Indiana University-Purdue University Indianapolis (IUPUI) / In response to different environmental stresses, phosphorylation of eIF2 (eIF2P) represses global translation coincident with preferential translation of ATF4. ATF4 is a transcriptional activator of the integrated stress response, a program of gene expression involved in metabolism, nutrient uptake, anti-oxidation, and the activation of additional transcription factors, such as CHOP/GADD153, that can induce apoptosis. Although eIF2P elicits translational control in response to many different stress arrangements, there are selected stresses, such as exposure to UV irradiation, that do not increase ATF4 expression despite robust eIF2P. In this study we addressed the underlying mechanism for variable expression of ATF4 in response to eIF2P during different stress conditions and the biological significance of omission of enhanced ATF4 function. We show that in addition to translational control, ATF4 expression is subject to transcriptional regulation. Stress conditions such as endoplasmic reticulum stress induce both transcription and translation of ATF4, which together enhance expression of ATF4 and its target genes in response to eIF2P. By contrast, UV irradiation represses ATF4 transcription, which diminishes ATF4 mRNA available for translation during eIF2∼P. eIF2P enhances cell survival in response to UV irradiation. However, forced expression of ATF4 and its target gene CHOP leads to increased sensitivity to UV irradiation. In this study, we also show that C/EBPβ is a transcriptional repressor of ATF4 during UV stress. C/EBPβ binds to critical elements in the ATF4 promoter resulting in its transcriptional repression. The LIP isoform of C/EBPβ, but not the LAP version is regulated following UV exposure and directly represses ATF4 transcription. Loss of the LIP isoform results in increased ATF4 mRNA levels in response to UV irradiation, and subsequent recovery of ATF4 translation, leading to enhanced expression of its target genes. Together these results illustrate how eIF2P and translational control, combined with transcription factors regulated by alternative signaling pathways, can direct programs of gene expression that are specifically tailored to each environmental stress.

Indiana University-Purdue University Indianapolis (IUPUI) / Photoreceptors are a class of sensory neuronal cells that are deleteriously affected in many disorders and injuries of the visual system. Significant injury or loss of these cells often results in a partial or complete loss of vision. While previous studies have determined many necessary components of the gene regulatory network governing the establishment, development, and maintenance of these cells, the necessary and sufficient profile and timecourse of gene expression and/or silencing has yet to be elucidated. Arduous protocols do exist to derive photoreceptors in vitro utilizing pluripotent stem cells, but only recently have been able to yield cells that are disease- and/or patient-specific. The discovery that mammalian somatic cells can be directly reprogrammed to another terminally-differentiated cell phenotype has inspired an explosion of research demonstrating the successful genetic reprogramming of one cell type to another, a process which is typically both more timely and efficient than those used to derive the same cells from pluripotent stem cell sources. Therefore, the emphasis of this study was to establish a novel system to be used to determine a minimal transcriptional network capable of directly reprogramming mouse embryonic fibroblasts (MEFs) to rod photoreceptors. The tools, assays, and experimental design chosen and established herein were designed and characterized to facilitate this determination, and preliminary data demonstrated the utility of this approach for accomplishing this aim.

Des techniques expérimentales récentes ont donné la possibilité d'acquérir un très grand nombre de données concernant des réseaux biologiques complexes, comme des réseaux de neurones, des réseaux de gènes et des réseaux d'interactions de protéines. Ces techniques sont capables d'enregistrer les états des composantes individuelles de ces réseaux (neurones, gènes, protéines) pour un grand nombre de configurations. Cependant, l'information la plus pertinente biologiquement se trouve dans la connectivité de ces systèmes et dans la façon précise avec laquelle ces composantes interagissent, information que les techniques expérimentales ne sont pas au point d'observer directement. Le bût de cette thèse est d'étudier les méthodes statistiques nécessaires pour inférer de l'information sur la connectivité des réseaux complexes en partant des données expérimentales. Ce sujet est traité par le point de vue de la physique statistique, en puisant de l'arsenal de méthodes théoriques qui ont été développées pour l'étude des verres de spins. Les verres de spins sont des exemples de réseaux à variables discrètes qui interagissent de façon complexe et sont souvent utilisés pour modéliser des réseaux biologiques. Après une introduction sur les modèles utilisés ainsi qu'une discussion sur la motivation biologique de cette thèse, toutes les méthodes d'inférence de réseaux connues sont présentées et analysées du point de vue de leur performance. Par la suite, dans la troisième partie de la thèse, un nouvelle méthode est proposée qui s'appuie sur la remarque que les interactions en biologie ne sont pas nécessairement symétriques (c'est-à-dire l'interaction entre les noeuds A et B n'est pas la même dans les deux directions). Il est démontré que cette assomption conduit à des méthodes qui sont capables de prédire les interactions de façon exacte, étant donné un nombre suffisant de données, tout en utilisant un temps de calcul polynomial. Ceci est un résultat original important car toutes les