BACKGROUND:A hub protein is one that interacts with many functional partners. The annotation of hub proteins, or more generally the protein-protein interaction "degree" of each gene, requires quality genome-wide data. Data obtained using yeast two-hybrid methods contain many false positive interactions between proteins that rarely encounter each other in living cells, and such data have fallen out of favor.RESULTS:We find that protein "stickiness", measured as network degree in ostensibly low quality yeast two-hybrid data, is a more predictive genomic metric than the number of functional protein-protein interactions, as assessed by supposedly higher quality high throughput affinity capture mass spectrometry data. In the yeast Saccharomyces cerevisiae, a protein's high stickiness, but not its high number of functional interactions, predicts low stochastic noise in gene expression, low plasticity of gene expression across different environments, and high probability of forming a homo-oligomer. Our results are robust to a multiple regression analysis correcting for other known predictors including protein abundance, presence of a TATA box and whether a gene is essential. Once the higher stickiness of homo-oligomers is controlled for, we find that homo-oligomers have noisier and more plastic gene expression than other proteins, consistent with a role for homo-oligomerization in mediating robustness.CONCLUSIONS:Our work validates use of the number of yeast two-hybrid interactions as a metric for protein stickiness. Sticky proteins exhibit low stochastic noise in gene expression, and low plasticity in expression across different environments.

Genetically identical cells and organisms growing in homogenous environmental conditions can show significant phenotypic variation. Furthermore, mutations often have consequences that vary among individuals (incomplete penetrance). Biochemical processes such as those involved in gene expression are subjected to fluctuations due to their inherent probabilistic nature. However, it is not clear how these fluctuations affect multicellular organisms carrying mutations and if stochastic variation in gene expression among individuals could confer any advantage to populations. We have investigated the consequences of stochastic gene expression using the nematode Caenorhabditis elegans as a model. Here we show that inter-individual stochastic variation in the induction of both specific and more general buffering systems combine to determine the outcome of inherited mutations in each individual. Also, we demonstrate that genetic and environmental robustness are coupled in C. elegans. Individuals with higher induction of stress response are more robust to the effect of mutations, however they incur a fitness cost, thus suggesting that variation at the population level could be beneficial in unpredictable environments. / Células y organismos genéticamente idénticos y creciendo en un ambiente homogéneo pueden mostrar diferencias en sus fenotipos. Además, una misma mutación puede afectar de un modo distinto a individuos de una misma población. Es sabido que los procesos bioquímicos responsables de la expresión de genes están sujetos a fluctuaciones debido a su inherentemente naturaleza probabilística. Sin embargo, el rol que juegan estas fluctuaciones en individuos portadores de mutaciones ha sido poco estudiado, así cómo si la expresión estocástica de genes puede conferir alguna ventaja al nivel poblacional. Para investigar las consecuencias de la expresión estocástica de genes usamos como modelo al nemátodo Caenorhabditis elegans. En este trabajo demostramos que existe variación entre individuos en la inducción de mecanismos (tanto gen-específicos como globales) que confieren robustez al desarrollo. En consecuencia, diferencias fenotípicas entre mutantes están determinadas por su variación. También, demostramos que la robustez a perturbaciones genéticos y ambientales están estrechamente ligadas en C. elegans. Individuos que inducen estocásticamente una mayor respuesta a stress, están fenotípicamente mejor protegidos al efecto de mutaciones pero incurren en un costo reproductivo importante. Eso sugiere, que variaciones estocásticas al nivel poblacional pueden ser benéficas cuando las poblaciones afrontan ambientes impredecibles.

