Reverse Engineering of Biological Systems

Gene regulatory network (GRN) consists of a set of genes and regulatory relationships between the genes. As outputs of the GRN, gene expression data contain important information that can be used to reconstruct the GRN to a certain degree. However, the reverse engineer of GRNs from gene expression data is a challenging problem in systems biology. Conventional methods fail in inferring GRNs from gene expression data because of the relative less number of observations compared with the large number of the genes. The inherent noises in the data make the inference accuracy relatively low and the combinatorial explosion nature of the problem makes the inference task extremely difficult. This study aims at reconstructing the GRNs from time-course gene expression data based on GRN models using system identification and parameter estimation methods. The main content consists of three parts: (1) a review of the methods for reverse engineering of GRNs, (2) reverse engineering of GRNs based on linear models and (3) reverse engineering of GRNs based on a nonlinear model, specifically S-systems. In the first part, after the necessary background and challenges of the problem are introduced, various methods for the inference of GRNs are comprehensively reviewed from two aspects: models and inference algorithms. The advantages and disadvantages of each method are discussed. The second part focus on inferring GRNs from time-course gene expression data based on linear models. First, the statistical properties of two sparse penalties, adaptive LASSO and SCAD, with an autoregressive model are studied. It shows that the proposed methods using these two penalties can asymptotically reconstruct the underlying networks. This provides a solid foundation for these methods and their extensions. Second, the integration of multiple datasets should be able to improve the accuracy of the GRN inference. A novel method, Huber group LASSO, is developed to infer GRNs from multiple time-course data, which is also robust to large noises and outliers that the data may contain. An efficient algorithm is also developed and its convergence analysis is provided. The third part can be further divided into two phases: estimating the parameters of S-systems with system structure known and inferring the S-systems without knowing the system structure. Two methods, alternating weighted least squares (AWLS) and auxiliary function guided coordinate descent (AFGCD), have been developed to estimate the parameters of S-systems from time-course data. AWLS takes advantage of the special structure of S-systems and significantly outperforms one existing method, alternating regression (AR). AFGCD uses the auxiliary function and coordinate descent techniques to get the smart and efficient iteration formula and its convergence is theoretically guaranteed. Without knowing the system structure, taking advantage of the special structure of the S-system model, a novel method, pruning separable parameter estimation algorithm (PSPEA) is developed to locally infer the S-systems. PSPEA is then combined with continuous genetic algorithm (CGA) to form a hybrid algorithm which can globally reconstruct the S-systems

Control and Data Analysis of Complex Networks

abstract: This dissertation treats a number of related problems in control and data analysis of complex networks. First, in existing linear controllability frameworks, the ability to steer a network from any initiate state toward any desired state is measured by the minimum number of driver nodes. However, the associated optimal control energy can become unbearably large, preventing actual control from being realized. Here I develop a physical controllability framework and propose strategies to turn physically uncontrollable networks into physically controllable ones. I also discover that although full control can be guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control energy to achieve actual control, and my work provides a framework to address this issue. Second, in spite of recent progresses in linear controllability, controlling nonlinear dynamical networks remains an outstanding problem. Here I develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another. I introduce the concept of attractor network and formulate a quantifiable framework: a network is more controllable if the attractor network is more strongly connected. I test the control framework using examples from various models and demonstrate the beneficial role of noise in facilitating control. Third, I analyze large data sets from a diverse online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors: linear, “S”-shape and exponential growths. Inspired by cell population growth model in microbial ecology, I construct a base growth model for meme popularity in OSNs. Then I incorporate human interest dynamics into the base model and propose a hybrid model which contains a small number of free parameters. The model successfully predicts the various distinct meme growth dynamics. At last, I propose a nonlinear dynamics model to characterize the controlling of WNT signaling pathway in the differentiation of neural progenitor cells. The model is able to predict experiment results and shed light on the understanding of WNT regulation mechanisms.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

A multi-objective method for optimizing the transittability of complex biomolecular networks

