Despite spectacular progress in biophysics, molecular biology and biochemistry our ability to predict the dynamic behavior of multicellular systems under different conditions is very limited. An important reason for this is that still not enough is known about how cells change their physical and biological properties by genetic or metabolic regulation, and which of these changes affect the cell behavior. For this reason, it is difficult to predict the system behavior of multicellular systems in case the cell behavior changes, for example, as a consequence of regulation or differentiation. The rules that underlie the regulation processes have been determined on the time scale of evolution, by selection on the phenotypic level of cells or cell populations. We illustrate by detailed computer simulations in a multi-scale approach how cell behavior controlled by regulatory networks may emerge as a consequence of an evolutionary process, if either the cells, or populations of cells are subject to selection on particular features. We consider two examples, migration strategies of single cells searching a signal source, or aggregation of two or more cells within minimal multiscale models of biological evolution. Both can be found for example in the life cycle of the slime mold Dictyostelium discoideum. However, phenotypic changes that can lead to completely different modes of migration have also been observed in cells of multi-cellular organisms, for example, as a consequence of a specialization in stem cells or the de-differentiation in tumor cells. The regulatory networks are represented by Boolean networks and encoded by binary strings. The latter may be considered as encoding the genetic information (the genotype) and are subject to mutations and crossovers. The cell behavior reflects the phenotype. We find that cells adopt naturally observed migration strategies, controlled by networks that show robustness and redundancy. The model simplicity allow us to unambiguously analyze the regulatory networks and the resulting phenotypes by different measures and by knockouts of regulatory elements. We illustrate that in order to maintain a cells' phenotype in case of a knockout, the cell may have to be able to deal with contradictory information. In summary, both the cell phenotype as well as the emerged regulatory network behave as their biological counterparts observed in nature.

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.