The stochastic nature of intracellular chemistry guarantees that even genetically identical cells sharing an environment will differ in composition. The question of whether this chemical diversity translates into significant phenotypic individuality is tied to the relative timescales of the processes involved. In order for cells in a population to have distinct functional identities, they must maintain their states for an appreciable period of time. Quantification of these timescales requires accurate time-lapse measurements covering tens or even hundreds of generations, a technical hurdle that has left these questions largely underexplored. In this thesis I present three pieces of work that aim to provide a foundation for the study of fluctuation timescales in bacteria. In the first part, I describe modifications to a recently developed microfluidic platform for continuous culture of cells under constant conditions. These revised devices enable the high-throughput, long-term measurement of gene expression dynamics while eliminating several confounding experimental factors that interfere with timescale measurements. In the second part, I employ one of these devices to survey fluctuation timescales in ~50 reporters for Eshcerichia coli gene expression. Under rich conditions, all reporters exhibited nearly identical, rapid fluctuation dynamics that were captured by a simple model of gene expression. In contrast, under poor nutritional conditions gene expression states became correlated over several cell divisions. However, accounting for instantaneous growth rate fluctuations eliminated these slow timescales, revealing an exceedingly simple behavior. In the third part, I describe our work to dissect the stochastic transition between the solitary motile state and sessile multicellular state in exponentially growing Bacillus subtilis</italic.. By enforcing static environmental conditions, we uncover the cell's internal strategies for state switching. The transition to the multicellular state occurs without regard to the cell's state history, whereas commitment to the multicellular state is tightly timed. By manipulating the genetic circuit responsible for the switch, we also expose surprising functional modularity in the commitment. I believe that the striking range of gene expression timescales we observe--from the fast fluctuations in E. coli gene expression to the feedback-amplified noise in B. subtilis--will serve as a useful starting point for future studies.

No description available.

Stochasticity is a ubiquitous feature of cellular processes such as gene expression that can give rise to phenotypic differences for genetically identical cells. Understanding how the underlying biochemical reactions give rise to variations in mRNA/protein levels is thus of fundamental importance to diverse cellular processes. Recent technological developments have enabled single-cell measurements of cellular macromolecules which can shed new light on processes underlying gene expression. Correspondingly, there is a need for the development of theoretical tools to quantitatively model stochastic gene expression and its consequences for cellular processes. In this dissertation, we address this need by developing general stochastic models of gene expression. By mapping the system to models analyzed in queueing theory, we derive analytical expressions for the noise in steady-state protein distributions. Furthermore, given that the underlying processes are intrinsically stochastic, cellular regulation must be designed to control the`noise' in order to adapt and respond to changing environments. Another focus of this dissertation is to develop and analyze stochastic models of post-transcription regulation. The analytical solutions of the models proposed provide insight into the effects of different mechanisms of regulation and the role of small RNAs in fine-tunning the noise in gene expression. The results derived can serve as building blocks for future studies focusing on regulation of stochastic gene expression. / Ph. D.

The methods that fueled the microscale revolution (top-down design/fabrication, combined with application of forces large enough to overpower stochasticity) constitute an approach that will not scale down to nanoscale systems. In contrast, in nanotechnology, we strive to embrace nature’s quite different paradigms to create functional systems, such as self-assembly to create structures, exploiting stochasticity, rather than overwhelming it, in order to create deterministic, yet highly adaptable, behavior. Nature’s approach, through billions of years of evolutionary development, has achieved self-assembling, self-duplicating, self-healing, adaptive systems. Compared to microprocessors, nature’s approach has achieved eight orders of magnitude higher memory density and three orders of magnitude higher computing capacity while utilizing eight orders of magnitude less power. Perhaps the most complex of functions, homeostatis by a biological cell – i.e., the regulation of its internal environment to maintain stability and function – in a fluctuating and unpredictable environment, emerges from the interactions between perhaps 50M molecules of a few thousand different types. Many of these molecules (e.g. proteins, RNA) are produced in the stochastic processes of gene expression, and the resulting populations of these molecules are distributed across a range of values. So although homeostasis is maintained at the system (i.e. cell) level, there are considerable and unavoidable fluctuations at the component (protein, RNA) level. While on at least some level, we understand the variability in individual components, we have no understanding of how to integrate these fluctuating components together to achieve complex function at the system level. This thesis will explore the regulation and control of stochasticity in cells. In particular, the focus will be on (1) how genetic circuits use noise to generate more function in less space; (2) how stochastic and deterministic responses are co-regulated to enhance function at a system level; and (3) the development of high-throughput analytical techniques that enable a comprehensive view of the structure and distribution of noise on a whole organism level.