International audienceWith the development of high-throughput techniques, systems biology has been pushing researchers to focus on how to optimize the steering of biomolecular networks from their actual state to a desired state. This phenomenon known as the ”transittability” means that complex biomolecular networks can be steered from an unexpected state to a desired state.This paper investigates the optimization of the transittability of complex biomolecular networks taking into account different objective functions. To solve this problem, we propose a multi-objective optimization approach which consists of two steps, the search and decision making step. The search step is based on a powerful multi-objective genetic algorithm, the non-dominated sorting genetic algrorithm (NSGA-II), to solve our problem and obtain a Pareto-optimal set. As regards the decision making step is based on the use of a multi-criteria decision making method, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), for providing the best compromise solution according to the user preferences. The proposed approach was tested and applied to solve the steering of the p53 Signaling network. Experimental results illustrate the effectiveness of this approach

Approches sémantiques pour la méta-optimisation des réseaux biomoléculaires complexes

Les modèles de la biologie des systèmes visent à comprendre le comportement d’une cellule à travers un réseau biomoléculaire complexe. Dans a littérature, la plupart des études ne se sont intéressés qu’à la modélisation des parties isolées du réseau biomoléculaire com les réseaux métaboliques, etc. Cependant, pour bien comprendre le comportement d’une cellule, nous devons modéliser et analyser le réseau biomoléculaire dans son ensemble. Les approches existantes ne répondent pas suffisamment à ces exigences. Dans ce projet de recherche,nous proposons une plate-forme qui permet aux biologistes de simuler les changements d’état des réseaux biomoléculaires dans le but de piloter leurs comportements et de les faire évoluer d’un état non désiré vers un état souhaitable. Cette plate-forme utilise des règles, des connaissances et de l’expérience, un peu comme celles que pourrait en tirer un biologiste expert. La plate-forme comprend quatre modules : un module de modélisation logique, un module de modélisation sémantique, un module de simulation qualitative à événements discrets etun module d’optimisation. Dans ce but, nous présentons d’abord une approche logique pour la modélisation des réseaux biomoléculaires complexes, incluant leurs aspects structurels, fonctionnels et comportementaux. Ensuite, nous proposons une approche sémantique basée sur quatre ontologies pour fournir une description riche des réseaux biomoléculaires et de leurs changements d’état. Ensuite, nous présentons une méthode de simulation qualitative à événements discrets pour simuler le comportement du réseau biomoléculaire dans le temps. Enfin, nous proposons une méthode d’optimisation multi-objectifs pour optimiser la transitabilité des réseaux biomoléculaires complexes dans laquelle nous prenons en compte différents critères tels que la minimisation du nombre de stimuli externes, la minimisation du coût de ces stimuli, la minimisation du nombre de noeuds cibles et la minimisation de l’inconfort du patient. En se fondant sur ces quatre contributions, un prototype appelé CBN-Simulateur a été développé. Nous décrivons nos approches et montrons leurs applications sur des études de cas réels, le bactériophage T4 gene 32, le phage lambda et le réseau de signalisation p53. Les résultats montrent que ces approches fournissent les éléments nécessaires pour modéliser, raisonner et analyser le comportement dynamique et les états de transition des réseaux biomoléculaires complexes.Systems biology models aim to understand the behaviour of a cell trough a complex biomolecular network. In the literature, most research focuses on modelling isolated parts of this network, such as metabolic networks.However, to fully understand the cell’s behaviour we should analyze the biomolecular network as a whole. Avail-able approaches do not address these requirements sufficiently. In this context, we aim at developing a platform that enables biologists to simulate the state changes of biomolecular networks with the goal of steering their be-haviours. The platform employs rules, knowledge and experience, much like those that an expert biologist mightderive. This platform consists of four modules: a logic-based modelling module, a semantic modelling module,a qualitative discrete-event simulation module and an optimization module. For this purpose, we first present alogic-based approach for modelling complex biomolecular networks including the structural, functional and be-havioural aspects. Next, we propose a semantic approach based on four ontologies to provide a rich description of biomolecular networks and their state changes. Then, we present a method of qualitative discrete-event simulation to simulate the biomolecular network behaviour over time. Finally, we propose a multi-objective optimization method for optimizing the transittability of complex biomolecular networks in which we take into account various criteria such as minimizing the number of external stimuli, minimizing the cost of these stimuli, minimizing the number of target nodes and minimizing patient discomfort. Based on these four contributions, a prototype called the CBNSimulator was developed. We describe our approaches and show their applicability through real cases studies, the bacteriophage T4 gene 32, the phage lambda, and the p53 signaling network. Results demonstrate that these approaches provide the necessary elements to model, reason and analyse the dynamic behaviour and the transition states of complex biomolecular networks