autres méthodes connues sont soit exactes et non-polynomiales soit inexactes et polynomiales. / Recent experimental techniques in biology made possible the acquisition of overwhelming amounts of data concerning complex biological networks, such as neural networks, gene regulation networks and protein-protein interaction networks. These techniques are able to record states of individual components of such networks (neurons, genes, proteins) for a large number of configurations. However, the most biologically relevantinformation lies in their connectivity and in the way their components interact, information that these techniques aren't able to record directly. The aim of this thesis is to study statistical methods for inferring information about the connectivity of complex networks starting from experimental data. The subject is approached from a statistical physics point of view drawing from the arsenal of methods developed in the study of spin glasses. Spin-glasses are prototypes of networks of discrete variables interacting in a complex way and are widely used to model biological networks. After an introduction of the models used and a discussion on the biological motivation of the thesis, all known methods of network inference are introduced and analysed from the point of view of their performance. Then, in the third part of the thesis, a new method is proposed which relies in the remark that the interactions in biology are not necessarily symmetric (i.e. the interaction from node A to node B is not the same as the one from B to A). It is shown that this assumption leads to methods that are both exact and efficient. This means that the interactions can be computed exactly, given a sufficient amount of data, and in a reasonable amount of time. This is an important original contribution since no other method is known to be both exact and efficient.

An der Interpretation extrazellulärer Signale beteiligte Regulationsnetzwerke sind von zentraler Bedeutung für alle Organismen. Extrazelluläre Signale werden gewöhnlich durch enzymatische Kaskaden innerhalb weniger Minuten in den Zellkern weitergeleitet, wo sie langsame Änderungen der Genexpression bewirken und so das Schicksal der Zelle beeinflussen. Im ersten Teil der Arbeit wird durch mathematische Modellierung untersucht, wie die MAPK Kaskade Signale von der Zellmembran in den Kern weiterleitet. Es wurden Netzwerkeigenschaften herausgearbeitet, die verhindern, dass die MAPK Kaskade fälschlicherweise durch genetische Mutationen aktiviert wird. Desweiteren wurde eine versteckte positive Rückkopplungsschleife identifiziert, welche die Aktivierung der MAPK Kaskade oberhalb eines gewissen Schwellwert-Stimulus verstärkt. Der zweite Teil der Arbeit konzentriert sich darauf, wie Änderungen der Genexpression auf langsamer Zeitskala in das Signalnetzwerk rückkoppeln. Eine systematische Genexpressionsdaten-Analyse ergab, dass transkriptionelle Rückkopplung in Eukaryoten generell über Induktion kurzlebiger Signalinhibitoren geschieht. Dynamische Modellierung und experimentelle Validierung von Modellvorhersagen ergab, dass das Inhibitorprotein SnoN als zentraler negativer Feedback Regulator im TGFbeta Signalweg fungiert. Der dritte Teil der Arbeit untersucht, wie die Genexpressionsmaschinerie intrazelluläre Signale interpretiert (“dekodiert“). Eine experimentelle und theoretische Analyse der cyanobakteriellen Eisenstress-Antwort ergab, dass IsrR, eine kleine regulatorische RNA, die Genexpression auf ausreichend starke und lange Stimulation beschränkt. Des Weiteren wurde ein “Reverse Engineering“-Algorithmus auf Hochdurchsatz-RNAi-Daten angewendet, um die Topologie eines krebsrelevanten Transkriptionsfaktornetzwerks abzuleiten. Zusammenfassend wurde in dieser Dissertation gezeigt, wie mathematische Modellierung die experimentelle Analyse biologischer Systeme unterstützen kann. / Intracellular regulatory networks involved in sensing extracellular cues are crucial to all living organisms. Extracellular signals are rapidly transmitted from the cell membrane to the nucleus by activation of enzymatic cascades which ultimately elicit slow changes in gene expression, and thereby affect the cell fate. In the first part of this thesis, the Ras-MAPK cascade transducing signals from the cell membrane to the nucleus is analyzed using mathematical modeling. Model analysis reveals network properties which prevent the MAPK cascade from being inappropriately activated by mutations. Moreover, the simulations unveil a hidden positive feedback loop which ensures strong amplification of MAPK signalling once extracellular stimulation exceeds a certain threshold. The second part of the thesis focuses on how slow gene expression responses feed back into the upstream signalling network. A systematic analysis of gene expression data gathered in mammalian cells demonstrates that such transcriptional feedback generally involves induction of highly unstable signalling inhibitors, thereby establishing negative feedback regulation. Dynamic data-based modelling identifies the SnoN oncoprotein as the central negative feedback regulator in the TGFbeta signalling pathway, and corresponding model predictions are verified experimentally in SnoN-depleted cells. The third part of the thesis focuses on how intracellular signals are decoded by the downstream gene expression machinery. A combined experimental and theoretical analysis of the cyanobacterial iron stress response reveals that small non-coding RNAs allow cells to selectively respond to sufficiently strong and sustained stimuli. Finally, a reverse engineering approach is applied to derive the topology of a complex mammalian transcription factor network from high-throughput knock-down data. In conclusion, this thesis demonstrates how mathematical modelling can support experimental analysis of biological systems.