Research on bacteria in their biofilm form is limited by the ability to artificially culture bacterial biofilms in a system that permits the visualization of individual cells. The experiments comprising this thesis research are on-going investigations of bacterial culture systems engineered to provide an environment that mimics biofilms while enabling real-time microscopy. Specifically, the microfluidic systems developed and assessed as part of this thesis permit the visualization of individual bacteria cells within consortia growing within a narrow space provided by a microfluidic device. This research demonstrates the versatility of these microfluidic systems across potentially high-throughput microbiological experiments utilizing genetically engineered Escherichia coli. Before demonstrating the efficacy of these systems, the development of the field of synthetic biology over the past half century is reviewed, focusing on synthetic genetic circuits and their applications (Chapter 2). The first and main microfluidic device explored in this research was developed to mimic the nutrient-deficient conditions within biofilms by forcing media to enter the culture area through a narrow, torturous channel. The microfluidic channel was thin enough (0.97 μm) to prevent the motility of 1-μm-wide E. coli cells, enabling visualization of individual cells. The bacteria cultured in the device contained either a simple Plux-driven quorum sensing receiver (Chapters 3 and 5) or a LacI- and TetR-driven genetic toggle switch (Chapter 4). Under the culture conditions, the quorum sensing reporter signal was detected even without addition of the signaling molecule (Chapter 3). The genetic toggle switch was stable when the system began in the high-LacI expression state, but after 5 days of culture, >5% of high-TetR expression cells began to consistently express the high-LacI state (Chapter 4). This system was also employed to track lineages of cells using real-time microscopy, which successfully characterized the inheritance of aberrant, enlarged cell phenotypes under stress (Chapter 5). Another microfluidic device, a droplet bioreactor, was also developed to culture small numbers of cells in an aqueous bubble suspended in oil (Chapter 6). Quorum sensing receiver cellswere cultured in this device, demonstrating that it is well suited for testing the effects of compounds on biofilms within water-in-oil droplets. / Ph. D. / Bacteria are the most abundant organisms globally, yet relatively little is understood about the basic biology of biofilms, one of the most common natural states of bacteria. Biofilms are ubiquitous consortia of individual microbial cells that send and received chemical signals from one another to carry out group behaviors such as quorum sensing. The impacts of biofilms range from the contamination of food processing equipment to antibiotic resistant bacterial infections. The vast majority of microbiological research has been conducted on bacteria in their planktonic state as individual cells cultured in a liquid medium. This form of culture does not permit the types of research that can help address the impacts of biofilms on human health and economic activities, never mind examine the biological mechanism of random gene and morphological expression within bacterial biofilm. This thesis presents research utilizing two microfluidic devices that will enable further large-scale studies to unravel the mechanisms that create biofilms as well as permit high-throughput testing of chemical compounds to control the growth and development of biofilms. Moreover, these devices permit the use of real-time microscopy to track cells and their growth over time. The first microfluidic device utilized in this research mimics the nutrient-limiting conditions of biofilms. This biofilm-mimicking device was used to culture a common research bacteria, Escherichia coli, with one of two engineered genetic circuits (reviewed in Chapter 2): a quorum sensing receiver (Chapters 3 and 5) or genetic toggle switch (Chapter 4). Both of these genetic circuits demonstrated stochasticity in their gene expression states under the culture conditions in the biofilm-mimicking device. The second microfluidic device successfully permitted the culture of small numbers of isolated cells within a small bubble of bacterial media suspended in oil (Chapter 6). Additionally, this device enabled the addition of chemical compounds to influence the growth and metabolism of the trapped cells. Collectively, these microfluidic devices provide the ability to effectively study both the mechanisms underlying random gene expression within biofilms as well as explore the chemical factors that can be used to control and mitigate biofilm formation and growth.

