The use of ultrasonic standing waves for contactless manipulation of microparticles in microfluidic systems is a field with potential to become a new standard tool in lab-on-chip systems. Compared to other contactless manipulation methods ultrasonic standing wave manipulation shows promises of gentle cell handling, low cost, and precise temperature control. The technology can be used both for batch handling, such as sorting and aggregation, and handling of single particles. This doctoral Thesis presents multi-dimensional ultrasonic manipulation, i.e., manipulation in both two and three spatial dimensions as well as time-dependent manipulation of living cells and microbeads in microfluidic systems. The lab-on-chip structures used allow for high-quality optical microscopy, which is central to many bio-applications. It is demonstrated how the ultrasonic force fields can be spatially confined to predefined regions in the system, enabling sequential manipulation functions. Furthermore, it is shown how frequency-modulated signals can be used both for spatial stabilization of the force fields as well as for flow-free transport of particles in a microchannel. Design parameters of the chip-transducer systems employed are investigated experimentally as well as by numerical simulations. It is shown that three-dimensional resonances in the solid structure of the chip strongly influences the resonance shaping in the channel. / QC 20100730

Cellular homeostasis is the effort of all living cells to maintain their intracellular content when facing physiological change(s) in the extracellular environment. To date, cellular homeostasis is known to be regulated mainly by time-consuming active mechanisms and via multiple signaling pathways within the cells. The aim of this thesis is to show that time-efficient passive (physical) mechanisms also, under the control and regulation of bio-physical factors such as cell morphology and distribution and co-localization of transport proteins in the cell membrane, can regulate cellular homeostasis. This thesis has been developed in an interface between physics and biology and focuses on critical cases in which cells face physiologically unstable environments at their steady state and therefore may need a constituent effort to maintain their homeostasis. The main hypothesis here is that the cell geometry is oriented in such a way that cellular homeostasis is preserved in a given environment. For exploring these cases, comparative spatial models have been developed that combine transporting function of membrane proteins with simple versus complex geometries of cells. Models confirm the hypothesis and show that cell morphology, size of extracellular space and intercellular distances are important for a dynamic regulation of water and ion homeostasis at steady state. The main clue is the existence of diffusion limited space (DLS) in the bulk extracellular space (ECS). DLS can, despite being ECS, maintain its ionic content and water balance due a controlled function of transport proteins in the membrane facing part of DLS. This can significantly regulate cellular water and ion homeostasis and play an important role in cell physiology. In paper I, the role of DLS is explored in the kidney whereas paper II addresses the brain. The response of cells to change in osmolarity is of critical importance for water homeostasis. Cells primarily respond to osmotic challenge by transport of water via their membranes. As water moves into or out of cells, the volumes of intra- and extracellular compartments consequently change. Water transport across the cell membrane is enhanced by a family of water channel proteins (aquaporins) which play important roles in regulation of both cell and the extracellular space dimensions. Paper III explores a role for aquaporins in renal K+ transport. Experimentally this role is suggested to be different from bulk water transport. In a geometrical model of a kidney principal cell with several DLS in the basolateral membrane, a biophysical role for DLS-aquaporins is suggested that also provides physiological relevance for this study. The biophysical function of water channels is then extensively explored in paper IV where the main focus has been the dynamics of the brain extracellular space following water transport. Both modeling and experimental data in this paper confirmed the importance of aquaporin-4 expressed in astrocytes for potassium kinetics in the brain extracellular space. Finally, geometrically controlled transport mechanisms are studied on a molecular level, using silicon particles as a simplified model system for cell studies (paper V and VI). In paper V the role of electrostatic forces (around the nano-pores and in between the loaded material and the silicon surface) is studied with regard to transport processes.  In paper VI the roles of pore size and molecular weight of loaded material are studied. All together this thesis presents various modeling approaches that employ biophysical aspects of transport mechanisms combined with cell geometry to explain cell homeostasis and address cell physiology-based questions. / QC20100727