La thèse est consacrée au développement de nouvelles méthodes de modélisations mathématiques en biologie et en médecine, du type “off-lattice" modèles hybrides discret-continus, et de leurs applications à l’hématopoïèse et aux maladies sanguines telles la leucémie et l’anémie. Dans cette approche, les cellules biologiques sont considérées comme des objets discrets alors que les réseaux intracellulaire et extracellulaire sont décrits avec des modèles continus régis par des équations aux dérivées partielles et des équations différentielles ordinaires. Les cellules interagissent mécaniquement et biochimiquement entre elles et avec le milieu environnant. Elles peuvent se diviser, mourir par apoptose ou se différencier. Le comportement des cellules est déterminé par le réseau de régulation intracellulaire et influencé par le contrôle local des cellules voisines ou par la régulation globale d’autres organes. Dans la première partie de la thèse, les modèles hybrides du type “off-lattice" dynamiques sont introduits. Des exemples de modèles, spécifiques aux processus biologiques, qui décrivent au sein de chaque cellule la concurrence entre la prolifération et l’apoptose, la prolifération et la différenciation et entre le cycle cellulaire et de l’état de repos sont étudiés. L’émergence des structures biologiques est étudiée avec les modèles hybrides. L’application à la modélisation des filamente de bactéries est illustrée. Dans le chapitre suivant, les modèle hybrides sont appliqués afin de modéliser l’érythropoïèse ou production de globules rouges dans la moelle osseuse. Le modèle inclut des cellules sanguines immatures appelées progéniteurs érythroïdes, qui peuvent s’auto-renouveler, se différencier ou mourir par apoptose, des cellules plus matures appelées les réticulocytes, qui influent les progéniteurs érythroïdes par le facteur de croissance Fas-ligand, et des macrophages, qui sont présents dans les îlots érythroblastiques in vivo. Les régulations intracellulaire et extracellulaire par les protéines et les facteurs de croissance sont précisées et les rétrocontrôles par les hormones érythropoïétine et glucocorticoïdes sont pris en compte. Le rôle des macrophages pour stabiliser les îlots érythroblastiques est montré. La comparaison des résultats de modélisation avec les expériences sur l’anémie chez les souris est effectuée. Le quatrième chapitre est consacré à la modélisation et au traitement de la leucémie. L’érythroleucémie, un sous-type de leucémie myéloblastique aigüe (LAM), se développe à cause de la différenciation insuffisante des progéniteurs érythroïdes et de leur auto-renouvellement excessif. Un modèle de type “Physiologically Based Pharmacokinetics-Pharmacodynamic” du traitement de la leucémie par AraC et un modèle de traitement chronothérapeutique de la leucémie sont examinés. La comparaison avec les données cliniques sur le nombre de blast dans le sang est effectuée. Le dernier chapitre traite du passage d’un modèle hybride à un modèle continu dans le cas 1D. Un théorème de convergence est prouvé. Les simulations numériques confirment un bon accord entre ces deux approches. / This dissertation is devoted to the development of new methods of mathematical modeling in biology and medicine, off-lattice discrete-continuous hybrid models, and their applications to modelling of hematopoiesis and blood disorders, such as leukemia and anemia. In this approach, biological cells are considered as discrete objects while intracellular and extracellular networks are described with continuous models, ordinary or partial differential equations. Cells interact mechanically and biochemically between each other and with the surrounding medium. They can divide, die by apoptosis or differentiate. Their fate is determined by intracellular regulation and influenced by local control from the surrounding cells or by global regulation from other organs. In the first part of the thesis, hybrid models with off-lattice cell dynamics are introduced. Model examples specific for biological processes and describing competition between cell proliferation and apoptosis, proliferation and differentiation and between cell cycling and quiescent state are investigated. Biological pattern formation with hybrid models is discussed. Application to bacteria filament is illustrated. In the next chapter, hybrid model are applied in order to