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Literatura académica sobre el tema "Control Boolean network"

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Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "Control Boolean network"

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CHEN, HONGWEI, YANG LIU y JIANQUAN LU. "SYNCHRONIZATION CRITERIA FOR TWO BOOLEAN NETWORKS BASED ON LOGICAL CONTROL". International Journal of Bifurcation and Chaos 23, n.º 11 (noviembre de 2013): 1350178. http://dx.doi.org/10.1142/s0218127413501782.

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This paper investigates the complete synchronization of two Boolean networks via logic control. Both feedback control and open-loop control are proposed to make the slave network completely synchronized with the master Boolean network. Using the algebraic state-space representation of Boolean networks, we derive several necessary and sufficient conditions for complete synchronization between two Boolean networks. Two examples are given to illustrate the obtained results.
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Li, Zhiqiang, Jinli Song y Huimin Xiao. "Reachability and Controllability Analysis of Periodic Switched Boolean Control Networks". Journal of Robotics and Mechatronics 26, n.º 5 (20 de octubre de 2014): 573–79. http://dx.doi.org/10.20965/jrm.2014.p0573.

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The reachability and controllability of switched Boolean (control) network are discussed in this paper. Based on semi-tensor product, using the vector form of Boolean logical variable, the switched Boolean (control) network is expressed as a discrete time system with state and control variables. For the switched Boolean network without control, the stabilization by suitable switching signal is discussed. Also, the controllability of the periodic switching signal is learned, and the conditions for stability and controllability of periodic switched Boolean networks avoiding states setCare obtained.
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3

Gao, Bo, Haipeng Peng, Dawei Zhao, Wenguang Zhang y Yixian Yang. "Attractor Transformation by Impulsive Control in Boolean Control Network". Mathematical Problems in Engineering 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/674571.

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Boolean control networks have recently been attracting considerable interests as computational models for genetic regulatory networks. In this paper, we present an approach of impulsive control for attractor transitions in Boolean control networks based on the recent developed matrix semitensor product theory. The reachability of attractors is estimated, and the controller is also obtained. The general derivation proposed here is exemplified with a kind of gene model, which is the protein-nucleic acid interactions network, on numerical simulations.
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4

Shi, Wenping, Bo Wu y Jing Han. "A Note on the Observability of Temporal Boolean Control Network". Abstract and Applied Analysis 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/631639.

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Temporal Boolean network is a generalization of the Boolean network model that takes into account the time series nature of the data and tries to incorporate into the model the possible existence of delayed regulatory interactions among genes. This paper investigates the observability problem of temporal Boolean control networks. Using the semi tensor product of matrices, the temporal Boolean networks can be converted into discrete time linear dynamic systems with time delays. Then, necessary and sufficient conditions on the observability via two kinds of inputs are obtained. An example is given to illustrate the effectiveness of the obtained results.
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Zhao, Yin, Bijoy K. Ghosh y Daizhan Cheng. "Control of Large-Scale Boolean Networks via Network Aggregation". IEEE Transactions on Neural Networks and Learning Systems 27, n.º 7 (julio de 2016): 1527–36. http://dx.doi.org/10.1109/tnnls.2015.2442593.

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Wang, Cailu y Yuegang Tao. "Conversion between Logic and Algebraic Expressions of Boolean Control Networks". Applied Sciences 10, n.º 20 (15 de octubre de 2020): 7180. http://dx.doi.org/10.3390/app10207180.

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The conversion between logic and algebraic expressions of Boolean control networks plays a worthy role in the analysis and design of digital circuits. In this paper, for a single Boolean function, a direct conversion between the minterm canonical form and the structure matrix is provided. For a Boolean control network consisting of systems of Boolean functions, two algorithms are developed to achieve the mutual conversion between the logic and algebraic expressions. The presented algorithms decrease exponentially the complexity of the semi-tensor product based method. Some numerical examples are given to demonstrate the algorithms and to compare our method with the existing ones.
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Li, Fangfei, Xiwen Lu y Zhaoxu Yu. "Optimal control algorithms for switched Boolean network". Journal of the Franklin Institute 351, n.º 6 (junio de 2014): 3490–501. http://dx.doi.org/10.1016/j.jfranklin.2014.03.008.