L'expression des gènes dans une cellule a longtemps été observable uniquement à travers des quantités moyennes mesurées sur des populations. L'arrivée des techniques «single-cell» permet aujourd'hui d'observer des niveaux d'ARN et de protéines dans des cellules individuelles : il s'avère que même dans une population de génome identique, la variabilité entre les cellules est parfois très forte. En particulier, une description moyenne est clairement insuffisante étudier la différenciation cellulaire, c'est-à-dire la façon dont les cellules souches effectuent des choix de spécialisation. Dans cette thèse, on s'intéresse à l'émergence de tels choix à partir de réseaux de régulation sous-jacents entre les gènes, que l'on souhaiterait pouvoir inférer à partir de données. Le point de départ est la construction d'un modèle stochastique de réseaux de gènes capable de reproduire les observations à partir d'arguments physiques. Les gènes sont alors décrits comme un système de particules en interaction qui se trouve être un processus de Markov déterministe par morceaux, et l'on cherche à obtenir un modèle statistique à partir de sa loi invariante. Nous présentons deux approches : la première correspond à une approximation de champ assez populaire en physique, pour laquelle nous obtenons un résultat de concentration, et la deuxième se base sur un cas particulier que l'on sait résoudre explicitement, ce qui aboutit à un champ de Markov caché aux propriétés intéressantes / Gene expression in a cell has long been only observable through averaged quantities over cell populations. The recent development of single-cell transcriptomics has enabled gene expression to be measured in individual cells: it turns out that even in an isogenic population, the molecular variability can be very important. In particular, an averaged description is not sufficient to account for cell differentiation. In this thesis, we are interested in the emergence of such cell decision-making from underlying gene regulatory networks, which we would like to infer from data. The starting point is the construction of a stochastic gene network model that is able to explain the data using physical arguments. Genes are then seen as an interacting particle system that happens to be a piecewise-deterministic Markov process, and our aim is to derive a tractable statistical model from its stationary distribution. We present two approaches: the first one is a popular field approximation, for which we obtain a concentration result, and the second one is based on an analytically tractable particular case, which provides a hidden Markov random field with interesting properties

One of the central themes of biology is to understand how individual cells achieve a high fidelity in gene expression. Each cell needs to ensure accurate protein levels for its proper functioning and its capability to proliferate. Therefore, complex regulatory mechanisms have evolved in order to render the expression of each gene dependent on the expression level of (all) other genes. Regulation can occur at different stages within the framework of the central dogma of molecular biology. One very effective and relatively direct mechanism concerns the regulation of the stability of mRNAs. All organisms have evolved diverse and powerful mechanisms to achieve this. In order to better comprehend the regulation in living cells, biochemists have studied specific degradation mechanisms in detail. In addition to that, modern high-throughput techniques allow to obtain quantitative data on a global scale by parallel analysis of the decay patterns of many different mRNAs from different genes. In previous studies, the interpretation of these mRNA decay experiments relied on a simple theoretical description based on an exponential decay. However, this does not account for the complexity of the responsible mechanisms and, as a consequence, the exponential decay is often not in agreement with the experimental decay patterns. We have developed an improved and more general theory of mRNA degradation which provides a general framework of mRNA expression and allows describing specific degradation mechanisms. We have made an attempt to provide detailed models for the regulation in different organisms. In the yeast S. cerevisiae, different degradation pathways are known to compete and furthermore most of them rely on the biochemical modification of mRNA molecules. In bacteria such as E. coli, degradation proceeds primarily endonucleolytically, i.e. it is governed by the initial cleavage within the coding region. In addition, it is often coupled to the level of maturity and the size of the polysome of an mRNA. Both for S. cerevisiae and E. coli, our descriptions lead to a considerable improvement of the interpretation of experimental data. The general outcome is that the degradation of mRNA must be described by an age-dependent degradation rate, which can be interpreted as a consequence of molecular aging of mRNAs. Within our theory, we find adequate ways to address this much debated topic from a theoretical perspective. The improvements of the understanding of mRNA degradation can be readily applied to further comprehend the mRNA