Thèses sur le sujet « Cell Mechanics -Stochastic Simulation »

Modelling and simulation are essential to modern research in cell biology. This thesis follows a journey starting from the construction of new stochastic methods for discrete biochemical systems to using them to simulate a population of interacting haematopoietic stem cell lineages. The first part of this thesis is on discrete stochastic methods. We develop two new methods, the stochastic extrapolation framework and the Stochastic Bulirsch-Stoer methods. These are based on the Richardson extrapolation technique, which is widely used in ordinary differential equation solvers. We believed that it would also be useful in the stochastic regime, and this turned out to be true. The stochastic extrapolation framework is a scheme that admits any stochastic method with a fixed stepsize and known global error expansion. It can improve the weak order of the moments of these methods by cancelling the leading terms in the global error. Using numerical simulations, we demonstrate that this is the case up to second order, and postulate that this also follows for higher order. Our simulations show that extrapolation can greatly improve the accuracy of a numerical method. The Stochastic Bulirsch-Stoer method is another highly accurate stochastic solver. Furthermore, using numerical simulations we find that it is able to better retain its high accuracy for larger timesteps than competing methods, meaning it remains accurate even when simulation time is speeded up. This is a useful property for simulating the complex systems that researchers are often interested in today. The second part of the thesis is concerned with modelling a haematopoietic stem cell system, which consists of many interacting niche lineages. We use a vectorised tau-leap method to examine the differences between a deterministic and a stochastic model of the system, and investigate how coupling niche lineages affects the dynamics of the system at the homeostatic state as well as after a perturbation. We find that larger coupling allows the system to find the optimal steady state blood cell levels. In addition, when the perturbation is applied randomly to the entire system, larger coupling also results in smaller post-perturbation cell fluctuations compared to non-coupled cells. In brief, this thesis contains four main sets of contributions: two new high-accuracy discrete stochastic methods that have been numerically tested, an improvement that can be used with any leaping method that introduces vectorisation as well as how to use a common stepsize adapting scheme, and an investigation of the effects of coupling lineages in a heterogeneous population of haematopoietic stem cell niche lineages.

Numerous challenges arise in modeling and simulation as biochemical networks are discovered with increasing complexities and unknown mechanisms. With the improvement in experimental techniques, biologists are able to quantify genes and proteins and their dynamics in a single cell, which calls for quantitative stochastic models for gene and protein networks at cellular levels that match well with the data and account for cellular noise. This dissertation studies a stochastic spatiotemporal model of the Caulobacter crescentus cell cycle. A two-dimensional model based on a Turing mechanism is investigated to illustrate the bipolar localization of the protein PopZ. However, stochastic simulations are often impeded by expensive computational cost for large and complex biochemical networks. The hybrid stochastic simulation algorithm is a combination of differential equations for traditional deterministic models and Gillespie's algorithm (SSA) for stochastic models. The hybrid method can significantly improve the efficiency of stochastic simulations for biochemical networks with multiscale features, which contain both species populations and reaction rates with widely varying magnitude. The populations of some reactant species might be driven negative if they are involved in both deterministic and stochastic systems. This dissertation investigates the negativity problem of the hybrid method, proposes several remedies, and tests them with several models including a realistic biological system. As a key factor that affects the quality of biological models, parameter estimation in stochastic models is challenging because the amount of empirical data must be large enough to obtain statistically valid parameter estimates. To optimize system parameters, a quasi-Newton algorithm for stochastic optimization (QNSTOP) was studied and applied to a stochastic budding yeast cell cycle model by matching multivariate probability distributions between simulated results and empirical data. Furthermore, to reduce model complexity, this dissertation simplifies the fundamental cooperative binding mechanism by a stochastic Hill equation model with optimized system parameters. Considering that many parameter vectors generate similar system dynamics and results, this dissertation proposes a general α-β-γ rule to return an acceptable parameter region of the stochastic Hill equation based on QNSTOP. Different objective functions are explored targeting different features of the empirical data.
Doctor of Philosophy
Modeling and simulation of biochemical networks faces numerous challenges as biochemical networks are discovered with increased complexity and unknown mechanisms. With improvement in experimental techniques, biologists are able to quantify genes and proteins and their dynamics in a single cell, which calls for quantitative stochastic models, or numerical models based on probability distributions, for gene and protein networks at cellular levels that match well with the data and account for randomness. This dissertation studies a stochastic model in space and time of a bacterium’s life cycle— Caulobacter. A two-dimensional model based on a natural pattern mechanism is investigated to illustrate the changes in space and time of a key protein population. However, stochastic simulations are often complicated by the expensive computational cost for large and sophisticated biochemical networks. The hybrid stochastic simulation algorithm is a combination of traditional deterministic models, or analytical models with a single output for a given input, and stochastic models. The hybrid method can significantly improve the efficiency of stochastic simulations for biochemical networks that contain both species populations and reaction rates with widely varying magnitude. The populations of some species may become negative in the simulation under some circumstances. This dissertation investigates negative population estimates from the hybrid method, proposes several remedies, and tests them with several cases including a realistic biological system. As a key factor that affects the quality of biological models, parameter estimation in stochastic models is challenging because the amount of observed data must be large enough to obtain valid results. To optimize system parameters, the quasi-Newton algorithm for stochastic optimization (QNSTOP) was studied and applied to a stochastic (budding) yeast life cycle model by matching different distributions between simulated results and observed data. Furthermore, to reduce model complexity, this dissertation simplifies the fundamental molecular binding mechanism by the stochastic Hill equation model with optimized system parameters. Considering that many parameter vectors generate similar system dynamics and results, this dissertation proposes a general α-β-γ rule to return an acceptable parameter region of the stochastic Hill equation based on QNSTOP. Different optimization strategies are explored targeting different features of the observed data.

Le projet de thèse concerne la construction, la génération et l'identification de modèles continus stochastiques, pour des milieux hétérogènes exhibant des comportements non linéaires. Le domaine d'application principal visé est la biomécanique, notamment au travers du développement d'outils de modélisation multi-échelles et stochastiques, afin de quantifier les grandes incertitudes exhibées par les tissus mous. Deux aspects sont particulièrement mis en exergue. Le premier point a trait à la prise en compte des incertitudes en mécanique non linéaire, et leurs incidences sur les prédictions des quantités d'intérêt. Le second aspect concerne la construction, la génération (en grandes dimensions) et l'identification multi-échelle de représentations continues à partir de résultats expérimentaux limités
This work is concerned with the construction, generation and identification of stochastic continuum models, for heterogeneous materials exhibiting nonlinear behaviors. The main covered domains of applications are biomechanics, through the development of multiscale methods and stochastic models, in order to quantify the great variabilities exhibited by soft tissues. Two aspects are particularly highlighted. The first one is related to the uncertainty quantification in non linear mechanics, and its implications on the quantities of interest. The second aspect is concerned with the construction, the generation in high dimension and multiscale identification based on limited experimental data

A complex network of genes and proteins governs the robust progression through cell cycles in the presence of inevitable noise. Stochastic modeling is viewed as a key paradigm to study the effects of intrinsic and extrinsic noise on the dynamics of biochemical networks. A detailed quantitative description of such complex and multiscale networks via stochastic modeling poses several challenges. First, stochastic models generally require extensive computations, particularly when applied to large networks. Second, the accuracy of stochastic models is highly dependent on the quality of the parameter estimation based on experimental observations. The goal of this dissertation is to address these problems by developing new efficient methods for modeling and simulation of stochastic effects in biochemical systems. Particularly, a hybrid stochastic model is developed to represent a detailed molecular mechanism of cell cycle control in budding yeast cells. In a single multiscale model, the proposed hybrid approach combines the advantages of two regimes: 1) the computational efficiency of a deterministic approach, and 2) the accuracy of stochastic simulations. The results show that this hybrid stochastic model achieves high computational efficiency while generating simulation results that match very well with published experimental measurements. Furthermore, a new hierarchical deep classification (HDC) algorithm is developed to address the parameter estimation problem in a monomolecular system. The HDC algorithm adopts a neural network that, via multiple hierarchical search steps, finds reasonably accurate ranges for the model parameters. To train the neural network in the presence of experimental data scarcity, the proposed method leverages the domain knowledge from stochastic simulations to generate labeled training data. The results show that the proposed HDC algorithm yields accurate ranges for the model parameters and highlight the potentials of model-free learning for parameter estimation in stochastic modeling of complex biochemical networks.
Doctor of Philosophy
Cell cycle is a process in which a growing cell replicates its DNA and divides into two cells. Progression through the cell cycle is regulated by complex interactions between networks of genes, transcripts, and proteins. These interactions inside the confined volume of a cell are subject to inherent noise. To provide a quantitative description of the cell cycle, several deterministic and stochastic models have been developed. However, deterministic models cannot capture the intrinsic noise. In addition, stochastic modeling poses the following challenges. First, stochastic models generally require extensive computations, particularly when applied to large networks. Second, the accuracy of stochastic models is highly dependent on the accuracy of the estimated model parameters. The goal of this dissertation is to address these challenges by developing new efficient methods for modeling and simulation of stochastic effects in biochemical networks. The results show that the proposed hybrid model that combines stochastic and deterministic modeling approaches can achieve high computational efficiency while generating accurate simulation results. Moreover, a new machine learning-based method is developed to address the parameter estimation problem in biochemical systems. The results show that the proposed method yields accurate ranges for the model parameters and highlight the potentials of model-free learning for parameter estimation in stochastic modeling of complex biochemical networks.

Fluid velocities and Brownian effects at nanoscales in the near-wall region of microchannels can be experimentally measured in an image plane parallel to the wall using, for example, evanescent wave illumination technique combined with particle image velocimetry [R. Sadr extit{et al.}, J. Fluid. Mech. 506, 357-367 (2004)]. The depth of field of this technique being difficult to modify, reconstruction of the out-of-plane dependence of the in-plane velocity profile remains extremely challenging. Tracer particles are not only carried by the flow, but they undergo random fluctuation imposed by the proximity of the wall. We study such a system under a particle based stochastic approach (Langevin) and a probabilistic approach (Fokker-Planck). The Langevin description leads to a coupled system of stochastic differential equations. Because the simulated data will be used to test a statistical hypothesis, we pay particular attention to the strong order of convergence of the scheme developing an appropriate Milstein scheme of strong order of convergence 1. Based on the probability density function of mean in-plane displacements, a statistical solution to the problem of the reconstruction of the out-of-plane dependence of the velocity profile is proposed. We developed a maximum likelihood algorithm which determines the most likely values for the velocity profile based on simulated perfect particle position, simulated perfect mean displacements and simulated observed mean displacements. Effects of Brownian motion on the approximation of the mean displacements are briefly discussed. A matched particle is a particle that starts and ends in the same image window after a measurement time. AS soon as the computation and observation domain are not the same, the distribution of the out-of-plane distances sampled by matched particles during the measurement time is not uniform. The combination of a forward and a backward solution of the one dimensional Fokker-Planck equation is used to determine this probability density function. The non-uniformity of the resulting distribution is believed to induce a bias in the determination of slip length and is quantified for relevant experimental parameters.