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Wei, Qiang, Cheng-jun Xie, Xu-ri Kou y Wei Shen. "Delay Partial Synchronization of Mutual Delay Coupled Boolean Networks". Measurement and Control 53, n.º 5-6 (15 de abril de 2020): 870–75. http://dx.doi.org/10.1177/0020294019882967.

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This paper studies the delay partial synchronization for mutual delay-coupled Boolean networks. First, the mutual delay-coupled Boolean network model is presented. Second, some necessary and sufficient conditions are derived to ensure the delay partial synchronization of the mutual delay-coupled Boolean networks. The upper bound of synchronization time is obtained. Finally, an example is provided to illustrate the efficiency of the theoretical analysis.
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Chen, Cheng y Wei Zhu. "Synchronization Analysis of Boolean Network". Applied Mechanics and Materials 432 (septiembre de 2013): 528–32. http://dx.doi.org/10.4028/www.scientific.net/amm.432.528.

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Boolean network and its synchronization have been gradually used to the global behavior analysis of large gene regulatory network. Network synchronization depends mainly on the dynamics of each node and the topology of the network. In this paper, using the semi-tensor product of matrices, a necessary and sufficient condition based on transition matrix for Boolean network complete synchronization is presented. The synchronization of Boolean control network is also discussed. Two examples are given to illustrate the theoretical result.
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Sun, Xiaolei, Naiming Qi y Weiran Yao. "Boolean Networks-Based Auction Algorithm for Task Assignment of Multiple UAVs". Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/425356.

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This paper presents an application of Boolean networks-based auction algorithm (BNAA) for task assignment in unmanned aerial vehicles (UAVs) systems. Under reasonable assumptions, the assignment framework consists of mission control system, communication network, and ground control station. As the improved algorithm of consensus-based bundle algorithm (CBBA), the BNAA utilizes a cluster-based combinatorial auction policy to handle multiple tasks. Instead of empirical method based on look-up table about conditional variables, Boolean network is introduced into consensus routine of BNAA for solving the conflict of assignment across the fleet of UAVs. As a new mathematic theory, semitensor product provides the implementation and theoretical proof of Boolean networks. Numerical results demonstrate the effectiveness and efficiency of proposed BNAA method.
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Tesis sobre el tema "Control Boolean network"

1

Magnini, Matteo. "An information theory analysis of critical Boolean networks as control software for robots". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23062/.

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This work is an analysis of critical random Boolean networks used as control software for robots. The main goal is to find if there are relations between information theory measures on robot's sensors and actuators and the capability of the robot to achieve a particular task. Secondary goals are to verify if just the number of nodes of the networks is significant to obtain better populations of controllers for a given task and if a Boolean network can perform well in more than one single task. Results show that for certain tasks there is a strongly positively correlation between some information theory measures and the objective function of the task. Moreover Boolean networks with an higher number of nodes tend to perform better. These results can be useful in the automatic design process of control software for robots. Finally some Boolean networks from a random generated population exhibit phenotypic plasticity, which is the ability to manifest more phenotypes from the same genotype in different environments. In this scenario it is the capability of the same Boolean network (same functions and connections) to successfully achieve different tasks.
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2

Münzner, Ulrike Tatjana Elisabeth. "From birth to birth A cell cycle control network of S. cerevisiae". Doctoral thesis, Humboldt-Universität zu Berlin, 2017. http://dx.doi.org/10.18452/18566.

