinds are analysed. First principles models whose reaction scheme and kinetics can be determined thanks to systematic procedures are compared with hybrid model structures where neural networks are used to describe the kinetics or the whole reaction term (i.e. kinetics and reaction scheme). The most common artificial neural networks, the MultiLayer Perceptron and the Radial Basis Function network, are tested. In this work, pure black box modelling is however not considered. Indeed, numerous papers already compare different neural networks with hybrid models. The results of these previous studies converge to the same conclusion: hybrid models, which combine the available prior knowledge with the neural network nonlinear mapping capabilities, provide better results.<p><p>From this model comparison and the fact that a physical kinetic model structure may be viewed as a combination of basis functions such as a neural network, kinetic model structures allowing biological interpretation should be preferred. This is why the second part of this work is dedicated to the improvement of the general kinetic model structure used in the previous study. Indeed, in spite of its good performance (largely due to the associated systematic identification procedure), this kinetic model which represents activation and/or inhibition effects by every culture component suffers from some limitations: it does not explicitely address saturation by a culture component. The structure models this kind of behaviour by an inhibition which compensates a strong activation. Note that the generalization of this kinetic model is a challenging task as physical interpretation has to be improved while a systematic identification procedure has to be maintained.<p><p>The last part of this work is devoted to another kind of biological systems: proteins. Such macromolecules, which are essential parts of all living organisms and consist of combinations of only 20 different basis molecules called amino acids, are currently used in the industrial world. In order to allow their functioning in non-physiological conditions, industrials are open to modify protein amino acid sequence. However, substitutions of an amino acid by another involve thermodynamic stability changes which may lead to the loss of the biological protein functionality. Among several theoretical methods predicting stability changes caused by mutations, the PoPMuSiC (Prediction Of Proteins Mutations Stability Changes) program has been developed within the Genomic and Structural Bioinformatics Group of the Université Libre de Bruxelles. This software allows to predict, in silico, changes in thermodynamic stability of a given protein under all possible single-site mutations, either in the whole sequence or in a region specified by the user. However, PoPMuSiC suffers from limitations and should be improved thanks to recently developed techniques of protein stability evaluation like the statistical mean force potentials of Dehouck et al. (2006). Our work proposes to enhance the performances of PoPMuSiC by the combination of the new energy functions of Dehouck et al. (2006) and the well known artificial neural networks, MultiLayer Perceptron or Radial Basis Function network. This time, we attempt to obtain models physically interpretable thanks to an appropriate use of the neural networks.<p> / Doctorat en sciences appliquées / info:eu-repo/semantics/nonPublished

Les fonctions biologiques sont le résultat de l'interaction de beaucoup de processus, avec différents objectifs, complexités, niveaux de hiérarchie, et changements de conditions que modifient le comportement de systèmes. Nous utilisons des équations différentielles ou dynamiques plus générales, et systèmes stochastiques de transition pour décrire la dynamique de changements des modèles. La composition, réconciliation et réutilisation des modèles nous permettent d'obtenir des descriptions de systèmes biologiques complètes et compatibles et leur combiner. Notre spécification de systèmes hybrides avec BioRica assure l'intégrité de modèles, et implémente notre approche. Nous appliquons notre approche pour décrire in-silico deux systèmes: la dynamique de la fermentation du vin, et des décisions cellulaires associées à la formation de tissu d'os. / The main aim of this thesis is to develop an approach that allows us to describe biological systems with theoretical sustenance and good results in practice. Biological functions are the result of the interaction of many processes, that connect different hierarchy levels going from macroscopic to microscopic level. Each process works in different way, with its own goal, complexity and hierarchy level. In addition, it is common to observe that changes in the conditions, such as nutrients or environment, modify the behavior of the systems. So, to describe the behavior of a biological system over time, it is convenient to combine different types of models: continuous models for gradual changes, discrete models for instantaneous changes, deterministic