Over the past few years, it has been increasingly recognized that stochastic mechanisms play a key role in the dynamics of biological systems. Genetic networks are one example where molecular-level fluctuations are of particular importance. Here stochasticity in the expression of gene products can result in genetically identical cells in the same environment displaying significant variation in biochemical or physical attributes. This variation can influence individual and population-level fitness. In this thesis we first explore the background required to obtain analytical solutions and perform simulations of stochastic models of gene expression. Then we develop an algorithm for the stochastic simulation of gene expression and heterogeneous cell population dynamics. The algorithm combines an exact method to simulate molecular-level fluctuations in single cells and a constant-number Monte Carlo approach to simulate the statistical characteristics of growing cell populations. This approach permits biologically realistic and computationally feasible simulations of environment and time-dependent cell population dynamics. The algorithm is benchmarked against steady-state and time-dependent analytical solutions of gene expression models, including scenarios when cell growth, division, and DNA replication are incorporated into the modelling framework. Furthermore, using the algorithm we obtain the steady-state cell size distribution of a large cell population, grown from a small initial cell population undergoing stochastic and asymmetric division, to the size distribution of a small representative sample of this population simulated to steady-state. These comparisons demonstrate that the algorithm provides an accurate and efficient approach to modelling the effects of complex biological features on gene expression dynamics. The algorithm is also employed to simulate expression dynamics within 'bet-hedging' cell populations during their adaption to environmental stress. These simulations indicate that the cell population dynamics algorithm provides a framework suitable for simulating and analyzing realistic models of heterogeneous population dynamics combining molecular-level stochastic reaction kinetics, relevant physiological details, and phenotypic variability and fitness.

Cell-extracellular matrix and cell-cell adhesion are essential for biological processes such as cell motility, signaling, proliferation, cytoskeletal organization and gene expression. For this reason, extensive effort has been devoted in the past few decades to measure cell adhesion as well as identify key molecules involved. This thesis focuses on two outstanding problems in this area, namely, how to quantitatively characterize the adhesion between neural cells and the substrate and how to model the turnover of adhesions in the intriguing phenomenon of stretch-induced cell realignment. First of all, using a combined atomic force (AFM) and total internal reflection fluorescence microscope (TIRFM) system a novel method was developed to systematically and quantitatively examine the adhesion between neurite branches and the extracellular matrix. Specifically, a tipless AFM cantilever was used to penetrate between a well-developed neurite and the functionalized substrate and then gradually peel the neurite from the surface. At the same time, a laser TIRFM was utilized to monitor the activities of different adhesion molecules during the detaching process. This approach provides a solution to the long-standing problem of how to quantitatively measure neuron-extracellular matrix interactions while, simultaneously, identify the roles of various adhesion proteins in the process. Besides heathy neurons, testes have also been conducted on cells affected by the Alzheimer's disease (AD) where the influence of such disease on the mechanical response of neural cells was demonstrated. Secondly, to better understand the observed peeling response of the neurite, as well as extract key information from it, finite element (FEM) simulation was carried out using ABAQUS. It was shown that a good fit between the simulation results and experimental data can be achieved by representing the adhesion between two surfaces with simple cohesive interactions. In particular, it was found that the apparent adhesion energy density, a quantity of central interest in cell adhesion studies, is in the range of 0.2-0.8 mj/m^2. Last but not the least, a mechanochemical modeling framework was developed to investigate the mechanism of cell reorientation induced by cyclic stretching on the substrate. It was shown that the final alignment of cells reflects the competition between stress fiber assembly or disassembly, focal adhesion growth or disruption, substrate stiffening and whole-cell rotation. Predictions from the model are consistent with a variety of experimental observations, suggesting that the main physics of this intriguing phenomenon may have been well captured.
published_or_final_version
Mechanical Engineering
Doctoral
Doctor of Philosophy

Stochastic effects in cellular systems are usually modeled and simulated with Gillespie's stochastic simulation algorithm (SSA), which follows the same theoretical derivation as the chemical master equation (CME), but the low efficiency of SSA limits its application to large chemical networks. To improve efficiency of stochastic simulations, Haseltine and Rawlings proposed a hybrid of ODE and SSA algorithm, which combines ordinary differential equations (ODEs) for traditional deterministic models and SSA for stochastic models. In this dissertation, accuracy analysis, efficient implementation strategies, and application of of Haseltine and Rawlings's hybrid method (HR) to a budding yeast cell cycle model are discussed. Accuracy of the hybrid method HR is studied based on a linear chain reaction system, motivated from the modeling practice used for the budding yeast cell cycle control mechanism. Mathematical analysis and numerical results both show that the hybrid method HR is accurate if either numbers of molecules of reactants in fast reactions are above certain thresholds, or rate constants of fast reactions are much larger than rate constants of slow reactions. Our analysis also shows that the hybrid method HR allows for a much greater region in system parameter space than those for the slow scale SSA (ssSSA) and the stochastic quasi steady state assumption (SQSSA) method. Implementation of the hybrid method HR requires a stiff ODE solver for numerical integration and an efficient event-handling strategy for slow reaction firings. In this dissertation, an event-handling strategy is developed based on inverse interpolation. Performances of five wildly used stiff ODE solvers are measured in three numerical experiments. Furthermore, inspired by the strategy of the hybrid method HR, a hybrid of ODE and SSA stochastic models for the budding yeast cell cycle is developed, based on a deterministic model in the literature. Simulation results of this hybrid model match very well with biological experimental data, and this model is the first to do so with these recently available experimental data. This study demonstrates that the hybrid method HR has great potential for stochastic modeling and simulation of large biochemical networks.
Ph. D.

Les réseaux de fibre sont une structure omniprésente dans les tissus biologiques, aussi bien au niveau macroscopique, où ils sont l'ingrédient principal des tissus mous, qu'au niveau microscopique, en tant que constituants des structures collagèniques ou du cytosquelette. L'objectif de ce travail de thèse est de proposer un modèle basé sur la microstructure physique des réseaux de fibres afin d'obtenir une compréhension du comportement mécanique des réseaux de fibres biologiques. Le modèle est basé sur une description de fibres glissant les unes par rapport aux autres et interagissant via des ponts qui se comportent comme des ressorts. Ces ponts peuvent s'attacher et se détacher de manière stochastique avec un taux de détachement qui dépend de la force subie. Comparé aux modélisations existantes, notre travail met en jeu une configuration en glissement dynamique des fibres, ainsi que des sites d'attachement discrets ne permettant l'attachement qu'à des endroits localisés de la fibre. Le détachement des ponts est basé sur la diffusion thermique hors d'un puit de potentiel suivant la théorie de Kramers. Cette théorie donne un contexte physique à la dynamique du détachement ainsi qu'une dépendance naturelle du détachement au chargement via l'inclinaison du paysage énergétique par la force de chargement. Le modèle donne deux modes de contrôle du système : un contrôle à vitesse imposée, appelé système dur, et un contrôle à force imposée, appelé système mou. Notre travail permet également de visualiser le comportement du système à travers une simulation stochastique. Les simulations offrent deux algorithmes, chacun adapté à la méthode de contrôle du système, en système dur ou mou et respectant la causalité dans chacun des modes. Les résultats de la simulation sont explorés via la visualisation des données sortantes de la simulation, qui servent de support pour l'investigation paramétrique du comportement du modèle et ancrent l'interprétation physique des résultats. En particulier, l'influence de l'espacement des sites d'attachement du système, un point caractéristique du modèle, est examiné. De même, nous explorons l'effet de chargements complexes (transitoires, cycliques, etc.) qui représentent les chargements physiologiques des tissus fibreux
Fibre networks are ubiquitous structures in biological tissues, both at the macroscopic level being the main ingredient in soft tissues and at the microscopic level, as constituents of collagen structures or the cytoskeleton. The goal of this work is to propose a model based on the physical microstructure of fibre networks in order to provide an understanding of the mechanical behaviour of biological fibre networks. The current model starts from fibres sliding with respect to one another and interacting via spring-like cross-bridges. These cross-bridges can attach and detach stochastically with a load-dependent detachment rate. Compared to existing modelling approaches, this work features a dynamic sliding configuration for the interacting fibres and discrete binding sites which permit attachment on localised spaces of the fibre. The detachment of cross-bridges is based on thermal diffusion out of an energy well, following the Kramers rate theory. This theory provides a physical background to the detachment dynamics as well as a natural load dependency in the tilting of the energy landscape by the load force. The model provides two modes by which the depicted system may be driven: an imposed velocity driving, called a hard device and an imposed load driving, called a soft device. The work also provides a way of visualising the behaviour of the model by performing a stochastic simulation. The simulations provided present two algorithms, each tailored to represent the driving of the system, whether in hard or soft device, respecting the causality in each of the driving mode. Simulation results are explored via data visualisation of simulation output. These visualisation serve as an entry point into parametric investigation of the model behaviour and anchor the interpretation of the results into physical systems. In particular, the influence of binding site spacing, one of the key features of the model, is investigated. We also investigate the effects of complex loading paths (transitory, cyclic, etc.) which can be associated to the physiological loadings fibrous tissues

Because cells are machines, their structure determines their function (health). But their structure also determines cells’ mechanical properties. So if we can understand how cells’ mechanical properties are influenced by specific structures, then we can observe what’s happening inside of cells via mechanical measurements. The Atomic Force Microscope (AFM) has become a standard tool for investigating the mechanical properties of cells. In many experiments, an AFM is used to ‘poke’ adherent cells with nanonewton forces, and the resulting deformation observed via, e.g. Laser Scanning Confocal Microscopy. Results of such experiments are often interpreted in terms of continuum mechanical models which characterize the cell as a linear viscoelastic solid. This “top-down” approach of poking an intact cell —complete with cytoskeleton, organelles etc.— can be problematic when trying to measure the mechanical properties and response of a single cell component. Moreover, how are we to know the sensitivity of the cell’s mechanical properties to partial modification of a single component (e.g. reducing the degree of cross- linking in the actin cortex)? In contrast, the approach taken here —studying the deformation and relaxation of lipid bilayer vesicles— might be called a “bottom-up” approach to cell mechanics. Using Coarse- Grained Molecular Dynamics simulations, we study the deformation and relaxation of bilayer vesicles, when poked with constant force. The relaxation time, equilibrium area expansion, and surface tension of the vesicle membrane are studied over a range of applied forces. Interestingly, the relaxation time exhibits a strong force-dependence. Force-compression curves for our simulated vesicle show a strong similarity to recent experiments where giant unilamellar vesicles were compressed in a manner nearly identical to that of our simulations.