expression under different internal or environmental conditions such as after the induction of transcription or stress application. Also, the role of mRNA decay can be assessed in the context of translation and protein synthesis. The ultimate goal in understanding gene regulation mediated by mRNA stability will be to identify the relevance and biological function of different mechanisms. Once more quantitative data will become available, our description allows to elaborate the role of each mechanism by devising a suitable model. / Ein zentrales Ziel der modernen Biologie ist es, ein umfassendes Verständnis der Genexpression zu erlangen. Die fundamentalen Prozesse sind im zentralen Dogma der Genexpression zusammengefasst: Die genetische Information wird von DNA in Boten-RNAs (mRNA) transkribiert und im Prozess der Translation von mRNA in Proteine übersetzt. Zum Erhalt ihrer Funktionalität und der Möglichkeit von Wachstum und Fortpflanzung muss in jeder Zelle und für jedes Gen die optimale Proteinkonzentration akkurat eingestellt werden. Hierzu hat jeder Organismus detaillierte Regulationsmechanismen entwickelt. Regulation kann auf allen Stufen der Genexpression erfolgen, insbesondere liefert der Abbau der mRNA-Moleküle einen effizienten und direkten Kontrollmechanismus. Daher sind in allen Lebewesen spezifische Mechanismen - die Degradationsmechanismen - entstanden, welche aktiv den Abbau befördern. Um ein besseres Verständnis von den zugrunde liegenden Prozessen zu erlangen, untersuchen Biochemiker die Degradationsmechanismen im Detail. Gleichzeitig erlauben moderne molekularbiologische Verfahren die simultane Bestimmung der Zerfallskurven von mRNA für alle untersuchten Gene einer Zelle. Aus theoretischer Perspektive wird der Zerfall der mRNA-Menge als exponentieller Zerfall mit konstanter Rate betrachtet. Diese Betrachtung dient der Interpretation der zugrunde liegenden Experimente, berücksichtigt aber nicht die fundierten Kenntnisse über die molekularen Mechanismen der Degradation. Zudem zeigen viele experimentelle Studien ein deutliches Abweichen von einem exponentiellen Zerfall. In der vorliegenden Doktorarbeit wird daher eine erweiterte theoretische Beschreibung für die Expression von mRNA-Molekülen eingeführt. Insbesondere lag der Schwerpunkt auf einer verbesserten Beschreibung des Prozesses der Degradation. Die Genexpression kann als ein stochastischer Prozess aufgefasst werden, in dem alle Einzelprozesse auf zufällig ablaufenden chemischen Reaktionen basieren. Die Beschreibung erfolgt daher im Rahmen von Methoden der stochastischen Modellierung. Die fundamentale Annahme besteht darin, dass jedes mRNA-Molekül eine zufällige Lebenszeit hat und diese Lebenszeit für jedes Gen durch eine statistische Lebenszeitverteilung gegeben ist. Ziel ist es nun, spezifische Lebenszeitverteilungen basierend auf den molekularen Degradationsmechanismen zu finden. In dieser Arbeit wurden theoretische Modelle für die Degradation in zwei verschiedenen Organismen entwickelt. Zum einen ist bekannt, dass in eukaryotischen Zellen wie dem Hefepilz S. cerevisiae mehrere Mechanismen zum Abbau der mRNA-Moleküle in Konkurrenz zueinander stehen. Zudem ist der Abbau durch mehrere geschwindigkeitsbestimmende biochemische Schritte charakterisiert. In der vorliegenden Arbeit wurden diese Feststellungen durch ein theoretisches Modell beschrieben. Eine Markow-Kette stellte sich als sehr erfolgreich heraus, um diese Komplexität in eine mathematisch-fassbare Form abzubilden. Zum anderen wird in Kolibakterien die Degradation überwiegend durch einen initialen Schnitt in der kodierenden Sequenz der mRNA eingeleitet. Des Weiteren gibt es komplexe Wechselwirkungen mit dem Prozess der Translation. Die dafür verantwortlichen Enzyme - die Ribosomen - schützen Teile der mRNA und vermindern dadurch deren Zerfall. In der vorliegenden Arbeit wurden diese Zusammenhänge im Rahmen eines weiteren spezifischen, theoretischen Modells untersucht. Beide Mechanismen konnten an experimentellen Daten verifiziert werden. Unter anderem konnten dadurch die Interpretation der Zerfallsexperimente deutlich verbessert und fundamentale Eigenschaften der mRNA-Moleküle bestimmt werden. Ein Vorteil der statistischen Herangehensweise in dieser Arbeit liegt darin, dass theoretische Konzepte für das molekulare Altern der mRNAs entwickelt werden konnten. Mit Hilfe dieser neuentwickelten Methode konnte gezeigt werden, dass sich die Komplexität der Abbaumechanismen in einem Alterungsprozess manifestiert. Dieser kann mit der Lebenserwartung von einzelnen mRNA-Molekülen beschrieben werden. In dieser Doktorarbeit wurde eine verallgemeinerte theoretische Beschreibung des Abbaus von mRNAMolek ülen entwickelt. Die zentrale Idee basiert auf der Verknüpfung von experimentellen Zerfallsmessungen mit den biochemischen Mechanismen der Degradation. In zukünftigen experimentellen Untersuchungen können die entwickelten Verfahren angewandt werden, um eine genauere Interpretation der Befunde zu ermöglichen. Insbesondere zeigt die Arbeit auf, wie verschiedene Hypothesen über den Degradationsmechanismus anhand eines geeigneten mathematischen Modells durch quantitative Experimente verifiziert oder falsifiziert werden können.