models for completely predictable behaviors, and stochastic or non- deterministic models to describe behaviors with imprecise or incomplete information. In this thesis we use the theory of Composition and Hybrid Systems as basis, and the BioRica framework as tool to model biological systems and analyze their emergent properties in silico.With respect to Hybrid Systems, we considered continuous models given by sets of differential equations or more general dynamics. We used Stochastic Transition Systems to describe the dynamics of model changes, allowing cofficient switches that control the parameters of the continuous model, and strong switches that choose different models. Composition, reconciliation and reusing of models allow us to build complete and consistent descriptions of complex biological systems by combining them. Compositions of hybrid systems are hybrid systems, and the refinement of a model forming part of a composed system results in a refinement of the composed system. To implement our approach ideas we complemented the theory of our approach with the improving of the BioRica framework. We contributed to do that giving a BioRica specification of Hybrid Systems that assures integrity of models, allowing composition, reconciliation, and reuse of models with SBML specification.We applied our approach to describe two systems: wine fermentation kinetics, and cell fate decisions leading to bone and fat formation. In the case of wine fermentation, we reused known models that describe the responses of yeasts cells to different temperatures, quantities of resources and toxins, and we reconciled these models choosing the model with best adjustment to experimental data depending on the initial conditions and fermentation variable. The resulting model can be applied to avoid process problems as stuck and sluggish fermentations. With respect to cell fate decisions the idea is very ambitious. By using accurate models to predict the bone and fat formation in response to activation of pathways such as the Wnt pathway, and changes of conditions affecting these functions such as increments in Homocysteine, one can analyze the responses to treatments for osteoporosis and other bone mass disorders. We think that here we are giving a first step to obtain in silico evaluations of medical treatments before testing them in vitro or in vivo.

Deux systèmes biologiques distincts, les muscles squelettiques et les sites d'adhésion de cellules kératocytes en mouvement, sont considérés dans un même cadre en raison de la similitude profonde de leur structure et de leur fonctionnalité. La réponse passive de l'un et de l'autre peut être modélisée à l'aide d'un grand nombre d'unités multi-stables couplées par des interactions à longue portée, et exposées à un désordre spatial fixé et un bruit thermique/mécanique. Les interactions à longue portée dans de tels systèmes conduisent à une synchronisation malgré les fluctuations temporelles et spatiales. Bien que les deux systèmes biologiques considérés présentent des différences structurelles importantes, nous montrons que l'on peut identifier une structure de verre de spin sous-jacente commune. À la lumière de cette analogie, ces systèmes vivants semblent être proches de points critiques et, à cet égard, le désordre gelé, reflétant l’incommensurabilité stérique des unités parallèles, peut être fonctionnel. Un autre paramètre important fixant la réponse est la rigidité interne du système qui couple les unités entre elles. / Two biological systems, a half-sarcomere of a skeletal muscle and an adhesive cluster of a crawling keratocyte, are considered in parallel because of the deep similarity in their structure and functionality. Their passive response can be modeled by a large number of multi-stable units coupled through long-range interactions, frustrated by quenched disorder and exposed to thermal noise. In such systems, long-range interactions lead to synchronization, defying temporal and spatial fluctuations. We use a mean-field description to obtain analytic results and elucidate the remarkable ensemble-dependence of the mechanical behavior of such systems in the thermodynamic limit. Despite important structural differences between muscle cross-bridges and adhesive binders, one can identify a common underlying spin glass structure, which we fully exploit in this work. Our study suggests that the muscle machinery is fine-tuned to operate near criticality, and we argue that in this respect the quenched disorder, reflecting here steric incommensuration, may be functional. We use the analogy between cell detachment and thermal fracture of disordered solids to study the statistics of fluctuations during cellular adhesion. We relate the obtained results to recent observations of intermittent behavior involved in cell debonding, also suggesting near-criticality. In addition to the study of the equilibrium properties of adhesive clusters, we also present the first results on their kinetic behavior in the presence of time-dependent loading.