model erythropoiesis, red blood cell production in the bone marrow. The model includes immature blood cells, erythroid progenitors, which can self-renew, differentiate or die by apoptosis, more mature cells, reticulocytes, which influence erythroid progenitors by means of growth factor Fas-ligand, and macrophages, which are present in erythroblastic islands in vivo. Intracellular and extracellular regulation by proteins and growth factors are specified and the feedback by the hormones erythropoietin and glucocorticoids is taken into account. The role of macrophages to stabilize erythroblastic islands is shown. Comparison of modelling with experiments on anemia in mice is carried out. The following chapter is devoted to leukemia modelling and treatment. Erythroleukemia, a subtype of Acute Myeloblastic Leukemia (AML), develops due to insufficient differentiation of erythroid progenitors and their excessive slef-renewal. A Physiologically Based Pharmacokinetics-Pharmacodynamics (PBPKPD) model of leukemia treatment with AraC drug and chronotherapeutic treatments of leukemia are examined. Comparison with clinical data on blast count in blood is carried out. The last chapter deals with the passage from a hybrid model to a continuous model in the 1D case. A convergence theorem is proved. Numerical simulations confirm a good agreement between these approaches.

In spite of remarkable progress in molecular biology, our understanding of the dynamics and functions of intra- and inter-cellular biological networks has been hampered by their complexity. Kinetics modelling, an important type of mathematical modelling, provides a rigorous and reliable way to reveal the complexity of biological networks. In this thesis, two genetic regulatory networks have been investigated via kinetic models. In the first part of the study, a model is developed to represent the transcriptional regulatory network essential for the circadian rhythms in Drosophila. The model incorporates the transcriptional feedback loops revealed so far in the network of the circadian clock (PER/TIM and VRI/PDP1 loops). Conventional Hill functions are not used to describe the regulation of genes, instead the explicit reactions of binding and unbinding processes of transcription factors to promoters are modelled. The model is described by a set of ordinary differential equations and the parameters are estimated from the in vitro experimental data of the clocks' components. The simulation results show that the model reproduces sustained circadian oscillations in mRNA and protein concentrations that are in agreement with experimental observations. It also simulates the entrainment by light-dark cycles, the disappearance of the rhythmicity in constant light and the shape of phase response curves resembling that of experimental results. The model is robust over a wide range of parameter variations. In addition, the simulated E-box mutation, perS and perL mutants are similar to that observed in the experiments. The deficiency between the simulated mRNA levels and experimental observations in per01, tim01 and clkJrk mutants suggests some differences in the model from reality. Finally, a possible function of VRI/PDP1 loops is proposed to increase the robustness of the clock. In the second part of the study, the sources of intrinsic noise and the influence of extrinsic noise are investigated on an intracellular viral infection system. The contribution of the intrinsic noise from each reaction is measured by means of a special form of stochastic differential equation, the chemical Langevin equation. The intrinsic noise of the system is the linear sum of the noise in each of the reactions. The intrinsic noise arises mainly from the degradation of mRNA and the transcription processes. Then, the effects of extrinsic noise are studied by means of a general form of stochastic differential equation. It is found that the noise of the viral components grows logarithmically with increasing noise intensities. The system is most susceptible to noise in the virus assembly process. A high level of noise in this process can even inhibit the replication of the viruses. In summary, the success of this thesis demonstrates the usefulness of models for interpreting experimental data, developing hypotheses, as well as for understanding the design principles of genetic regulatory networks.