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Der Zellzyklus organisiert die Zellteilung, und kontrolliert die Replikation der DNA sowie die Weitergabe des Genoms an die nächste Zellgeneration. Er unterliegt einer strengen Kontrolle auf molekularer Ebene. Diese molekularen Kontrollmechanismen sind für das Überleben eines Organismus essentiell, da Fehler Krankheiten begüngstigen können. Vor allem Krebs ist assoziiert mit Abweichungen im Ablauf des Zellzyklus. Die Aufklärung solcher Kontrollmechanismen auf molekularer Ebene ermöglicht einerseits das Verständnis deren grundlegender Funktionsweise, andererseits können solche Erkenntnisse dazu beitragen, Methoden zu entwickeln um den Zellzyklus steuern zu können. Um die molekularen Abläufe des Zellzyklus in ihrer Gesamtheit besser zu verstehen, eignen sich computergestützte Analysen. Beim Zellzyklus handelt es sich um einen Signaltransduktionsweg. Die Eigenschaften dieser Prozesse stellen Rekonstruktion und Übersetzung in digital lesbare Formate vor besondere Herausforderungen in Bezug auf Skalierbarkeit, Simulierbarkeit und Parameterschätzung. Diese Studie präsentiert eine großskalige Netzwerkrekonstruktion des Zellzyklus des Modellorganismus Saccharomyces cerevisiae. Hierfür wurde die reaction-contingency Sprache benutzt, die sowohl eine mechanistisch detaillierte Rekonstruktion auf molekularer Ebene zulässt, als auch deren Übersetzung in ein bipartites Boolesches Modell. Für das Boolesche Modell mit 2506 Knoten konnte ein zyklischer Attraktor bestimmt werden, der das Verhalten einer sich teilenden Hefezelle darstellt. Das Boolesche Modell reproduziert zudem das erwartete phänotypische Verhalten bei Aktivierung von vier Zellzyklusinhibitoren, und in 32 von 37 getesteten Mutanten. Die Rekonstruktion des Zellzyklus der Hefe kann in Folgestudien genutzt werden, um Signaltransduktionswege zu integrieren, die mit dem Zellzyklus interferieren, deren Schnittstellen aufzuzeigen, und dem Ziel, die molekularen Mechanismen einer ganzen Zelle abzubilden, näher zu kommen. Diese Studie zeigt zudem, dass eine auf reaction- contingency Sprache basierte Rekonstruktion geeignet ist, um ein biologisches Netzwerk konsistent mit empirischer Daten darzustellen, und gleichzeitig durch Simulation die Funktionalität des Netzwerkes zu überprüfen.
The survival of a species depends on the correct transmission of an intact genome from one generation to the next. The cell cycle regulates this process and its correct execution is vital for survival of a species. The cell cycle underlies a strict control mechanism ensuring accurate cell cycle progression, as aberrations in cell cycle progression are often linked to serious defects and diseases such as cancer. Understanding this regulatory machinery of the cell cycle offers insights into how life functions on a molecular level and also provides for a better understanding of diseases and possible approaches to control them. Cell cycle control is furthermore a complex mechanism and studying it holistically provides for understanding its collective properties. Computational approaches facilitate holistic cell cycle control studies. However, the properties of the cell cycle control network challenge large-scale in silico studies with respect to scalability, model execution and parameter estimation. This thesis presents a mechanistically detailed and executable large-scale reconstruction of the Saccharomyces cerevisiae cell cycle control network based on reaction- contingency language. The reconstruction accounts for 229 proteins and consists of three individual cycles corresponding to the macroscopic events of DNA replication, spindle pole body duplication, and bud emergence and growth. The reconstruction translated into a bipartite Boolean model has, using an initial state determined with a priori knowledge, a cyclic attractor which reproduces the cyclic behavior of a wildtype yeast cell. The bipartite Boolean model has 2506 nodes and correctly responds to four cell cycle arrest chemicals. Furthermore, the bipartite Boolean model was used in a mutational study where 37 mutants were tested and 32 mutants found to reproduce known phenotypes. The reconstruction of the cell cycle control network of S. cerevisiae demonstrates the power of the reaction-contingency based approach, and paves the way for network extension with regard to the cell cycle machinery itself, and several signal transduction pathways interfering with the cell cycle.
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Goudarzi, Alireza. "On the Effect of Topology on Learning and Generalization in Random Automata Networks". PDXScholar, 2011. https://pdxscholar.library.pdx.edu/open_access_etds/193.