The behaviour of individual cells must be carefully coordinated across a tissue to achieve correct function. In particular, proliferation and differentiation decisions must be precisely regulated throughout development, tissue maintenance, and repair. A better understanding of how these processes are controlled would have implications for human health; cancer is, after all, dysregulated proliferation, while regenerative medicine relies on being able to influence cell decisions accurately. To investigate such fundamental biological processes, it is common practice to use an experimentally tractable model organism. Here, we focus on the germ line of the nematode worm C. elegans, which provides opportunities to study organogenesis, tissue maintenance, and ageing effects. Despite the advantages of this organism as a biological model, certain questions about germ cell behaviour and coordination remain challenging to address in the lab. There is therefore a need for computational models of the germ line to complement experimental approaches. In this thesis, we develop a new in silico model of the C. elegans germ line. Novel aspects include working in three dimensions, covering the late larval period, and integrating a logical model of germ cell behaviour into a wider cell mechanics simulation. Our model produces a reasonable fit to wild-type germline behaviour, and provides the first cell tracking and labelling predictions for the larval period. It also suggests two new biological hypotheses: 1) that “stretching” growth plays a significant role in gonadogenesis, and 2) that a feedback mechanism acts on the germ cell cycle to prevent overproliferation. Having introduced the full model, we address some technical questions arising from our work, namely: what is the effect of applying a more physically realistic force law?; and can simulation performance be improved by changing the numerical scheme? Finally, we use in silico modelling to compare a number of hypothesised germ line maintenance mechanisms. There, our results support a model with functionally equivalent germ cells undergoing at most infrequent, transient cell cycle arrests.

An object-oriented framework is presented for developing and simulating individual-based models of cell populations. The framework supplies classes to define objects called simulation channels that encapsulate the algorithms that make up a simulation model. These may govern state-updating events at the individual level, perform global state changes, or trigger cell division. Simulation engines control the scheduling and execution of collections of simulation channels, while a simulation manager coordinates the engines according to one of two scheduling protocols. When the ensemble of cells being simulated reaches a specified maximum size, a procedure is introduced whereby random cells are ejected from the simulation and replaced by newborn cells to keep the sample population size constant but representative in composition. The framework permits recording of population snapshot data and/or cell lineage histories. Use of the framework is demonstrated through validation benchmarks and two case studies based on experiments from the literature.

An accelerated pace of discovery in biological sciences is made possible by a new generation of computational biology and bioinformatics tools. In this dissertation we develop novel computational, analytical, and high performance simulation techniques for biological problems, with applications to the yeast cell division cycle, and to the RNA-Sequencing of the yellow fever mosquito. Cell cycle system evolves stochastic effects when there are a small number of molecules react each other. Consequently, the stochastic effects of the cell cycle are important, and the evolution of cells is best described statistically. Stochastic simulation algorithm (SSA), the standard stochastic method for chemical kinetics, is often slow because it accounts for every individual reaction event. This work develops a stochastic version of a deterministic cell cycle model, in order to capture the stochastic aspects of the evolution of the budding yeast wild-type and mutant strain cells. In order to efficiently run large ensembles to compute statistics of cell evolution, the dissertation investigates parallel simulation strategies, and presents a new probabilistic framework to analyze the performance of dynamic load balancing algorithms. This work also proposes new accelerated stochastic simulation algorithms based on a fully implicit approach and on stochastic Taylor expansions. Next Generation RNA-Sequencing, a high-throughput technology to sequence cDNA in order to get information about a sample's RNA content, is becoming an efficient genomic approach to uncover new genes and to study gene expression and alternative splicing. This dissertation develops efficient algorithms and strategies to find new genes in Aedes aegypti, which is the most important vector of dengue fever and yellow fever. We report the discovery of a large number of new gene transcripts, and the identification and characterization of genes that showed male-biased expression profiles. This basic information may open important avenues to control mosquito borne infectious diseases.
Ph. D.

The present thesis deals with a number of challenges in the field of large eddy simulation (LES). These include the performance of subgrid-scale (SGS) models at fairly high Reynolds numbers and coarse resolutions, passive scalar and stochastic modeling in LES. The fully-developed turbulent channel flow is used as the test case for these investigations. The advantage of this particular test case is that highly accurate pseudo-spectral methods can be used for the discretization of the governing equations. In the absence of discretization errors, a better understanding of the subgrid-scale model performance can be achieved. Moreover, the turbulent channel flow is a challenging test case for LES, since it shares some of the common important features of all wall-bounded turbulent flows. Most commonly used eddy-viscosity-type models are suitable for moderately to highly-resolved LES cases, where the unresolved scales are approximately isotropic. However, this makes simulations of high Reynolds number wall-bounded flows computationally expensive. In contrast, explicit algebraic (EA) model takes into account the anisotropy of SGS motions and performs well in predicting the flow statistics in coarse-grid LES cases. Therefore, LES of high Reynolds number wall-bounded flows can be performed at much lower number of grid points in comparison with other models. A demonstration of the resolution requirements for the EA model in comparison with the dynamic Smagorinsky and its high-pass filtered version for a fairly high Reynolds number is given in this thesis. One of the shortcomings of the commonly used eddy diffusivity model arises from its assumption of alignment of the SGS scalar flux vector with the resolved scalar gradients. However, better SGS scalar flux models that overcome this issue are very few. Using the same methodology that led to the EA SGS stress model, a new explicit algebraic SGS scalar flux model is developed, which allows the SGS scalar fluxes to be partially independent of the resolved scalar gradient. The model predictions are verified and found to improve the scalar statistics in comparison with the eddy diffusivity model. The intermittent nature of energy transfer between the large and small scales of turbulence is often not fully taken into account in the formulation of SGS models both for velocity and scalar. Using the Langevin stochastic differential equation, the EA models are extended to incorporate random variations in their predictions which lead to a reasonable amount of backscatter of energy from the SGS to the resolved scales. The stochastic EA models improve the predictions of the SGS dissipation by decreasing its length scale and improving the shape of its probability density function.
QC 20110615

Fluctuations, or "noise", can play a key role in determining the behaviour of living systems. The molecular-level fluctuations that occur in genetic networks are of particular importance. Here, noisy gene expression can result in genetically identical cells displaying significant variation in phenotype, even in identical environments. This variation can act as a basis for natural selection and provide a fitness benefit to cell populations under stress. This thesis focuses on the development of new conceptual knowledge about how gene expression noise and gene network topology influence drug resistance, as well as new simulation techniques to better understand cell population dynamics. Network topology may at first seem disconnected from expression noise, but genes in a network regulate each other through their expression products. The topology of a genetic network can thus amplify or attenuate noisy inputs from the environment and influence the expression characteristics of genes serving as outputs to the network. The main body of the thesis consists of five chapters: 1. A published review article on the physical basis of cellular individuality. 2. A published article presenting a novel method for simulating the dynamics of cell populations. 3. A chapter on modeling and simulating replicative aging and competition using an object-oriented framework. 4. A published research article establishing that noise in gene expression can facilitate adaptation and drug resistance independent of mutation. 5. An article submitted for publication demonstrating that gene network topology can affect the development of drug resistance. These chapters are preceded by a comprehensive introduction that covers essential concepts and theories relevant to the work presented.

Fuel cells represent a promising energy alternative to the traditional combustion of fossil fuels. In particular, solid oxide fuel cells (SOFCs) have been of interest due to their high energy densities and potential for stationary power applications. One of the key obstacles precluding the maturation and commercialization of planar SOFCs has been the absence of a robust sealant. A leakage computational model has been developed and refined in conjunction with leakage experiments and material characterization tests at Oak Ridge National Laboratory to predict leakage in a single interface metal-metal compressive seal assembly as well as multi-interface mica compressive seal assemblies. The composite model is applied as a predictive tool for assessing how certain parameters (i.e., temperature, applied compressive stress, surface finish, and elastic thermo physical properties) affect seal leakage rates.

From the developing embryo to the evacuation of football stadiums, the migration and movement of populations of individuals is a vital part of human life. Such movement often occurs in crowded conditions, where the space occupied by each individual impacts on the freedom of others. This thesis aims to analyse and understand the effects of occupied volume (volume exclusion) on the movement of the individual and the population. We consider, as a motivating system, the rearrangement of individuals required to turn a clump of cells into a functioning embryo. Specifically, we consider the migration of cranial neural crest cells in the developing chick embryo. Working closely with experimental collaborators we construct a hybrid model of the system, consisting of a continuum chemoattractant and individual-based cell description and find that multiple cell phenotypes are required for successful migration. In the crowded environment of the migratory system, volume exclusion is highly important and significantly enhances the speed of cell migration in our model, whilst reducing the numbers of individuals that can enter the domain. The developed model is used to make experimental predictions, that are tested in vivo, using cycles of modelling and experimental work to give greater insight into the biological system. Our formulated model is computational, and is thus difficult to analyse whilst considering different parameter regimes. The second part of the thesis is driven by the wish to systematically analyse our model. As such, it concentrates on developing new techniques to derive continuum equations from diffusive and chemotactic individual-based and hybrid models in one and two spatial dimensions with the incorporation of volume exclusion. We demonstrate the accuracy of our techniques under different parameter regimes and using different mechanisms of movement. In particular, we show that our derived continuum equations almost always compare better to data averaged over multiple simulations than the equivalent equations without volume exclusion. Thus we establish that volume exclusion has a substantial effect on the evolution of a migrating population.