Cette thèse est consacrée à la modélisation qualitative des réseaux biologiques pour l'innovation thérapeutique. Elle étudie comment utiliser les réseaux Booléens, et comment les améliorer, afin d'identifier des cibles thérapeutiques au moyen d'approches in silico. Elle se compose de deux travaux : i) un algorithme exploitant les attracteurs des réseaux Booléens pour l'identification in silico de cibles dans des modèles Booléens de réseaux biologiques pathologiquement perturbés, et ii) une amélioration des réseaux Booléens dans leur capacité à modéliser la dynamique des réseaux biologiques grâce à l'utilisation des opérateurs de la logique floue et grâce au réglage des arrêtes. L'identification de cibles constitue l'une des étapes de la découverte de nouveaux médicaments et a pour but d'identifier des biomolécules dont la fonction devrait être thérapeutiquement modifiée afin de lutter contre la pathologie considérée. Le premier travail de cette thèse propose un algorithme pour l'identification in silico de cibles par l'exploitation des attracteurs des réseaux Booléens. Il suppose que les attracteurs des systèmes dynamiques, tel que les réseaux Booléens, correspondent aux phénotypes produits par le système biologique modélisé. Sous cette hypothèse, et étant donné un réseau Booléen modélisant une physiopathologie, l'algorithme identifie des combinaisons de cibles capables de supprimer les attracteurs associés aux phénotypes pathologiques. L'algorithme est testé sur un modèle Booléen du cycle cellulaire arborant une inactivation constitutive de la protéine du rétinoblastome, tel que constaté dans de nombreux cancers, tandis que ses applications sont illustrées sur un modèle Booléen de l'anémie de Fanconi. Les résultats montrent que l'algorithme est à même de retourner des combinaisons de cibles capables de supprimer les attracteurs associés aux phénotypes pathologiques, et donc qu'il réussit l'identification in silico de cibles proposée. En revanche, comme tout résultat in silico, il y a un pont à franchir entre théorie et pratique, requérant ainsi une utilisation conjointe d'approches expérimentales. Toutefois, il est escompté que l'algorithme présente un intérêt pour l'identification de cibles, notamment par l'exploitation du faible coût des approches computationnelles, ainsi que de leur pouvoir prédictif, afin d'optimiser l'efficience d'expérimentations coûteuses. La modélisation quantitative en biologie systémique peut s'avérer difficile en raison de la rareté des détails quantitatifs concernant les phénomènes biologiques, particulièrement à l'échelle subcellulaire, l'échelle où les médicaments interagissent avec leurs cibles. Une alternative permettant de contourner cette difficulté est la modélisation qualitative étant donné que celle-ci ne requiert que peu ou pas d'informations quantitatives. Parmi les méthodes de modélisation qualitative, les réseaux Booléens en sont l'une des plus populaires. Cependant, les modèles Booléens autorisent leurs variables à n'être évaluées qu'à vrai ou faux, ce qui peut apparaître trop simpliste lorsque des processus biologiques sont modélisés. En conséquence, le second travail de cette thèse propose une méthode de modélisation dérivée des réseaux Booléens où les opérateurs de la logique floue sont utilisés et où les arrêtes peuvent être réglées. Les opérateurs de la logique floue permettent aux variables d'être continues, et ainsi d'être plus finement évaluées qu'avec des méthodes de modélisation discrètes tel que les réseaux Booléens, tout en demeurant qualitatives. De plus, dans le but de considérer le fait que certaines interactions peuvent être plus lentes et/ou plus faibles que d'autres, l'état des arrêtes est calculé afin de moduler en vitesse et en force le signal qu'elles véhiculent. La méthode proposée est illustrée par son implémentation sur un petit échantillon de la signalisation du récepteur au facteur de croissance épidermique... [etc] / This thesis is devoted to the qualitative modeling of biological networks for therapeutic innovation. It investigates how to use the Boolean network formalism, and how to enhance it, for identifying therapeutic targets through in silico approaches. It is composed of two works: i) an algorithm using Boolean network attractors for in silico target identification in Boolean models of pathologically disturbed biological networks, and ii) an enhancement of the Boolean network formalism in modeling the dynamics of biological networks through the incorporation of fuzzy operators and edge tuning. Target identification, one of the steps of drug discovery, aims at identifying biomolecules whose function should be therapeutically altered in order to cure the considered pathology. The first work of this thesis proposes an algorithm for in silico target identification using Boolean network attractors. It assumes that attractors of dynamical systems, such as Boolean networks, correspond to phenotypes produced by the modeled biological system. Under this assumption, and given a Boolean network modeling a pathophysiology, the algorithm identifies target combinations able to remove attractors associated with pathological phenotypes. It is tested on a Boolean model of the mammalian cell cycle bearing a constitutive inactivation of the retinoblastoma protein, as seen in cancers, and its applications are illustrated on a Boolean model of Fanconi anemia. The results show that the algorithm returns target combinations able to remove attractors associated with pathological phenotypes and then succeeds in performing the proposed in silico target identification. However, as with any in silico evidence, there is a bridge to cross between theory and practice, thus requiring it to be used in combination with wet lab experiments. Nevertheless, it is expected that the algorithm is of interest for target identification, notably by exploiting the inexpensiveness and predictive power of computational approaches to optimize the efficiency of costly wet lab experiments. Quantitative modeling in systems biology can be difficult due to the scarcity of quantitative details about biological phenomenons, especially at the subcellular scale, the scale where drugs interact with there targets. An alternative to escape this difficulty is qualitative modeling since it requires few to no quantitative information. Among the qualitative modeling approaches, the Boolean network formalism is one of the most popular. However, Boolean models allow variables to be valued at only true or false, which can appear too simplistic when modeling biological processes. Consequently, the second work of this thesis proposes a modeling approach derived from Boolean networks where fuzzy operators are used and where edges are tuned. Fuzzy operators allow variables to be continuous and then to be more finely valued than with discrete modeling approaches, such as Boolean networks, while remaining qualitative. Moreover, to consider that some interactions are slower and/or weaker relative to other ones, edge states are computed in order to modulate in speed and strength the signal they convey. The proposed formalism is illustrated through its implementation on a tiny sample of the epidermal growth factor receptor signaling pathway. The obtained simulations show that continuous results are produced, thus allowing finer analysis, and that modulating the signal conveyed by the edges allows their tuning according to knowledge about the modeled interactions, thus incorporating more knowledge. The proposed modeling approach is expected to bring enhancements in the ability of qualitative models to simulate the dynamics of biological networks while not requiring quantitative information. The main prospect of this thesis is to use the proposed enhancement of Boolean networks to build a version of the algorithm based on continuous dynamical systems...[etc]

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