Approches sémantiques pour la méta-optimisation des réseaux biomoléculaires complexes

Systems biology models aim to understand the behaviour of a cell trough a complex biomolecular network. In the literature, most research focuses on modelling isolated parts of this network, such as metabolic networks.However, to fully understand the cell’s behaviour we should analyze the biomolecular network as a whole. Avail-able approaches do not address these requirements sufficiently. In this context, we aim at developing a platform that enables biologists to simulate the state changes of biomolecular networks with the goal of steering their be-haviours. The platform employs rules, knowledge and experience, much like those that an expert biologist mightderive. This platform consists of four modules: a logic-based modelling module, a semantic modelling module,a qualitative discrete-event simulation module and an optimization module. For this purpose, we first present alogic-based approach for modelling complex biomolecular networks including the structural, functional and be-havioural aspects. Next, we propose a semantic approach based on four ontologies to provide a rich description of biomolecular networks and their state changes. Then, we present a method of qualitative discrete-event simulation to simulate the biomolecular network behaviour over time. Finally, we propose a multi-objective optimization method for optimizing the transittability of complex biomolecular networks in which we take into account various criteria such as minimizing the number of external stimuli, minimizing the cost of these stimuli, minimizing the number of target nodes and minimizing patient discomfort. Based on these four contributions, a prototype called the CBNSimulator was developed. We describe our approaches and show their applicability through real cases studies, the bacteriophage T4 gene 32, the phage lambda, and the p53 signaling network. Results demonstrate that these approaches provide the necessary elements to model, reason and analyse the dynamic behaviour and the transition states of complex biomolecular networks.Les modèles de la biologie des systèmes visent à comprendre le comportement d’une cellule à travers un réseau biomoléculaire complexe. Dans a littérature, la plupart des études ne se sont intéressés qu’à la modélisation des parties isolées du réseau biomoléculaire com les réseaux métaboliques, etc. Cependant, pour bien comprendre le comportement d’une cellule, nous devons modéliser et analyser le réseau biomoléculaire dans son ensemble. Les approches existantes ne répondent pas suffisamment à ces exigences. Dans ce projet de recherche,nous proposons une plate-forme qui permet aux biologistes de simuler les changements d’état des réseaux biomoléculaires dans le but de piloter leurs comportements et de les faire évoluer d’un état non désiré vers un état souhaitable. Cette plate-forme utilise des règles, des connaissances et de l’expérience, un peu comme celles que pourrait en tirer un biologiste expert. La plate-forme comprend quatre modules : un module de modélisation logique, un module de modélisation sémantique, un module de simulation qualitative à événements discrets etun module d’optimisation. Dans ce but, nous présentons d’abord une approche logique pour la modélisation des réseaux biomoléculaires complexes, incluant leurs aspects structurels, fonctionnels et comportementaux. Ensuite, nous proposons une approche sémantique basée sur quatre ontologies pour fournir une description riche des réseaux biomoléculaires et de leurs changements d’état. Ensuite, nous présentons une méthode de simulation qualitative à événements discrets pour simuler le comportement du réseau biomoléculaire dans le temps. Enfin, nous proposons une méthode d’optimisation multi-objectifs pour optimiser la transitabilité des réseaux biomoléculaires complexes dans laquelle nous prenons en compte différents critères tels que la minimisation du nombre de stimuli externes, la minimisation du coût de ces stimuli, la minimisation du nombre de noeuds cibles et la minimisation de l’inconfort du patient. En se fondant sur ces quatre contributions, un prototype appelé CBN-Simulateur a été développé. Nous décrivons nos approches et montrons leurs applications sur des études de cas réels, le bactériophage T4 gene 32, le phage lambda et le réseau de signalisation p53. Les résultats montrent que ces approches fournissent les éléments nécessaires pour modéliser, raisonner et analyser le comportement dynamique et les états de transition des réseaux biomoléculaires complexes