La RMN du solide a récemment émergé en tant que technique très puissante en biologie structurale, permettant de caractériser au niveau atomique des systèmes qui ne peuvent être étudiés par d’autres méthodes. Des protocoles spécifiques à la RMN du solide sont à présent bien établis pour la préparation des échantillons, l’attribution des spectres et l’acquisition de contraintes structurales. Ensemble, ces protocoles ont ouvert la voie aux premières déterminations de structures tridimensionnelles de molécules biologiques à l’état solide avec une résolution atomique, et ce non seulement pour des échantillons protéiques microcristallins, mais également pour des systèmes plus complexes tels que des fibrilles ou des protéines membranaires.La détermination structurale de tels systèmes est cependant encore loin d’être une routine, et des avancées de plus large ampleur sont attendues grâce à des développements aux niveaux méthodologique et matériel. Pour cette raison, une majeure partie du travail présenté dans cette thèse a été consacrée au développement d’expériences à la fois nouvelles et sophistiquées pour améliorer la sensibilité et la résolution des méthodes déjà existantes pour attribuer les spectres et élargir les possibilités offertes par la RMN du solide en vue d’étudier la structure de systèmes protéiques plus larges. Ces développements reposent notamment sur l’utilisation de champs magnétiques très intenses et sur la rotation des échantillons à l’angle magique dans la gamme des très hautes vitesses angulaires. Nous montrons que dans ce cadre, il est possible de concevoir des expériences utilisant uniquement des champs radiofréquences à faible puissance ainsi que d’utiliser des transferts sélectifs, l’acquisition de corrélations à travers les liaisons chimiques et la détection proton.En particulier, nous montrons que des expériences de corrélation homonucléaire reposant sur des transferts scalaires deviennent une alternative compétitive aux expériences basées sur des transferts dipolaires. Deux nouvelles séquences d’impulsion permettant de détecter des corrélations 13C-13C à travers les liaisons chimiques avec une excellente résolution sont présentées; couplées à des transferts 15N-13C, elles permettent l’attribution des résonances de la chaîne principale des protéines avec une grande sensibilité.De plus, nous démontrons qu’il est possible d’obtenir des raies très fines pour les résonances de protons dans des protéines complètement protonées à l’état solide grâce à la rotation à l’angle magique à ultra-haute vitesse, sans avoir recours à la deutération. Dans ce contexte, nous avons développé de nouvelles stratégies permettant d’attribuer rapidement et de façon fiable les résonances des spins 1H, 15N, 13CO, 13CA et 13CB dans différentes classes de protéines, ainsi que pour mesurer des contraintes structurales à partir des distances entre protons. L’approche proposée repose sur la haute sensibilité des protons et accélère donc considérablement les processus d’attribution et de détermination structurale des protéines à l’état solide, comme illustré sur la protéine beta-2-microglobuline.Enfin, nous avons appliqué cette nouvelle approche pour réaliser l’attribution et l’étude structurale et fonctionnelle de trois catégories de complexes protéiques: les fibrilles amyloidogènes formées par beta-2-microglobuline, les nucléocapsides du virus de la rougeole, et les nucléocapsides d’Acinetobacter phage205. Nous avons également utilisé la technique de Polarisation Nucléaire Dynamique pour obtenir des informations sur l’ARN des nucléocapsides du virus de la rougeole.Nous considérons que les résultats présentés dans cette thèse représentent une avancée substantielle dans le domaine de la RMN du solide appliquée à la biologie structurale. Grâce aux progrès actuels dans ce domaine, l’impact de la RMN biomoléculaire à l’état solide promet d’augmenter dans les prochaines années. / Solid-state NMR has recently emerged as a key technique in modern structural biology, by providing information at atomic level for the characterization of a wide range of systems that cannot be investigated by other atomic-scale methods. There are now well established protocols for sample preparation, resonance assignment and collection of structural restraints, that have paved the way to the first three-dimensional structure determinations at atomic