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We extend the study of learning and generalization in feed forward Boolean networks to random Boolean networks (RBNs). We explore the relationship between the learning capability and the network topology, the system size, the training sample size, and the complexity of the computational tasks. We show experimentally that there exists a critical connectivity Kc that improves the generalization and adaptation in networks. In addition, we show that in finite size networks, the critical K is a power-law function of the system size N and the fraction of inputs used during the training. We explain why adaptation improves at this critical connectivity by showing that the network ensemble manifests maximal topological diversity near Kc. Our work is partly motivated by self-assembled molecular and nanoscale electronics. Our findings allow to determine an automata network topology class for efficient and robust information processing.
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Pardo, Jérémie. "Méthodes d'inférence de cibles thérapeutiques et de séquences de traitement". Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG011.

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Un enjeu majeur de la médecine des réseaux est l’identification des perturbations moléculaires induites par les maladies complexes et les thérapies afin de réaliser une reprogrammation cellulaire. L’action de la reprogrammation est le résultat de l’application d’un contrôle. Dans cette thèse, nous étendons le contrôle unique des réseaux biologiques en étudiant le contrôle séquentiel des réseaux booléens. Nous présentons un nouveau cadre théorique pour l’étude formelle des séquences de contrôle. Nous considérons le contrôle par gel de noeuds. Ainsi, une variable du réseau booléen peut être fixée à la valeur 0, 1 ou décontrôlée. Nous définissons un modèle de dynamique contrôlée pour le mode de mise à jour synchrone où la modification de contrôle ne se produit que sur un état stable. Nous appelons CoFaSe le problème d’inférence consistant à trouver une séquence de contrôle modifiant la dynamique pour évoluer vers une propriété ou un état souhaité. Les réseaux auxquels sera appliqué CoFaSe auront toujours un ensemble de variables incontrôlables. Nous montrons que ce problème est PSPACE-dur. L’étude des caractéristiques dynamiques du problème CoFaSe nous a permis de constater que les propriétés dynamiques qui impliquent la nécessité d’une séquence de contrôle émergent des fonctions de mise à jour des variables incontrôlables. Nous trouvons que la longueur d’une séquence de contrôle minimale ne peut pas être supérieure à deux fois le nombre de profils des variables incontrôlables. À partir de ce résultat, nous avons construit deux algorithmes inférant des séquences de contrôle minimales sous la dynamique synchrone. Enfin, l’étude des interdépendances entre le contrôle séquentiel et la topologie du graphe d’interaction du réseau booléen nous a permis de découvrir des relations existantes entre structure et contrôle. Celles-ci mettent en évidence une borne maximale plus resserrée pour certaines topologies que celles obtenues par l’étude de la dynamique. L’étude sur la topologie met en lumière l’importance de la présence de cycles non-négatifs dans le graphe d’interaction pour l’émergence de séquences minimales de contrôle de taille supérieure ou égale à deux
Network controllability is a major challenge in network medicine. It consists in finding a way to rewire molecular networks to reprogram the cell fate. The reprogramming action is typically represented as the action of a control. In this thesis, we extended the single control action method by investigating the sequential control of Boolean networks. We present a theoretical framework for the formal study of control sequences.We consider freeze controls, under which the variables can only be frozen to 0, 1 or unfrozen. We define a model of controlled dynamics where the modification of the control only occurs at a stable state in the synchronous update mode. We refer to the inference problem of finding a control sequence modifying the dynamics to evolve towards a desired state or property as CoFaSe. Under this problem, a set of variables are uncontrollable. We prove that this problem is PSPACE-hard. We know from the complexity of CoFaSe that finding a minimal sequence of control by exhaustively exploring all possible control sequences is not practically tractable. By studying the dynamical properties of the CoFaSe problem, we found that the dynamical properties that imply the necessity of a sequence of control emerge from the update functions of uncontrollable variables. We found that the length of a minimal control sequence cannot be larger than twice the number of profiles of uncontrollable variables. From this result, we built two algorithms inferring minimal control sequences under synchronous dynamics. Finally, the study of the interdependencies between sequential control and the topology of the interaction graph of the Boolean network allowed us to investigate the causal relationships that exist between structure and control. Furthermore, accounting for the topological properties of the network gives additional tools for tightening the upper bounds on sequence length. This work sheds light on the key importance of non-negative cycles in the interaction graph for the emergence of minimal sequences of control of size greater than or equal to two
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Jiao, Yue y 焦月. "Mathematical models for control of probabilistic Boolean networks". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B41508634.