<p>The recent decades of research in molecular biology have resulted in break-throughs concerning our knowledge of the genetic code, protein structures and functions of the different cellular components. With this new information follows an increased interest in constructing computational models of the biological systems. A computational model can range from a description of one specific protein to a complete cell or organism. The aim of a computational model is often to complement the experimental studies and help identify essential mechanisms of a system.</p><p>All processes taking place in our cells, from general metabolic processes to cell specific actions, originates from information encoded in our DNA. The first step in transferring the genetic information to a functional protein or RNA, is through the transcription of a gene. The transcription process is controlled by cellular proteins binding to DNA regions called promoters. The term "genetic switch", used in the title of this thesis, refers to a specific change in transcription activity, where one or several promoters get activated or silenced.</p><p>In this thesis, I present studies of the regulation mechanisms in two different genetic switches. The first is a switch between two central promoters in the Epstein- Barr virus. This human virus is mostly known for causing the ’kissing disease’, but is also coupled to several cancer types. Infected cells can change between a resting and a proliferating phenotype, depending on which viral promoter is active. In order to understand what causes uncontrolled proliferation in tumors, it is important to understand the regulation of these viral promoters. The other switch is present in the phage λ, a bacterial virus. This virus has one specific promoter region, controlling expression of two proteins that determine if the phage will remain silent (lysogenic) in the host cell, or start producing new viral particles (go lytic). For the Epstein- Barr virus we tested, and confirmed, the hypothesis that the regulation of the two central promoters can be obtained by only one viral and one human protein. Further, we studied the cooperative effects on one of the promoters, showing that steric hindrance at the promoter region results in a more effective switching than with only cooperative binding present. For the bacteriophage λ we studied the genetically altered λ- Lac mutants, presented by Little & Atsumi in 2006. We demonstrate that the experimental results cannot, in terms of its equilibria, be explained by the mechanisms generally believed to be in control of the lysogenic/ lytic switch.</p>

Cells live in inherently noisy environments. The machinery that cells use to sense their environment is also noisy. Yet, cells are exquisite environmental sensors, often approaching the limits of what is physically possible. This thesis investigates how the precision of environmental sensing is improved when cells behave collectively. We derive physical limits to cells' ability to collectively sense and respond to chemical concentrations and gradients. For concentration sensing, we find that when cell populations become sufficiently large, long-range communication can provide higher sensory precision than short-range communication, and that the optimal cell-cell separation in such a system can be large, due to a tradeoff between maintaining communication strength and reducing signal cross-correlations. We also show that concentration profiles formed diffusively are more precise for large profile lengths while those formed via directed transport are more precise for short profile lengths. These effects are due to increased molecule refresh rate and mean concentration respectively. For gradient sensing, we derive the sensory precision of the well-known the local excitation-global inhibition (LEGI) model and the more recently proposed regional excitation-global inhibition (REGI) model for two and three dimensional cell cluster geometries. We find that REGI systems achieve higher levels of precision than LEGI systems and give rise to optimally sensing geometries that are consistent with the shapes of naturally occurring gradient-sensing cell populations. Lastly, we analyze the precision with which migrating cell clusters can track a chemical gradient via an individual-based and emergent method. We show that one and two dimensional clusters utilizing the emergent chemotactic method have improved scaling with population size due to differences in the scaling properties of the variance in the total polarization. By completing these studies we aim to understand the limits and precise roles of collective behavior in environmental sensing.