The area of systems biology evolved in an attempt to introduce mathematical systems theory principles in biology. Although we believe that all biological processes are essentially chemical reactions, describing those using precise mathematical rules is not easy, primarily due to the complexity and enormity of biological systems. Here we introduce a formal approach for modeling biological dynamical relationships and diseases such as cancer. The immediate motivation behind this research is the urgency to find a practicable cure of cancer, the emperor of all maladies. Unlike other deadly endemic diseases such as plague, dengue and AIDS, cancer is characteristically heterogenic and hence requires a closer look into the genesis of the disease. The actual cause of cancer lies within our physiology. The process of cell division holds the clue to unravel the mysteries surrounding this disease. In normal scenario, all control mechanisms work in tandem and cell divides only when the division is required, for instance, to heal a wound platelet derived growth factor triggers cell division. The control mechanism is tightly regulated by several biochemical interactions commonly known as signal transduction pathways. However, from mathematical point of view, these pathways are marginal in nature and unable to cope with the multi-variability of a heterogenic disease like cancer. The present research is possibly one first attempt towards unraveling the mysteries surrounding the dynamics of a proliferating cell. A novel yet simple methodology is developed to bring all the marginal knowledge of the signaling pathways together to form the simplest mathematical abstract known as the Boolean Network. The malfunctioning in the cell by genetic mutations is formally modeled as stuck-at faults in the underlying Network. Finally a mathematical methodology is discovered to optimally find out the possible best combination drug therapy which can drive the cell from an undesirable condition of proliferation to a desirable condition of quiescence or apoptosis. Although, the complete biological validation was beyond the scope of the current research, the process of in-vitro validation has been already initiated by our collaborators. Once validated, this research will lead to a bright future in the field on personalized cancer therapy.

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X-Chromosom-Inaktivierung (XCI) ist der Mechanismus, den Säuger zur Dosiskompensierung zwischen weiblichen und männlichen Zellen verwenden. XCI wird ausgelöst durch die monoallelische Hochregulation der langen nicht-kodierenden RNA Xist von einem der zwei X-Chromosomen in weiblichen Zellen. Die Xist RNA vermittelt dann das Ausschalten der Gene auf diesem X-Chromosom. Das wirft einige interessante Fragen auf: Wie zählen Zellen ihre X-Chromosomen und stellen sicher, dass genau eines aktiv bleibt? Wie entscheiden sie, welches X-Chromosom aktiv bleibt und welches ausgeschaltet wird? Und wie erinnern sie sich an diese Entscheidung und behalten sie stabil bei durch alle weiteren Zellteilungen? Mithilfe eines stochastischen Modells zeigen wir, dass diese XCI Regulation prinzipiell durch nur zwei Regulatoren erklärt werden kann: Ein global (in trans) agierender XCI Aktivator und ein lokal (in cis) agierender XCI Repressor. Dieses Netzwerk aus nur zwei Regulatoren kann die Xist Expressionsmuster in verschiedenen Säugerspezies reproduzieren, von der Maus bis zum Mensch. Es sagt außerdem voraus, dass Zellen in der Lage sind, biallelische zu monoallelischer Xist Expression zu korrigieren, eine Vorhersage, für die wir tatsächlich experimentelle Belege finden. Mit einem mechanistischen Modell zeigen wir, dass das cis-Gedächtnis über den Xist Expressionszustand durch Antisense-Transkription zustande kommen könnte. Auf dieser Hypothese aufbauend untersucht der zweite Teil der Arbeit das Potential von Antisense-Transkription, ein lokales Gedächtnis über den Expressionszustand eines Gens zu generieren, genauer. Diese Analyse sagt vorher, dass Antisense-Repression den Expressionszustand eines Lokus tatsächlich für einige Tage stabil erhalten kann. / X-chromosome inactivation (XCI) is the mechanism for dosage compensation between the sexes in mammals. It is initiated through monoallelic upregulation of the long non-coding RNA Xist from one X chromosome, which mediates almost complete transcriptional silencing of this X chromosome. XCI regulation raises intriguing and thus far unanswered questions: How do cells count their X chromosomes and ensure that exactly one stays active? How do they make a mutually exclusive choice for one inactive X chromosome, and how do they then stably maintain this choice throughout subsequent cell divisions? Using stochastic modeling, we show that XCI onset only requires two regulators: A trans-acting Xist activator that ensures female specificity and a cis-acting Xist repressor that allows stable maintenance of alternative Xist expression states. This two-regulator network can recapitulate Xist expression patterns across different species and makes a novel prediction that is validated experimentally: Cells are able to revert biallelic Xist expression to monoallelic expression. With a mechanistic stochastic model we show that Xist's antisense transcript Tsix might be the cis-acting Xist repressor, uncovering the molecular mechanism behind the stabilization of the alternative Xist expression states. Building upon Tsix' possible functional role in stabilizing alternative Xist expression states on the active and inactive X chromosome, the second part of this thesis investigates the potential of antisense transcription to maintain a transient transcriptional memory. We find that mutual repression between a pair of antisense genes can allow the locus to remember the transcription state it has acquired due to a past signal for several days.