Nous nous proposons d'étudier la gestion du bruit stochastique d'expression génique. On s'intéresse plus particulièrement à la dynamique du bruit lors de la réponse cellulaire. Comment évolue le bruit? Quels sont les mécanismes en jeux? Quelle est l'importance du bruit dans le fonctionnement cellulaire? Pour répondre à ces questions, nous nous appuyons sur le réseau de régulation génétique qui gère la réponse au stress nutritionnel chez E. Coli. L'étude du comportement dynamique de ce réseau, au niveau d'une population de bactéries, a été initiée et est portée par la forte collaboration de deux équipes de la région : une de bio-informaticiens (l'équipe de Hidde de Jong de l'INRIA Rhône-Alpes) et la deuxième de biologistes (l'équipe de Hans Geiselmann, Laboratoire d'Adaptation et Pathogénie des Micro-organismes). En profitant donc de l'expérience et de la compréhension acquise par ces équipes, nous étudions les réponses individuelles de chaque bactérie lors de la transition entre état de stress nutritionnel, et état de croissance exponentielle. Le bruit d'expression génique est quantifié dans des nœuds clés du réseau de régulation. Pour ce faire, les bactéries sont suivies individuellement par microscopie de fluorescence sur plusieurs générations. Les données de fluorescence collectées sur cellules uniques permettent d'étudier la variabilité inter-cellulaire. Cette variabilité est quantifiée tout le long de la réponse: à chaque instant, on connaît la distribution des densités de fluorescence cellulaire dans la population de cellules. Et le suivi des lignées individuelles permet de travailler sur une population de cellules saines: les individus malades ou morts qui ne se divisent pas, sont écartés. En réduisant ainsi les phénomènes cellulaires en jeux, on réduit le nombre de paramètres. Les sources de bruit sont moins nombreuses, et il est plus facile de comprendre les mécanismes en jeux. Les informations de lignage cellulaire permettent aussi d'étudier la variabilité introduite par la phase du cycle cellulaire: les événements de division cellulaire peut être artificiellement synchronisés. Le bruit est alors étudié sur une population en phase lors de la division. Cette étude montre que le bruit sondé n'est pas dominé par les différences dans la phase du cycle cellulaire. On peut donc modéliser nos cellules sans tenir compte des différences introduites par le cycle cellulaire. Le modèle testé est simplifié aux étapes de transcription-traduction-maturation. Les paramètres du modèle sont inférés de nos données expérimentales, et le modèle est testé à travers des simulations. / We aim to investigate the management of the stochastic noise in gene expression and more precisely the study of noise in dynamical cellular responses. How the noise varies following a perturbation? What mechanisms are at play? How important is noise in the cellular function? To answer these questions, we are interested in the genetic regulatory network that handles the nutritional stress response in E. Coli. The noise of gene expression is quantified in a key node of the network control. For that bacteria are followed individually by fluorescence and phase contrast microscopy over several generations. This variability between cells is quantified throughout the response to the nutritional perturbation: at every moment, we know the density distribution of cellular fluorescence in the cell population. And monitoring of individual lines allows us to take into account only the population of healthy cells: individuals that do not divide neither grow, are discarded. Thereby reducing other sources of variability (e.g. cellular phenomena) we reduce the number of parameters. Noise sources are less numerous, and it is easier to understand the mechanisms at play. Also the information on cell lineage allow to study the variability introduced by the phase of the cell cycle: the events of cell division can be artificially synchronized. This study shows that the noise sounded is not dominated by differences in the phase of the cell cycle. We can therefore model our cells regardless of the differences introduced by the cell cycle. The tested model is simplified to the steps of transcription-translation-maturation. The model parameters are inferred from our experimental data and the model is tested through simulations.