<p>The main goal of data analysis is to summarize huge amount of data (as our observation) with a few numbers that come up us with some sort of intuition into the process that generated the data. Regardless of the method we use to analyze the data, the process of analysis includes (1) create the mathematical formulation for the problem, (2) data collection, (3) create a probability model for the data, (4) estimate the parameters of the model, and (5) summarize the results in a proper way-a process that is called ”statistical inference”.<br></p><p>Recently it has been suggested that using the concept of Bayesian approach and more specifically Bayesian nonparametrics (BNPs) is showed to have a deep influence in the area of data analysis [1], and in this field, they have just begun to be extracted [2–4]. However, to our best knowledge, there is no single resource yet avail-able that explain it, both its concepts, and implementation, as would be needed to bring the capacity of BNPs to relieve on data analysis and accelerate its unavoidable extensive acceptance.<br></p><p>Therefore, in this dissertation, we provide a description of the concepts and implementation of an important, and computational tool that extracts BNPs in this area specifically its application in the field of biophysics. Here, the goal is using BNPs to understand the rules of life (in vivo) at the scale at which life occurs (single molecule)from the fastest possible acquirable data (single photons).<br></p><p>In chapter 1, we introduce a brief introduction to Data Analysis in biophysics.Here, our overview is aimed for anyone, from student to established researcher, who plans to understand what can be accomplished with statistical methods to modeling and where the field of data analysis in biophysics is headed. For someone just getting started, we present a special on the logic, strengths and shortcomings of data analysis frameworks with a focus on very recent approaches.<br></p><p>In chapter 2, we provide an overview on data analysis in single molecule bio-physics. We discuss about data analysis tools and model selection problem and mainly Bayesian approach. We also discuss about BNPs and their distinctive characteristics that make them ideal mathematical tools in modeling of complex biomolecules as they offer meaningful and clear physical interpretation and let full posterior probabilities over molecular-level models to be deduced with minimum subjective choices.<br></p><p>In chapter 3, we work on spectroscopic approaches and fluorescence time traces.These traces are employed to report on dynamical features of biomolecules. The fundamental unit of information came from these time traces is the single photon.Individual photons have information from the biomolecule, from which they are emit-ted, to the detector on timescales as fast as microseconds. Therefore, from confocal microscope viewpoint it is theoretically feasible to monitor biomolecular dynamics at such timescales. In practice, however, signals are stochastic and in order to derive dynamical information through traditional means such as fluorescence correlation spectroscopy (FCS) and related methods fluorescence time trace signals are gathered and temporally auto-correlated over many minutes. So far, it has been unfeasible to analyze dynamical attributes of biomolecules on timescales near data acquisition as this requests that we estimate the biomolecule numbers emitting photons and their locations within the confocal volume. The mathematical structure of this problem causes that we leave the normal (”parametric”) Bayesian paradigm. Here, we utilize novel mathematical tools, BNPs, that allow us to extract in a principled fashion the same information normally concluded from FCS but from the direct analysis of significantly smaller datasets starting from individual single photon arrivals. Here, we specifically are looking for diffusion coefficient of the molecules. Diffusion coefficient allows molecules to find each other in a cell and at the cellular level, determination of the diffusion coefficient can provide us valuable insights about how molecules interact with their environment. We discuss the concepts of this method in assisting significantly reduce phototoxic damage on the sample and the ability to monitor the dynamics of biomolecules, even down to the single molecule level, at such timescales.<br></p><p>In chapter 4, we present a new approach to infer lifetime. In general, fluorescenceLifetime Imaging (FLIM) is an approach which provides us information on the numberof species and their associated lifetimes. Current lifetime data analysis methods relyon either time correlated single photon counting (TCSPC) or phasor analysis. These methods require large numbers of photons to converge to the appropriate lifetimes and do not determine how many species are responsible for those lifetimes. Here, we propose a new method to analyze lifetime data based on BNPs that precisely takes into account several experimental complexities. Using BNPs, we can not only identify the most probable number of species but also their lifetimes with at least an order magnitudes less data than competing methods (TCSPC or phasors). To evaluate our method, we test it with both simulated and experimental data for one, two, three and four species with both stationary and moving molecules. Also, we compare our species estimate and lifetime determination with both TCSPC and phasor analysis for different numbers of photons used in the analysis.<br></p><p>In conclusion, the basis of every spectroscopic method is the detection of photons.Photon arrivals encode complex dynamical and chemical information and methods to analyze such arrivals have the capability to reveal dynamical and chemical processes on fast timescales. Here, we turn our attention to fluorescence lifetime imaging and single spot fluorescence confocal microscopy where individual photon arrivals report on dynamics and chemistry down to the single molecule level. The reason this could not previously be achieved is because of the uncertainty in the number of chemical species and numbers of molecules contributing for the signal (i.e., responsible for contributing photons). That is, to learn dynamical or kinetic parameters (like diffusion coefficients or lifetime) we need to be able to interpret which photon is reporting on what process. For this reason, we abandon the parametric Bayesian paradigm and use the nonparametric paradigm that allows us to flexibly explore and learn numbers of molecules and chemical reaction space. We demonstrate the power of BNPs over traditional methods in single spot confocal and FLIM analysis in fluorescence lifetime imaging.<br></p>