Inductive plasmas are simulated by using a one-dimensional particle-in-cell simulation including Monte Carlo collision techniques (pic/mcc). To model inductive heating, a non-uniform radio-frequency (rf) electric field, perpendicular to the electron motion is included into the classical particle-in-cell scheme. The inductive plasma pic simulation is used to confirm recent experimental results that electric double layers can form in current-free plasmas. These results differ from previous experimental or simulation systems where the double layers are driven by a current or by imposed potential differences. The formation of a super-sonic ion beam, resulting from the ions accelerated through the potential drop of the double layer and predicted by the pic simulation is confirmed with nonperturbative laser-induced fluorescence measurements of ion flow. It is shown that at low pressure, where the electron mean free path is of the order of, or greater than the system length, the electron energy distribution function (eedf) is close to Maxwellian, except for its tail which is depleted at energies higher than the plasma potential. Evidence supporting that this depletion is mostly due to the high-energy electrons escaping to the walls is given. ¶ A new hybrid simulation scheme (particle ions and Boltzmann/particle electrons), accounting for non-Maxwellian eedf and self-consistently simulating low-pressure high-density plasmas at low computational cost is proposed. Results obtained with the “improved” hybrid model are in much better agreement with the full pic simulation than the classical non self-consistent hybrid model. This model is used to simulate electronegative plasmas and to provide evidence supporting the fact that propagating double layers may spontaneously form in electronegative plasmas. It is shown that critical parameters of the simulation were very much aligned with critical parameters of the experiment.

This thesis developed a high preforming alternative numerical technique to investigate microscale morphological changes of plant food materials during drying. The technique is based on a novel meshfree method, and is more capable of modeling large deformations of multiphase problem domains, when compared with conventional grid-based numerical modeling techniques. The developed cellular model can effectively replicate dried tissue morphological changes such as shrinkage and cell wall wrinkling, as influenced by moisture reduction and turgor loss.

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O objetivo principal deste trabalho é adicionar e analisar uma equação que repre senta o volume no modelo dinâmico do ciclo celular de mamíferos proposto por Gérard e Goldbeter (2011). A divisão celular ocorre quando o complexo ciclinaB/Cdk1(quínase dependente de ciclina) é totalmente degradado atingindo um valor mínimo. Neste ponto, a célula é divida em duas novas células filhas e cada uma irá conter a metade do conteúdo citoplasmático da célula mãe. As equações do modelo de base são válidas apenas se o volume celular, onde as reações ocorrem, é constante. Quando o volume celular não é constante, isto é, a taxa de variação do volume em relação ao tempo é explicitamente levada em consideração no modelo matemático, então as equações do modelo original não são mais válidas. Portanto, todas as equações foram modificadas a partir do princípio de conservação das massas para considerar um volume que varia ao longo do tempo. Por meio desta abordagem, o volume celular afeta todas as variáveis do modelo. Dois méto dos diferentes de simulação foram efetuados: determinista e estocástico. Na simulação estocástica, o volume afeta todos os parâmetros do modelo que possuem de alguma forma unidade molar, enquanto que no determinista, ele é incorporado nas equações diferen ciais. Na simulação determinista, as espécies bioquímicas podem estar em unidades de concentração, enquanto na simulação estocástica tais espécies devem ser convertidas para número de moléculas que são diretamente proporcional ao volume celular. Em um esforço para entender a influência da nova equação sobre o modelo uma análise de estabilidade foi feita. Isso esclarece como o novo parâmetro µ, fator de crescimento do volume celular, impacta na estabilidade do ciclo limite do modelo. Para encontrar a solução aproximada do modelo determinista, o método Runge Kutta de quarta ordem foi implementado. Já para o modelo estocástico, o método direto de Gillespie foi usado. Para concluir, um modelo mais preciso, em comparação ao modelo de base, foi desenvolvido ao levar em consideração a influência da taxa de variação do volume celular sobre o ciclo celular.
The main goal of this work is to add and analyse an equation that represents the volume in a dynamical model of the mammalian cell cycle proposed by Gérard and Gold beter (2011). The cell division occurs when the cyclinB/Cdk1 (cyclin-dependent kinase) complex is totally degraded and it reaches a minimum value. At this point, the cell is divided into two newborn daughter cells and each one will contain the half of the cyto plasmic content of the mother cell. The equations of our base model are valid only if the cell volume, where the reactions occur, is constant. Whether the cell volume is not constant, that is, the rate of change of its volume with respect to time is explicitly taken into account in the mathematical model, then the equations of the original model are no longer valid. Therefore, every equations were modified from the mass conservation prin ciple for considering a volume that changes with time. Through this approach, the cell volume affects all model variables. Two different dynamic simulation methods were ac complished: deterministic and stochastic. In the stochastic simulation, the volume affects every model’s parameters which have molar unit, whereas in the deterministic one, it is incorporated into the differential equations. In deterministic simulation, the biochemical species may be in concentration units, while in stochastic simulation such species must be converted to number of molecules which are directly proportional to the cell volume. In an effort to understand the influence of the new equation over the model an stability analysis was performed. This elucidates how the new parameter µ, cell volume growth factor, impacts the stability of the model’s limit cycle. In order to find the approximated solution of the deterministic model, the fourth order Runge Kutta method was implemen ted. As for the stochastic model, the Gillespie’s Direct Method was used. In conclusion, a more precise model, in comparison to the base model, was created for the cell cycle as it now takes into consideration the rate of change of the cell volume.

Nous nous interessons à la modélisation des interactions fluide-structure entre un fluide visqueux incompressible et une structure pouvant être déformable. Apres une approche des méthodes de type frontière immergée existantes, nous présentons une nouvelle approche : la méthode IPC (Image Point Correction) que nous validons ensuite sur différents cas tests. Puis, nous l'appliquons à la simulation 2D puis 3D de la nage d'un poisson grâce à une reconstruction utilisant l'outil du squelette.

Durante as últimas décadas, pesquisas em biologia do tumor com a utilização de novas técnicas de biologia molecular produziram informações em profusão, motivando e dando condições para que fossem criados novos modelos matemáticos dedicados à análise de vários aspectos de crescimento e proliferação da população celular. Alguns desses modelos têm sido dedicados à descrição e análise do regime estacionário do processo de desenvolvimento de uma população celular sob condições químicas que se consideram favorecer a aceleração ou desaceleração do crescimento da população de células tumorais. Todavia, a dinâmica temporal do crescimento de uma população de células tumorais ainda não foi analisada nesses trabalhos. Uma das dificuldades é o estabelecimento da interação entre células de múltiplos tipos que sirvam como descrição para essa dinâmica. Nosso trabalho vem preencher essa lacuna e a presente dissertação tem como objetivo a apresentação do modelo, desenvolvido por nós, de simulação da dinâmica do crescimento e proliferação celular do melanoma (câncer de baixa incidência, mas de letalidade extremamente alta) e também dos resultados obtidos através das simulações deste modelo computacional
During the last decades, tumor biology research with the use of new techniques in molecular biology resulted in a profusion of information that have given conditions and motivated the development of new mathematical models dedicated to analyzing various aspects of growth and proliferation of the cell population. Some of these models have been devoted to the description and analysis of the steady state of the development process of a cell population under chemical conditions that, in theory, promote the acceleration or deceleration of the growth of tumor cell population. However, these studies have not yet analyzed the temporal dynamics of growth of a tumor cell population. One of the difficulties is the establishment of the interaction between cells of multiple types that serve as the description for this dynamic. Our work fills this gap and this dissertation aims to present the model, developed by us, to simulate the growth dynamics and cellular proliferation of melanoma (cancer of low incidence but of extremely high lethality) and the results obtained through the simulations of this computational model

Les propriétés magnétiques détaillées des solides peuvent être vu comme le résultat de l'interaction de plusieurs sous-systèmes: celui des spins effectifs, portant l'aimantation, celui des électrons et celui du réseau crystallin. Différents processus permettent à ces sous-systèmes d'échanger de l'énergie. Parmis ceux-ci, les phénomènes de relaxation jouent un rôle prépondérants. Cependant, la complexité de ces processus en rend leur modélisation ardue. Afin de prendre en compte ces interactions de façon abordable aux calculs, l'approche de Langevin est depuis longtemps appliquée à la dynamique d'aimantation, qui peut être vue comme la réponse collective des spins. Elle consiste à modéliser les interactions entre les trois sous-systèmes par des interactions effectives entre le sous-système d'intérêt, les spins, et un bain thermique, dont seulement la densité de probabilité constituerait une quantité pertinente. Après avoir présenté cette approche, nous verrons en quoi elle permet de bâtir une dynamique atomique de spin. Une fois son implémentation détaillée, cette méthodologie sera appliquée à un exemple tiré de la littérature et basé sur le superparamagnétisme de nanoaimants de fer
Detailed magnetic properties of solids can be regarded as the result of the interaction between three subsystems: the effective spins, that will be our focus in this thesis, the electrons and the crystalline lattice. These three subsystems exchange energy, in many ways, in particular, through relaxation processes. The nature of these processes remains extremely hard to understand, and even harder to simulate. A practical approach, for performing such simulations, involves adapting the description of random processes by Langevin to the collective dynamics of the spins, usually called the magnetization dynamics. It consists in describing the, complicated, interactions between the subsystems, by the effective interactions of the subsystem of interest, the spins, and a thermal bath, whose probability density is only of relevance. This approach allows us to interpret the results of atomistic spin dynamics simulations in appropriate macroscopic terms. After presenting the numerical implementation of this methodology, a typical study of a magnetic device based on superparamagnetic iron monolayers is presented, as an example. The results are compared to experimental data and allow us to validate the atomistic spin dynamics simulations