resolution of biomolecules in the solid state, from microcrystalline samples to fibrils and membrane-associated systems. These determinations are however still far from being routine, and larger breakthroughs are expected with further methodological and hardware developments. Accordingly, most of the work presented in this thesis consists of the development of new, sophisticated NMR experiments to improve the sensitivity and resolution of the currently existing schemes for resonance assignment and to extend the capabilities of solid-state NMR in terms of structural investigation of proteins for the analysis of large substrates. These developments notably rely on the use of very high magnetic fields and ultra-fast magic-angle spinning (MAS). We show the great potential of this particular regime, which enables the use of low-power experiments and the acquisition of selective cross-polarization transfers, through-bond correlations and 1H-detected correlations.In particular, we show that homonuclear correlation experiments based on through-bond transfers become competitive alternatives to dipolar transfer schemes. Two new pulse sequences that detect sensitive and resolved 13C-13C through-bond correlations are introduced, which coupled to 15N-13C dipolar transfer steps provide sensitive routes for protein backbone resonance assignment.Furthermore, we demonstrate that narrow 1H NMR line widths can be obtained for fully protonated proteins in the solid state under ultra-fast MAS, even without perdeuteration. In this context, we have developed new strategies for extensive, robust and expeditious assignments of the 1H, 15N, 13CO, 13CA and 13CB resonances of proteins in different aggregation states, and new schemes for the measurements of site-specific 1H-1H distance restraints. This approach relying on the very high sensitivity of 1H spins remarkably accelerates the processes of assignment and structure determination of proteins in the solid state, as shown by the assignment and de novo structure determination of native beta-2-microglobulin. Finally, we apply this new approach to perform resonance assignment and to study structural and dynamic features of three complex protein aggregates: amyloid fibrils formed by native and D76N beta-2-microglobulin, Acinetobacter phage 205 nucleocapsids and measles virus (MeV) nucleocapsids. We also used Dynamic Nuclear Polarization to obtain the first information about RNA in MeV nucleocapsids.We believe that the results presented in this thesis represent a substantial step forward for solid-state NMR in structural biology. With all the current advances in the field, the impact of biomolecular solid-state NMR is likely to increase in the next years.

Plusieurs types d'expérimentation existent pour étudier et comprendre les systèmes biologiques. Dans ces travaux, nous nous intéressons à la simulation in silico, c'est-à-dire à la simulation numérique de modèles sur un ordinateur. Les systèmes biologiques sont composés d'entités, à la fois nombreuses et variées, en interaction les unes avec les autres. Ainsi, ils peuvent être modélisés par l'intermédiaire de deux approches complémentaires : l'approche population-centrée et l'approche individu-centrée. Face à la multitude et à la variété des phénomènes composant les systèmes biologiques, il nous semble pertinent de coupler ces deux approches pour obtenir une modélisation mixte. En outre, en raison de la quantité conséquente d'informations que représente l'ensemble des entités et des interactions à modéliser, la simulation numérique des systèmes biologiques est particulièrement coûteuse en temps de calcul informatique. Ainsi, dans ce mémoire, nous proposons des solutions techniques de parallélisation permettant d'exploiter au mieux les performances offertes par les architectures multicoeur et multiprocesseur et les architectures graphiques pour la simulation de systèmes biologiques à base de modélisations mixtes. Nous appliquons nos travaux au domaine de la coagulation du sang et plus particulièrement à l'étude de la cinétique biochimique à l'échelle microscopique ainsi qu'à la simulation d'un vaisseau sanguin virtuel. Ces deux applications nous permettent d'évaluer les performances offertes par les solutions techniques de parallélisation que nous proposons, ainsi que leur pertinence dans le cadre de la simulation des systèmes biologiques.