<p>    </p> <p>Migration is a ubiquitous phenomenon in biology and is relevant to all scales ranging from bacteria to human beings. It is relevant to fundamental biological processes like bacterial chemotaxis, development, disease progression, etc. So, understanding migration is pivotal to addressing fundamental questions in biology. We address three broad questions relevant to cell migration using models from physics: (i) What are the critical features of cancer cell migration? (ii) Is it possible to explain complex cell migration data using minimal bio- chemical networks? And (iii) how does cell-to-cell communication affect its migration at the population level? To address these questions we performed (i) mathematical analysis using the Cellular Potts model, simulations using the Biased Persistent random walk model, and steady-state analysis of cell response to graded signals to explain cancer cell migration in response to single and multiple chemical and mechanical signals, (ii) rigorous network anal- ysis of ∼ 500,000 minimal networks having features of fundamental biochemical processes like regulation, conversion or molecular binding to understand the origin of antagonism in multiple cue cancer cell migration experiments and (iii) the steady-state analysis of Keller- Segel equations mimicking collective cell migration to understand the role of cell to cell communication on chemotaxis of a bacterial population. From our analysis, we found that (i) persistence and bias in cancer cell migration are decoupled from each other owing to a lack of memory about past movements and for any general cell migration they are inherently constrained to take only a fixed set of values. (ii) Bias in cancer cell migration in response to a combination of chemoattractant gradients can be less than the response to individual gradients (antagonism in bias) while the speed remains unaltered. This antagonism in bias and lack thereof in speed can be explained by several minimal networks having molecular regulation, conversion, or binding as its central feature and all these distinct mechanisms show convergence and saturation of an internal molecule common to both the chemoattrac- tants. (iii) By analyzing the role of cell-cell communication in bacterial chemotaxis using the Keller-Segel model we find that communication enhances chemotaxis only when it is adaptive to its external surroundings and cell-to-cell variability helps in increasing the chemotactic drift in the bacterial population. </p>

Bien que grandement complexes, les animaux, les tissus vivants et les cellules, la plus petite unité de vie, sont assujettis aux lois de la physique. Dans les tissus vivants, des processus de régulation permanents ou transitoires, tels que des interactions biochimiques et mécaniques entre une cellule isolée et son environnement, sont essentiel au développement et au maintient de la structure et des fonctions du tissu. Ces interactions interviennent dans des processus biologiques tel que la différentiation cellulaire et l'expression génétique. Les cellules cancéreuses et les métastases échappent au contraire à toutes régulations. Elles prolifèrent et migrent à travers les tissus, ignorant les signaux de régulation environnant. Leurs propriétés mécaniques et d'adhésion sont très différentes de celles des cellules saines. L'étude suivante présente différentes expériences qui cherchent à mimer les conditions in vivo en appliquant un stress uniaxial à une cellule isolée sous conditions physiologiques. Simultanément, la force appliquée à la cellule, sa déformation et sa forme, sont mesurées. La déformation uniaxiale est appliquée à une cellule isolée adhérant sur deux plaques de verre. De tels essais mécaniques réalisés à constante déformation ou à constante force permettent la quantification des propriétés mécaniques cellulaire et une description physique des données par le modèle de Kelvin. Des lipides bioactifs tel que la sphingosylphosphorylcholine et l'acide lysophosphatidique, modifient l'architecture du cytosquelette. Ces modifications influencent fortement les propriétés mécaniques des fibroblastes ou les cellules cancéreuses du pancréas.