Ce travail porte sur la modélisation et la simulation d’écoulements compressibles. Par une démarche d’homogénéisation, on commence par dériver un modèle d’écoulements diphasiques à sept équations. Les termes de fluctuation restants sont modélisés par des termes de relaxation. Dans le cas où ces coefficients de relaxation tendent vers l’infini, ce qui correspond à des écoulements très bien mélangés, on obtient par un développement asymptotique un modèle à cinq équations qui est strictement hyperbolique, mais non-conservatif. La discrétisation de ce modèle est obtenue par un développement asymptotique d’un schéma numérique pour le système à sept équations. Le schéma obtenu est implémenté, validé sur des cas analytiques, et comparé dans le cas de chocs multiphasiques à des résultats expérimentaux. On s’intéresse ensuite à la modélisation du changement de phase avec deux équations d’état. Un principe d’optimisation de l’entropie de mélange mène à distinguer trois zones : une zone où le liquide pur est le plus stable, une autre zone où le gaz pur est le plus stable, et, enfin, une zone où un mélange à l’équilibre des pressions, températures et potentiels thermodynamiques est stable. On donne alors des conditions sur le couplage des deux équations d’état pour que l’équation d’état de mélange soit convexe, et pour que le système soit hyperbolique. Afin de prendre en compte le changement de phase, on introduit dans la solution du problème de Riemann une onde de vaporisation modélisée comme une onde de déflagration. On montre ensuite que la fermeture habituelle, la fermeture de Chapman-Jouguet, est inadéquate en général, et on donne une fermeture correcte dans le cas où les deux phases sont des gaz parfaits. Enfin, la solution du problème de Riemann est implémentée dans un code multiphasique, et validée sur des cas analytiques. Dans ce même code, on met en place un modèle de dépôt laser et de conduction thermique non linéaire afin de modéliser les phénomènes physiques intervenant dans l’ablation laser. Les résultats obtenus sont comparables à ceux obtenus avec des lois d’échelle. Le dernier chapitre, complètement indépendant, porte sur la recherche de correcteurs en homogénéisation stochastique dans le cas de processus à queue lourde
This work deals with the modelling and simulation of compressible flows. A seven equations model is obtained by homogenizing the Euler system. Fluctuation terms are modeled as relaxation terms. When the relaxation terms tend to infinity, which means that the phases are well mixed, a five equations model is obtained via an asymptotic expansion. This five equations model is strictly hyperbolic, but nonconser- vative. The discretization of this model is obtained by an asymptotic expansion of a scheme for the seven equations model. The numerical method is implemented, validated on analytic cases, and compared with experiments in the case of multiphase shocks. We are then interested in the modelling of phase transition with two equations of state. Optimization of the mixture entropy leads to the fact that three zones can be separated: one in which the pure liquid is the most stable, one in which the pure gas is the most stable, and one in which a mixture with equality of temperature, pressure and chemical potentials is the most stable. Conditions are given on the coupling of the two equations of state for ensuring that the mixture equation of state is convex, and that the system is strictly hyperbolic. In order to take into account phase transition, a vaporization wave is introduced in the solution of the Riemann problem, that is modeled as a deflagration wave. It is then proved that the usual closure, the Chapman-Jouguet closure, is wrong in general, and a correct closure in the case when both fluids have a perfect gas equation of state. Last, the solution of the Riemann problem is implemented in a multiphase code, and validated on analytic cases. In the same code, models of laser release and thermal conduction are implemented to simulate laser ablation. The results are comparable to the ones obtained with scale laws. The last chapter, fully independent, is concerned with correctors in stochastic homogenization in the case of heavy tails process

Ce travail est consacré à l'étude de structures cellulaires bidimensionnelles et notamment à leur évolution au cours du temps. Après une phase transitoire (dont la durée dépend de l'ordre initial de la structure) l'évolution atteint généralement un régime stationnaire (où l'aire moyenne des cellules varie linéairement avec le temps et où les propriétés sans dimension, telles que le désordre topologique et la distribution des nombres de côtés des cellules, sont invariantes). Une méthodologie d'analyse d'images a été mise au point pour caractériser les structures à un instant donné. Leurs propriétés métriques et topologiques sont déterminées en tenant compte d'une correction de biais statistique. Nous avons réalisé des expériences portant sur des mousses de savon bidimensionnelles (réalisées entre deux plaques). Un système de drainage a également été mis en place dans le but de conserver l'épaisseur des arêtes constante au cours de l'évolution. Lorsque l'état initial de la structure est très ordonné, le stade transitoire est très long, ce qui limite notre étude à cette seule phase de l'évolution. En revanche, nous avons pû étudier le régime stationnaire en partant de structures initiales désordonnées et en particulier déterminer la valeur du désordre topologique dans cette phase. Cette valeur semble dépendre de la composition du liquide moussant, de la taille de la boîte contenant la mousse, mais apparemment pas du drainage. Des simulations ont été effectuées à l'aide d'un programme développé par H. Telley à l'EPFL. Ce programme est fondé sur l'utilisation des complexes de Laguerre bidimensionnels et périodiques. Ces simulations ont fourni des résultats comparables à ceux observés pour les mousses, mais également pour les polycristaux, grâce à l'ajustement d'un paramètre distributif. Celui-ci est relié de façon simple aux transformations topologiques élémentaires intervenant au cours de l'évolution. La validation du programme a été effectuée non seulement pour le régime stationnaire mais aussi pour le régime transitoire (pour les mousses de savon)

Nous proposons dans cette thèse de caractériser quantitativement la variabilité à différentes échelles au cours de l'embryogenèse. Pour ce faire, nous utilisons une combinaison de modèles mathématiques et de résultats expérimentaux. Dans la première partie, nous utilisons une petite cohorte d'oursins digitaux pour construire une représentation prototypique du lignage cellulaire, reliant les caractéristiques des cellules individuelles avec les dynamiques à l'échelle de l'embryon tout entier. Ce modèle probabiliste multi-niveau et empirique repose sur les symétries des embryons et sur les identités cellulaires; cela permet d'identifier un niveau de granularité générique pour observer les distributions de caractéristiques cellulaires individuelles. Le prototype est défini comme le barycentre de la cohorte dans la variété statistique correspondante. Parmi plusieurs résultats, nous montrons que la variabilité intra-individuelle est impliquée dans la reproductibilité du développement embryonnaire. Dans la seconde partie, nous considérons les mécanismes sources de variabilité au cours du développement et leurs relations à l'évolution. En nous appuyant sur des résultats expérimentaux montrant une pénétrance incomplète et une expressivité variable de phénotype dans une lignée mutante du poisson zèbre, nous proposons une clarification des différents niveaux de variabilité biologique reposant sur une analogie formelle avec le cadre mathématique de la mécanique quantique. Nous trouvons notamment une analogie formelle entre l'intrication quantique et le schéma Mendélien de transmission héréditaire. Dans la troisième partie, nous étudions l'organisation biologique et ses relations aux trajectoires développementales. En adaptant les outils de la topologie algébrique, nous caractérisons des invariants du réseaux de contacts cellulaires extrait d'images de microscopie confocale d'épithéliums de différentes espèces et de différents fonds génétiques. En particulier, nous montrons l'influence des histoires individuelles sur la distribution spatiales des cellules dans un tissu épithélial
We propose in this thesis to characterize variability quantitatively at various scales during embryogenesis. We use a combination of mathematical models and experimental results. In the first part, we use a small cohort of digital sea urchin embryos to construct a prototypical representation of the cell lineage, which relates individual cell features with embryo-level dynamics. This multi-level data-driven probabilistic model relies on symmetries of the embryo and known cell types, which provide a generic coarse-grained level of observation for distributions of individual cell features. The prototype is defined as the centroid of the cohort in the corresponding statistical manifold. Among several results, we show that intra-individual variability is involved in the reproducibility of the developmental process. In the second part, we consider the mechanisms sources of variability during development and their relations to evolution. Building on experimental results showing variable phenotypic expression and incomplete penetrance in a zebrafish mutant line, we propose a clarification of the various levels of biological variability using a formal analogy with quantum mechanics mathematical framework. Surprisingly, we find a formal analogy between quantum entanglement and Mendel’s idealized scheme of inheritance. In the third part, we study biological organization and its relations to developmental paths. By adapting the tools of algebraic topology, we compute invariants of the network of cellular contacts extracted from confocal microscopy images of epithelia from different species and genetic backgrounds. In particular, we show the influence of individual histories on the spatial distribution of cells in epithelial tissues

The present habilitation thesis in theoretical biological physics addresses two central dynamical processes in cells and organisms: (i) active motility and motility control and (ii) self-organized pattern formation. The unifying theme is the nonlinear dynamics of biological function and its robustness in the presence of strong fluctuations, structural variations, and external perturbations. We theoretically investigate motility control at the cellular scale, using cilia and flagella as ideal model system. Cilia and flagella are highly conserved slender cell appendages that exhibit spontaneous bending waves. This flagellar beat represents a prime example of a chemo-mechanical oscillator, which is driven by the collective dynamics of molecular motors inside the flagellar axoneme. We study the nonlinear dynamics of flagellar swimming, steering, and synchronization, which encompasses shape control of the flagellar beat by chemical signals and mechanical forces. Mechanical forces can synchronize collections of flagella to beat at a common frequency, despite active motor noise that tends to randomize flagellar synchrony. In Chapter 2, we present a new physical mechanism for flagellar synchronization by mechanical self-stabilization that applies to free-swimming flagellated cells. This new mechanism is independent of direct hydrodynamic interactions between flagella. Comparison with experimental data provided by experimental collaboration partners in the laboratory of J. Howard (Yale, New Haven) confirmed our new mechanism in the model organism of the unicellular green alga Chlamydomonas. Further, we characterize the beating flagellum as a noisy oscillator. Using a minimal model of collective motor dynamics, we argue that measured non-equilibrium fluctuations of the flagellar beat result from stochastic motor dynamics at the molecular scale. Noise and mechanical coupling are antagonists for flagellar synchronization. In addition to the control of the flagellar beat by mechanical forces, we study the control of the flagellar beat by chemical signals in the context of sperm chemotaxis. We characterize a fundamental paradigm for navigation in external concentration gradients that relies on active swimming along helical paths. In this helical chemotaxis, the direction of a spatial concentration gradient becomes encoded in the phase of an oscillatory chemical signal. Helical chemotaxis represents a distinct gradient-sensing strategy, which is different from bacterial chemotaxis. Helical chemotaxis is employed, for example, by sperm cells from marine invertebrates with external fertilization. We present a theory of sensorimotor control, which combines hydrodynamic simulations of chiral flagellar swimming with a dynamic regulation of flagellar beat shape in response to chemical signals perceived by the cell. Our theory is compared to three-dimensional tracking experiments of sperm chemotaxis performed by the laboratory of U. B. Kaupp (CAESAR, Bonn). In addition to motility control, we investigate in Chapter 3 self-organized pattern formation in two selected biological systems at the cell and organism scale, respectively. On the cellular scale, we present a minimal physical mechanism for the spontaneous self-assembly of periodic cytoskeletal patterns, as observed in myofibrils in striated muscle cells. This minimal mechanism relies on the interplay of a passive coarsening process of crosslinked actin clusters and active cytoskeletal forces. This mechanism of cytoskeletal pattern formation exemplifies how local interactions can generate large-scale spatial order in active systems. On the organism scale, we present an extension of Turing’s framework for self-organized pattern formation that is capable of a proportionate scaling of steady-state patterns with system size. This new mechanism does not require any pre-pattering clues and can restore proportional patterns in regeneration scenarios. We analytically derive the hierarchy of steady-state patterns and analyze their stability and basins of attraction. We demonstrate that this scaling mechanism is structurally robust. Applications to the growth and regeneration dynamics in flatworms are discussed (experiments by J. Rink, MPI CBG, Dresden)
Das Thema der vorliegenden Habilitationsschrift in Theoretischer Biologischer Physik ist die nichtlineare Dynamik funktionaler biologischer Systeme und deren Robustheit gegenüber Fluktuationen und äußeren Störungen. Wir entwickeln hierzu theoretische Beschreibungen für zwei grundlegende biologische Prozesse: (i) die zell-autonome Kontrolle aktiver Bewegung, sowie (ii) selbstorganisierte Musterbildung in Zellen und Organismen. In Kapitel 2, untersuchen wir Bewegungskontrolle auf zellulärer Ebene am Modelsystem von Zilien und Geißeln. Spontane Biegewellen dieser dünnen Zellfortsätze ermöglichen es eukaryotischen Zellen, in einer Flüssigkeit zu schwimmen. Wir beschreiben einen neuen physikalischen Mechanismus für die Synchronisation zweier schlagender Geißeln, unabhängig von direkten hydrodynamischen Wechselwirkungen. Der Vergleich mit experimentellen Daten, zur Verfügung gestellt von unseren experimentellen Kooperationspartnern im Labor von J. Howard (Yale, New Haven), bestätigt diesen neuen Mechanismus im Modellorganismus der einzelligen Grünalge Chlamydomonas. Der Gegenspieler dieser Synchronisation durch mechanische Kopplung sind Fluktuationen. Wir bestimmen erstmals Nichtgleichgewichts-Fluktuationen des Geißel-Schlags direkt, wofür wir eine neue Analyse-Methode der Grenzzykel-Rekonstruktion entwickeln. Die von uns gemessenen Fluktuationen entstehen mutmaßlich durch die stochastische Dynamik molekularen Motoren im Innern der Geißeln, welche auch den Geißelschlag antreiben. Um die statistische Physik dieser Nichtgleichgewichts-Fluktuationen zu verstehen, entwickeln wir eine analytische Theorie der Fluktuationen in einem minimalen Modell kollektiver Motor-Dynamik. Zusätzlich zur Regulation des Geißelschlags durch mechanische Kräfte untersuchen wir dessen Regulation durch chemische Signale am Modell der Chemotaxis von Spermien-Zellen. Dabei charakterisieren wir einen grundlegenden Mechanismus für die Navigation in externen Konzentrationsgradienten. Dieser Mechanismus beruht auf dem aktiven Schwimmen entlang von Spiralbahnen, wodurch ein räumlicher Konzentrationsgradient in der Phase eines oszillierenden chemischen Signals kodiert wird. Dieser Chemotaxis-Mechanismus unterscheidet sich grundlegend vom bekannten Chemotaxis-Mechanismus von Bakterien. Wir entwickeln eine Theorie der senso-motorischen Steuerung des Geißelschlags während der Spermien-Chemotaxis. Vorhersagen dieser Theorie werden durch Experimente der Gruppe von U.B. Kaupp (CAESAR, Bonn) quantitativ bestätigt. In Kapitel 3, untersuchen wir selbstorganisierte Strukturbildung in zwei ausgewählten biologischen Systemen. Auf zellulärer Ebene schlagen wir einen einfachen physikalischen Mechanismus vor für die spontane Selbstorganisation von periodischen Zellskelett-Strukturen, wie sie sich z.B. in den Myofibrillen gestreifter Muskelzellen finden. Dieser Mechanismus zeigt exemplarisch auf, wie allein durch lokale Wechselwirkungen räumliche Ordnung auf größeren Längenskalen in einem Nichtgleichgewichtssystem entstehen kann. Auf der Ebene des Organismus stellen wir eine Erweiterung der Turingschen Theorie für selbstorganisierte Musterbildung vor. Wir beschreiben eine neue Klasse von Musterbildungssystemen, welche selbst-organisierte Muster erzeugt, die mit der Systemgröße skalieren. Dieser neue Mechanismus erfordert weder eine vorgegebene Kompartimentalisierung des Systems noch spezielle Randbedingungen. Insbesondere kann dieser Mechanismus proportionale Muster wiederherstellen, wenn Teile des Systems amputiert werden. Wir bestimmen analytisch die Hierarchie aller stationären Muster und analysieren deren Stabilität und Einzugsgebiete. Damit können wir zeigen, dass dieser Skalierungs-Mechanismus strukturell robust ist bezüglich Variationen von Parametern und sogar funktionalen Beziehungen zwischen dynamischen Variablen. Zusammen mit Kollaborationspartnern im Labor von J. Rink (MPI CBG, Dresden) diskutieren wir Anwendungen auf das Wachstum von Plattwürmern und deren Regeneration in Amputations-Experimenten

With the growth of computational resources available, demand on the accuracy and speed of simulation is steeply increasing. Accordingly, accounting for the variabilities in the simulations plays a pivotal role in taking informed engineering decision. Variability or uncertainties can be classi fied into two broad categories, epistemic uncertainty, and aleoteric uncertainty. Where, epistemic uncertainty is because of the lack of knowledge or data, and aleoteric is the uncertainty inherent to the system. In this dissertation epistemic uncertainty is considered. Monte Carlo simulation is the most versatile method for the analysis of systems with uncertainties. However, for large scale engineering applications where finite element is used for solving the underlying physical problem, the computational cost of Monte Carlo becomes prohibitive. On the other hand, spectral stochastic finite element (SSFEM) outperforms Monte Carlo for problems with low stochastic dimensionality | that is, the number of basic random variables. However, in SSFEM the computational cost becomes prohibitive for problems with large stochastic dimensionality. Therefore, in this dissertation a hybrid method combining both Monte Carlo and SSFEM is developed to solve large scale stochastic systems. That is, the target systems are of large physical degrees of freedom and stochastic dimensionality. The total computational cost of analyzing a stochastic system can be divided into two parts, namely, uncertainty modeling and uncertainty propagation. Accordingly, first, a domain shape independence property of Karhunen-Loeve (KL) expansion is proposed and mathematically proved. Based on this property, an algorithm for the computation of KL expansion is pro posed. Next, the rate of decay of the eigenvalues in the KL expansion | which determines the stochastic dimensionality of the problem | is mathematically shown to be dependent on the domain size. That is, it is shown that as the size of the domain reduces the eigenvalues in the KL expansion decay faster. Based on these mathematical results, next, a domain decomposition (DD) based hybrid stochastic finite element formulation is proposed and its superior numerical performance for a serial implementation is demonstrated. In this algorithm MCS is used to solve the interface problem and SSFEM is used to solve the subdomain level problem, in this sense this proposed method is a hybrid method. Here, the Finite Element Tearing and Interconnecting (FETI) is used as the DD solver. Further, the same DD based hybrid algorithm is parallelized and is used to solve a large three dimensional elasticity problem, with large stochastic dimensionality. In these parallel numerical studies it was observed that, although using the proposed hybrid method brings down the computational cost to a great extent, the cost of solving the subdomain level problem using SSFEM turns out to be a large fraction of the total cost, for problems with very large stochastic dimensionality. Hence, fi nally the system of linear equations of SSFEM is reformulated as generalized Sylvester equation to achieve computational cost saving in solving the subdomain level problems.

Off-road and racing motorcycles require a particular setup of the suspension to improve the comfort and the safety of the rider. Further, due to ground unevenness, off-road motorcycle suspensions usually experience extreme and erratic excursions in performing their function. In this regard, the adoption of nonlinear devices, such as progressive springs and hydro pneumatic shock absorbers, can help limiting both the acceleration experienced by the sprung mass and the excursions of the suspensions. For dynamic analysis purposes, this option involves the solution of the nonlinear differential equations that govern the motion of the motorcycle, which is excited by the stochastic road ground profile. In this study a 4 degrees-of-freedom (4-DOF) nonlinear motorcycle model is considered. The model involves suspension elements with asymmetric behaviour. Further, it is assumed that the motorcycle is exposed to loading of a stochastic nature as it moves with a specified speed over a road profile defined by a particular power spectrum. It is shown that a meaningful analysis of the motorcycle response can be conducted by using the technique of statistical linearization. The validity of the proposed approach is established by comparison with results from pertinent Monte Carlo studies. In this context the applicability of auto-regressive (AR) filters for efficient implementation of the Monte Carlo simulation is pointed out. The advantages of these methods for the synthesis of excitation signals from a given power spectrum, are shown by comparison with other methods. It is shown that the statistical linearization method allows the analysis of multi-degree-of-freedom (M-DOF) systems that present strong nonlinearities, exceeding other nonlinear analysis methods in both accuracy and applicability. It is expected that the proposed approaches, can be used for a variety of parameter/ride quality studies and as preliminary design tool by the motorcycle industry.

In recent years, there has been increased awareness that stochasticity in chemical reactions and diffusion of molecules can have significant effects on the outcomes of intracellular processes, particularly given the low copy numbers of many proteins and mRNAs present in a cell. For such molecular species, the number and locations of molecules can provide a more accurate and detailed description than local concentration. In addition to diffusion, drift in the movements of molecules can play a key role in the dynamics of intracellular processes, and can often be modeled as arising from potential fields. Examples of sources of drift include active transport, variations in chemical potential, material heterogeneities in the cytoplasm, and local interactions with subcellular structures. This dissertation presents a new numerical method for simulating the stochastically varying numbers and locations of molecular species undergoing chemical reactions and drift-diffusion. The method combines elements of the First-Passage Kinetic Monte Carlo (FPKMC) method for reaction-diffusion systems and the Wang—Peskin—Elston lattice discretization of the Fokker—Planck equation that describes drift-diffusion processes in which the drift arises from potential fields. In the FPKMC method, each molecule is enclosed within a "protective domain," either by itself or with a small number of other molecules. To sample when a molecule leaves its protective domain or a reaction occurs, the original FPKMC method relies on analytic solutions of one- and two-body diffusion equations within the protective domains, and therefore cannot be used in situations with non-constant drift. To allow for such drift in our new method (hereafter Dynamic Lattice FPKMC or DL-FPKMC), each molecule undergoes a continuous-time random walk on a lattice within its protective domain, and the lattices change adaptively over time. One of the most commonly used spatial models for stochastic reaction-diffusion systems is the Smoluchowski diffusion-limited reaction (SDLR) model. The DL-FPKMC method generates convergent realizations of an extension of the SDLR model that includes drift from potentials. We present detailed numerical results demonstrating the convergence and accuracy of our method for various types of potentials (smooth, discontinuous, and constant). We also present several illustrative applications of DL-FPKMC, including examples motivated by cell biology.