Les assemblages dynamiques d'actine sont à l'origine de forces intracellulaires présentes tant dans les processus physiologiques que pathologiques comme l'invasion tumorale. Pour s'échapper du foyer primaire, les cellules néoplasiques étendent une protrusion grâce aux forces générées par polymérisation d'actine dirigée puis exercent des forces de traction via des câbles composés d'actine et de myosine. Le but de cet étude est de caractériser ces forces par micromanipulation. Dans une première partie, nous décrivons une étude préliminaire de la traction cellulaire par une expérience d'étirement uniaxial sur fibroblaste unique. Les forces générées par polymérisation d'actine font l'objet de la deuxième partie de ce travail. La bactérie Listeria monocytogenes a été largement utilisée comme modèle pour identifier les composants biochimiques de la polymérisation dirigée d'actine. Afin de mesurer directement les forces mises en jeu dans ce processus, nous avons développé une expérience de micromanipulation sur un système qui mime la propulsion de cette bactérie. Une comète croissant à partir d'une bille recouverte de protéine et fixée à un senseur de force est maintenue grâce à une micropipette. Nous appliquons à ce système des forces de traction ou de compression de plusieurs nanonewtons. Une sollicitation rapide nous permet de mesurer le module d'élasticité de la comète et la force d'adhésion entre la comète et la bille. La relation force vitesse du système est déterminée, par l'application d'une force constante, et expliquée à partir d'une analyse élastique qui permet de comprendre la robustesse d'un tel mouvement. Enfin la transition vers une instabilité dynamique de type stick-slip est mise en évidence en variant la vitesse imposée au système.

Les cyanobactéries sont les organismes les plus simples connus possédant une horloge circadienne. Cette horloge produit des rythmes stables, dont la période est proche à 24 heures et peut être entraînée à exactement 24h par des signaux externes, comme des cycles d'éclairage ou de température. Dans ce travail, nous montrons que cette horloge biologique se comporte comme un oscillateur de phase. De plus, sous influence d'un entraînement externe, son comportement peut être simplement décrit par le modèle d'Adler. Pour le montrer, nous avons réalisé des expériences sur des populations, en utilisant des cycles d'éclairage ou de température qui entraînent l'horloge circadienne à des phases différentes. Nous détaillons le montage expérimental qui permet de surveiller en permanence l'horloge circadienne de la cyanobactérie; et ce pendant plusieurs semaines consécutives. Nous montrons comment démasquer des perturbations supplémentaires du rapporteur de bioluminescence et nous quantifions la force de couplage entre l'horloge et l'entraînement externe. Par ajustement numérique des données expérimentales nous montrons que le modèle utilisé reproduit très bien le comportement observé. Via des simulations, nous cherchons les effets d'une distribution de phases initiales à l'intérieur de la population, ainsi que l'effet d'un bruit éventuel sur la phase ou d'une distribution des fréquences propres. Nous proposons également un nouveau concept pour les dispositifs à cellule unique pour observer plus en détail les effets du bruit sur cette horloge biologique. Ces dispositifs sont conçus pour des expériences à long terme (> 20 générations) d'observation de bactéries individuelles à l'intérieur d'une population.

La protéine SufA d'Escherichia coli est une protéine homodimérique, dite d'échafaudage, qui permet l'assemblage de centres [Fe-S] avant de les transmettre à une protéine cible possèdant une fonction biologique. Elle appartient à un ensemble de protéines organisées en opéron (SUF) qui semble activé dans le cadre de stress oxydatif. Afin de mieux comprendre le mode de fixation du centre [Fe-S] par la protéine SufA, la détermination de la structure tridimensionnelle par RMN a été entreprise. Nous avons obtenu la structure monomérique de la protéine sans son centre [Fe-S] (forme apo) et effectué des études de résonance magnétique nucléaire (RMN) préliminaires de la protéine SufA avec son centre métallique reconstitué. Nous avons pu ainsi observer trois résidus cystéine, que des études biochimiques ont montré comme étant impliqués dans la chélation du centre [Fe-S], et affirmer qu'ils étaient proches du site métallique sans toutefois définir exactement le mode de coordination du centre [Fe-S]. La détermination de la structure du dimère, en cours, et des études RMN complémentaires sur l'holoprotéine (avec son centre [Fe-S]) devrait nous permettre de répondre à ces questions toujours en suspens. D'autre part nous avons observé des différences structurales par rapport aux structures cristallographiques (publiées au cours de ce travail de thèse), particulièrement dans la partie C-terminale de la protéine où se situent deux des cystéines observées.