Much experimental evidence shows that the cytoskeleton is a downstream target and effector during cell death in numerous neurodegenerative diseases, including Parkinson's, Huntington's, and Alzheimer's diseases. However, recent evidence indicates that cytoskeletal dysfunction can also trigger neuronal death, by mechanisms as yet poorly understood. We studied a mathematical model of cytoskeleton-induced neuron death in which assembly control of the neuronal cytoskeleton interacts with both cellular stress levels and cytosolic free radical concentrations to trigger neurodegeneration. This trigger mechanism is further modulated by the presence of cell interactions in the form of a diffusible toxic factor released by dying neurons. We found that, consistent with empirical observations, the model produces one-hit exponential and sigmoid patterns of cell dropout. In all cases, cell dropout is exponential-tailed and described accurately by a gamma distribution. The transition between exponential and sigmoidal is gradual, and determined by a synergetic interaction between the magnitude of fluctuations in cytoskeleton assembly control and by the degree of cell coupling. We concluded that a single mechanism involving neuron interactions and fluctuations in cytoskeleton assembly control is compatible with the experimentally observed range of neuronal attrition kinetics. We also studied the transit of neurons through states intermediate between initial viability and cell death. We found that the stochastic flow of neuron fate, from viability to cell death, self-organizes into two distinct temporal phases. There is a rapid relaxation of the initial neuron population to a more disordered phase that is long-lived, or metastable, with respect to the time scales of change in single cells. Strikingly, cellular egress from this metastable phase follows the one-hit kinetic pattern of exponential decline now established as a principal hallmark of cell death in neurodegenerative disorders. Intermediate state metastability may therefore be an important element in the systems biology of one-hit neurodegeneration. Further, we studied the full spatiotemporal dynamics of death factor pulses released from dying neurons to emphasize the effects of the cell-to-cell coupling strength on neuron death rates. The rate of neuron cell loss monotonically increased with increased diffusion-dependent intercellular communication. Death factor diffusion effects may therefore be important moderators of one-hit neurodegeneration.

In this dissertation, we consider a complex biological system known as cortical microtubule (CMT) system, where stochastic dynamics of the components (i.e., the CMTs) are defined in both space and time. CMTs have an inherent spatial dimension of their own, as their length changes over time in addition to their location. As a result of their dynamics in a confined space, they run into and interact with each other according to simple stochastic rules. Over time, CMTs acquire an ordered structure that is achieved without any centralized control beginning with a completely disorganized system. It is also observed that this organization might be distorted, when parameters of dynamicity and interactions change due to genetic mutation or environmental conditions. The main question of interest is to explore the characteristics of this system and the drivers of its self-organization, which is not feasible relying solely on biological experiments. For this, we replicate the system dynamics and interactions using computer simulations. As the simulations successfully mimic the organization seen in plant cells, we conduct an extensive analysis to discover the effects of dynamics and interactions on system characteristics by experimenting with different input parameters. To compare simulation results, we characterize system properties and quantify organization level using metrics based on entropy, average length and number of CMTs in the system. Based on our findings and conjectures from simulations, we develop analytical models for more generalized conclusions and efficient computation of system metrics. As a fist step, we formulate a mean-field model, which we use to derive sufficient conditions for organization to occur in terms of input parameters. Next, considering the parameter ranges that satisfy these conditions, we develop predictive methodologies for estimation of expected average length and number of CMTs over time, using a fluid model, transient analysis, and approximation algorithms tailored to our problem. Overall, we build a comprehensive framework for analysis and control of microtubule organization in plant cells using a wide range of models and methodologies in conjunction. This research also has broader impacts related to the fields of bio-energy, healthcare, and nanotechnology; in addition to its methodological contribution to stochastic modeling of systems with high-level spatial and temporal complexity.

For cell-biological processes, it is the complex interaction of their biochemical components, affected by both stochastic and spatial considerations, that create the overall picture. Formal modeling provides a method to overcome the limits of experimental observation in the wet-lab by moving to the abstract world of the computer. The limits of the abstract world again depend on the expressiveness of the modeling language used to formally describe the system under study. In this thesis, reaction constraints for the pi-calculus are proposed as a language for the stochastic and spatial modeling of cell-biological processes. The goal is to develop a language with sufficient expressive power to model dynamic cell structures, like fusing compartments. To this end, reaction constraints are augmented with two language constructs: priority and a global imperative store, yielding two different modeling languages, including non-deterministic and stochastic semantics. By several modeling examples, e.g. of Euglena's phototaxis, and extensive expressiveness studies, e.g. an encoding of the spatial modeling language BioAmbients, including a prove of its correctness, the usefulness of reaction constraints, priority, and a global imperative store for the modeling of cell-biological processes is shown. Thereby, besides dynamic cell structures, different modeling styles, e.g. individual-based vs. population-based modeling, and different abstraction levels, as e.g. provided by reaction kinetics following the law of Mass action or the Michaelis-Menten theory, are considered.

This thesis presents a whole-embryo finite element model of neurulation -- the first of its kind. An advanced, multiscale finite element approach is used to capture the mechanical interactions that occur across cellular, tissue and whole-embryo scales. Cell-based simulations are used to construct a system of constitutive equations for embryonic tissue fabric evolution under different scenarios including bulk deformation, cell annealing, mitosis, and Lamellipodia effect. Experimental data are used to determine the parameters in these equations. Techniques for obtaining images of live embryos, serial sections of fixed embryo fabric parameters, and material properties of embryonic tissues are used. Also a spatial-temporal correlation system is introduced to organize and correlate the data and to construct the finite element model. Biological experiments have been conducted to verify the validity of this constitutive model. A full functional finite element analysis package has been written and is used to conduct computational simulations. A simplified contact algorithm is introduced to address the element permeability issue. Computational simulations of different cases have been conducted to investigate possible causes of neural tube defects. Defect cases including neural plate defect, non-neural epidermis defect, apical constriction defect, and convergent extension defect are compared with the case of normal embryonic development. Corresponding biological experiments are included to support these defect cases. A case with biomechanical feedbacks on non-neural epidermis is also discussed in detail with biological experiments and computational simulations. Its comparison with the normal case indicates that the introduction of biomechanical feedbacks can yield more realistic simulation results.

Inductive plasmas are simulated by using a one-dimensional particle-in-cell simulation including Monte Carlo collision techniques (pic/mcc). To model inductive heating, a non-uniform radio-frequency (rf) electric field, perpendicular to the electron motion is included into the classical particle-in-cell scheme. The inductive plasma pic simulation is used to confirm recent experimental results that electric double layers can form in current-free plasmas. These results differ from previous experimental or simulation systems where the double layers are driven by a current or by imposed potential differences. The formation of a super-sonic ion beam, resulting from the ions accelerated through the potential drop of the double layer and predicted by the pic simulation is confirmed with nonperturbative laser-induced fluorescence measurements of ion flow. It is shown that at low pressure, where the electron mean free path is of the order of, or greater than the system length, the electron energy distribution function (eedf) is close to Maxwellian, except for its tail which is depleted at energies higher than the plasma potential. Evidence supporting that this depletion is mostly due to the high-energy electrons escaping to the walls is given. ¶ A new hybrid simulation scheme (particle ions and Boltzmann/particle electrons), accounting for non-Maxwellian eedf and self-consistently simulating low-pressure high-density plasmas at low computational cost is proposed. Results obtained with the “improved” hybrid model are in much better agreement with the full pic simulation than the classical non self-consistent hybrid model. This model is used to simulate electronegative plasmas and to provide evidence supporting the fact that propagating double layers may spontaneously form in electronegative plasmas. It is shown that critical parameters of the simulation were very much aligned with critical parameters of the experiment.

Indiana University-Purdue University Indianapolis (IUPUI)
Cancer is still among those diseases that prominently contribute to the numerous deaths that are caused each year. But as technology and research is reaching new zeniths in the present times, cure or early detection of cancer is possible. The detection of rare cells can help understand the origin of many diseases. The current study deals with one such technology that is used for the capture or effective separation of these rare cells called Lab-on-a-chip microchip technology. The isolation and capture of rare cells is a problem uniquely suited to microfluidic devices, in which geometries on the cellular length scale can be engineered and a wide range of chemical functionalizations can be implemented. The performance of such devices is primarily affected by the chemical interaction between the cell and the capture surface and the mechanics of cell-surface collision and adhesion. This study focuses on the fundamental adhesion and transport mechanisms in rare cell-capture microdevices, and explores modern device design strategies in a transport context. The biorheology and engineering parameters of cell adhesion are defined; chip geometries are reviewed. Transport at the microscale, cell-wall interactions that result in cell motion across streamlines, is discussed. We have concentrated majorly on the fluid dynamics design of the chip. A simplified description of the device would be to say that the chip is at micro scale. There are posts arranged on the chip such that the arrangement will lead to a higher capture of rare cells. Blood consisting of rare cells will be passed through the chip and the posts will pose as an obstruction so that the interception and capture efficiency of the rare cells increases. The captured cells can be observed by fluorescence microscopy. As compared to previous studies of using solid microposts, we will be incorporating a new concept of cylindrical shell micropost. This type of micropost consists of a solid inner core and the annulus area is covered with a forest of silicon nanopillars. Utilization of such a design helps in increasing the interception and capture efficiency and reducing the hydrodynamic resistance between the cells and the posts. Computational analysis is done for different designs of the posts. Drag on the microposts due to fluid flow has a great significance on the capture efficiency of the chip. Also, the arrangement of the posts is important to contributing to the increase in the interception efficiency. The effects of these parameters on the efficiency in junction with other factors have been studied and quantified. The study is concluded by discussing design strategies with a focus on leveraging the underlying transport phenomena to maximize device performance.

The present habilitation thesis in theoretical biological physics addresses two central dynamical processes in cells and organisms: (i) active motility and motility control and (ii) self-organized pattern formation. The unifying theme is the nonlinear dynamics of biological function and its robustness in the presence of strong fluctuations, structural variations, and external perturbations. We theoretically investigate motility control at the cellular scale, using cilia and flagella as ideal model system. Cilia and flagella are highly conserved slender cell appendages that exhibit spontaneous bending waves. This flagellar beat represents a prime example of a chemo-mechanical oscillator, which is driven by the collective dynamics of molecular motors inside the flagellar axoneme. We study the nonlinear dynamics of flagellar swimming, steering, and synchronization, which encompasses shape control of the flagellar beat by chemical signals and mechanical forces. Mechanical forces can synchronize collections of flagella to beat at a common frequency, despite active motor noise that tends to randomize flagellar synchrony. In Chapter 2, we present a new physical mechanism for flagellar synchronization by mechanical self-stabilization that applies to free-swimming flagellated cells. This new mechanism is independent of direct hydrodynamic interactions between flagella. Comparison with experimental data provided by experimental collaboration partners in the laboratory of J. Howard (Yale, New Haven) confirmed our new mechanism in the model organism of the unicellular green alga Chlamydomonas. Further, we characterize the beating flagellum as a noisy oscillator. Using a minimal model of collective motor dynamics, we argue that measured non-equilibrium fluctuations of the flagellar beat result from stochastic motor dynamics at the molecular scale. Noise and mechanical coupling are antagonists for flagellar synchronization. In addition to the control of the flagellar beat by mechanical forces, we study the control of the flagellar beat by chemical signals in the context of sperm chemotaxis. We characterize a fundamental paradigm for navigation in external concentration gradients that relies on active swimming along helical paths. In this helical chemotaxis, the direction of a spatial concentration gradient becomes encoded in the phase of an oscillatory chemical signal. Helical chemotaxis represents a distinct gradient-sensing strategy, which is different from bacterial chemotaxis. Helical chemotaxis is employed, for example, by sperm cells from marine invertebrates with external fertilization. We present a theory of sensorimotor control, which combines hydrodynamic simulations of chiral flagellar swimming with a dynamic regulation of flagellar beat shape in response to chemical signals perceived by the cell. Our theory is compared to three-dimensional tracking experiments of sperm chemotaxis performed by the laboratory of U. B. Kaupp (CAESAR, Bonn). In addition to motility control, we investigate in Chapter 3 self-organized pattern formation in two selected biological systems at the cell and organism scale, respectively. On the cellular scale, we present a minimal physical mechanism for the spontaneous self-assembly of periodic cytoskeletal patterns, as observed in myofibrils in striated muscle cells. This minimal mechanism relies on the interplay of a passive coarsening process of crosslinked actin clusters and active cytoskeletal forces. This mechanism of cytoskeletal pattern formation exemplifies how local interactions can generate large-scale spatial order in active systems. On the organism scale, we present an extension of Turing’s framework for self-organized pattern formation that is capable of a proportionate scaling of steady-state patterns with system size. This new mechanism does not require any pre-pattering clues and can restore proportional patterns in regeneration scenarios. We analytically derive the hierarchy of steady-state patterns and analyze their stability and basins of attraction. We demonstrate that this scaling mechanism is structurally robust. Applications to the growth and regeneration dynamics in flatworms are discussed (experiments by J. Rink, MPI CBG, Dresden).:1 Introduction 10 1.1 Overview of the thesis 10 1.2 What is biological physics? 12 1.3 Nonlinear dynamics and control 14 1.3.1 Mechanisms of cell motility 16 1.3.2 Self-organized pattern formation in cells and tissues 28 1.4 Fluctuations and biological robustness 34 1.4.1 Sources of fluctuations in biological systems 34 1.4.2 Example of stochastic dynamics: synchronization of noisy oscillators 36 1.4.3 Cellular navigation strategies reveal adaptation to noise 39 2 Selected publications: Cell motility and motility control 56 2.1 “Flagellar synchronization independent of hydrodynamic interactions” 56 2.2 “Cell body rocking is a dominant mechanism for flagellar synchronization” 57 2.3 “Active phase and amplitude fluctuations of the flagellar beat” 58 2.4 “Sperm navigation in 3D chemoattractant landscapes” 59 3 Selected publications: Self-organized pattern formation in cells and tissues 60 3.1 “Sarcomeric pattern formation by actin cluster coalescence” 60 3.2 “Scaling and regeneration of self-organized patterns” 61 4 Contribution of the author in collaborative publications 62 5 Eidesstattliche Versicherung 64 6 Appendix: Reprints of publications 66
Das Thema der vorliegenden Habilitationsschrift in Theoretischer Biologischer Physik ist die nichtlineare Dynamik funktionaler biologischer Systeme und deren Robustheit gegenüber Fluktuationen und äußeren Störungen. Wir entwickeln hierzu theoretische Beschreibungen für zwei grundlegende biologische Prozesse: (i) die zell-autonome Kontrolle aktiver Bewegung, sowie (ii) selbstorganisierte Musterbildung in Zellen und Organismen. In Kapitel 2, untersuchen wir Bewegungskontrolle auf zellulärer Ebene am Modelsystem von Zilien und Geißeln. Spontane Biegewellen dieser dünnen Zellfortsätze ermöglichen es eukaryotischen Zellen, in einer Flüssigkeit zu schwimmen. Wir beschreiben einen neuen physikalischen Mechanismus für die Synchronisation zweier schlagender Geißeln, unabhängig von direkten hydrodynamischen Wechselwirkungen. Der Vergleich mit experimentellen Daten, zur Verfügung gestellt von unseren experimentellen Kooperationspartnern im Labor von J. Howard (Yale, New Haven), bestätigt diesen neuen Mechanismus im Modellorganismus der einzelligen Grünalge Chlamydomonas. Der Gegenspieler dieser Synchronisation durch mechanische Kopplung sind Fluktuationen. Wir bestimmen erstmals Nichtgleichgewichts-Fluktuationen des Geißel-Schlags direkt, wofür wir eine neue Analyse-Methode der Grenzzykel-Rekonstruktion entwickeln. Die von uns gemessenen Fluktuationen entstehen mutmaßlich durch die stochastische Dynamik molekularen Motoren im Innern der Geißeln, welche auch den Geißelschlag antreiben. Um die statistische Physik dieser Nichtgleichgewichts-Fluktuationen zu verstehen, entwickeln wir eine analytische Theorie der Fluktuationen in einem minimalen Modell kollektiver Motor-Dynamik. Zusätzlich zur Regulation des Geißelschlags durch mechanische Kräfte untersuchen wir dessen Regulation durch chemische Signale am Modell der Chemotaxis von Spermien-Zellen. Dabei charakterisieren wir einen grundlegenden Mechanismus für die Navigation in externen Konzentrationsgradienten. Dieser Mechanismus beruht auf dem aktiven Schwimmen entlang von Spiralbahnen, wodurch ein räumlicher Konzentrationsgradient in der Phase eines oszillierenden chemischen Signals kodiert wird. Dieser Chemotaxis-Mechanismus unterscheidet sich grundlegend vom bekannten Chemotaxis-Mechanismus von Bakterien. Wir entwickeln eine Theorie der senso-motorischen Steuerung des Geißelschlags während der Spermien-Chemotaxis. Vorhersagen dieser Theorie werden durch Experimente der Gruppe von U.B. Kaupp (CAESAR, Bonn) quantitativ bestätigt. In Kapitel 3, untersuchen wir selbstorganisierte Strukturbildung in zwei ausgewählten biologischen Systemen. Auf zellulärer Ebene schlagen wir einen einfachen physikalischen Mechanismus vor für die spontane Selbstorganisation von periodischen Zellskelett-Strukturen, wie sie sich z.B. in den Myofibrillen gestreifter Muskelzellen finden. Dieser Mechanismus zeigt exemplarisch auf, wie allein durch lokale Wechselwirkungen räumliche Ordnung auf größeren Längenskalen in einem Nichtgleichgewichtssystem entstehen kann. Auf der Ebene des Organismus stellen wir eine Erweiterung der Turingschen Theorie für selbstorganisierte Musterbildung vor. Wir beschreiben eine neue Klasse von Musterbildungssystemen, welche selbst-organisierte Muster erzeugt, die mit der Systemgröße skalieren. Dieser neue Mechanismus erfordert weder eine vorgegebene Kompartimentalisierung des Systems noch spezielle Randbedingungen. Insbesondere kann dieser Mechanismus proportionale Muster wiederherstellen, wenn Teile des Systems amputiert werden. Wir bestimmen analytisch die Hierarchie aller stationären Muster und analysieren deren Stabilität und Einzugsgebiete. Damit können wir zeigen, dass dieser Skalierungs-Mechanismus strukturell robust ist bezüglich Variationen von Parametern und sogar funktionalen Beziehungen zwischen dynamischen Variablen. Zusammen mit Kollaborationspartnern im Labor von J. Rink (MPI CBG, Dresden) diskutieren wir Anwendungen auf das Wachstum von Plattwürmern und deren Regeneration in Amputations-Experimenten.:1 Introduction 10 1.1 Overview of the thesis 10 1.2 What is biological physics? 12 1.3 Nonlinear dynamics and control 14 1.3.1 Mechanisms of cell motility 16 1.3.2 Self-organized pattern formation in cells and tissues 28 1.4 Fluctuations and biological robustness 34 1.4.1 Sources of fluctuations in biological systems 34 1.4.2 Example of stochastic dynamics: synchronization of noisy oscillators 36 1.4.3 Cellular navigation strategies reveal adaptation to noise 39 2 Selected publications: Cell motility and motility control 56 2.1 “Flagellar synchronization independent of hydrodynamic interactions” 56 2.2 “Cell body rocking is a dominant mechanism for flagellar synchronization” 57 2.3 “Active phase and amplitude fluctuations of the flagellar beat” 58 2.4 “Sperm navigation in 3D chemoattractant landscapes” 59 3 Selected publications: Self-organized pattern formation in cells and tissues 60 3.1 “Sarcomeric pattern formation by actin cluster coalescence” 60 3.2 “Scaling and regeneration of self-organized patterns” 61 4 Contribution of the author in collaborative publications 62 5 Eidesstattliche Versicherung 64 6 Appendix: Reprints of publications 66