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Chen, Xi y 陈曦. "On construction and control of probabilistic Boolean networks". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48329605.

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Modeling gene regulation is an important problem in genomic research. The Boolean network (BN) and its generalization Probabilistic Boolean network (PBN) have been proposed to model genetic regulatory interactions. BN is a deterministic model while PBN is a stochastic model. In a PBN, on one hand, its stationary distribution gives important information about the long-run behavior of the network. On the other hand, one may be interested in system synthesis which requires the construction of networks from the observed stationary distribution. This results in an inverse problem of constructing PBNs from a given stationary distribution and a given set of Boolean Networks (BNs), which is ill-posed and challenging, because there may be many networks or no network having the given properties and the size of the inverse problem is huge. The inverse problem is first formulated as a constrained least squares problem. A heuristic method is then proposed based on the conjugate gradient (CG) algorithm, an iterative method, to solve the resulting least squares problem. An estimation method for the parameters of the PBNs is also discussed. Numerical examples are then given to demonstrate the effectiveness of the proposed methods. However, the PBNs generated by the above algorithm depends on the initial guess and is not unique. A heuristic method is then proposed for generating PBNs from a given transition probability matrix. Unique solution can be obtained in this case. Moreover, these algorithms are able to recover the dominated BNs and therefore the major structure of the network. To further evaluate the feasible solutions, a maximum entropy approach is proposed using entropy as a measure of the fitness. Newton’s method in conjunction with the CG method is then applied to solving the inverse problem. The convergence rate of the proposed method is demonstrated. Numerical examples are also given to demonstrate the effectiveness of our proposed method. Another important problem is to find the optimal control policy for a PBN so as to avoid the network from entering into undesirable states. By applying external control, the network is desired to enter into some state within a few time steps. For PBN CONTROL, people propose to find a control sequence such that the network will terminate in the desired state with a maximum probability. Also, the problem of minimizing the maximum cost is considered. Integer linear programming (ILP) and dynamic programming (DP) in conjunction with hard constraints are then employed to solve the above problems. Numerical experiments are given to demonstrate the effectiveness of our algorithms. A hardness result is demonstrated and suggests that PBN CONTROL is harder than BN CONTROL. In addition, deciding the steady state probability in PBN for a specified global state is demonstrated to be NP-hard. However, due to the high computational complexity of PBNs, DP method is computationally inefficient for a large size network. Inspired by the state reduction strategies studied in [86], the DP method in conjunction with state reduction approach is then proposed to reduce the computational cost of the DP method. Numerical examples are given to demonstrate both the effectiveness and the efficiency of our proposed method.
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Mathematics
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Jiao, Yue. "Mathematical models for control of probabilistic Boolean networks". Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B41508634.

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Choudhary, Ashish. "Intervention in gene regulatory networks". Texas A&M University, 2006. http://hdl.handle.net/1969.1/4284.

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In recent years Boolean Networks (BN) and Probabilistic Boolean Networks (PBN) have become popular paradigms for modeling gene regulation. A PBN is a collection of BNs in which the gene state vector transitions according to the rules of one of the constituent BNs, and the network choice is governed by a selection distribution. Intervention in the context of PBNs was first proposed with an objective of avoid- ing undesirable states, such as those associated with a disease. The early methods of intervention were ad hoc, using concepts like mean first passage time and alteration of rule based structure. Since then, the problem has been recognized and posed as one of optimal control of a Markov Network, where the objective is to find optimal strategies for manipulating external control variables to guide the network away from the set of undesirable states towards the set of desirable states. This development made it possible to use the elegant theory of Markov decision processes (MDP) to solve an array of problems in the area of control in gene regulatory networks, the main theme of this work. We first introduce the optimal control problem in the context of PBN models and review our solution using the dynamic programming approach. We next discuss a case in which the network state is not observable but for which measurements that are probabilistically related to the underlying state are available. We then address the issue of terminal penalty assignment, considering long term prospective behavior and the special attractor structure of these networks. We finally discuss our recent work on optimal intervention for the case of a family of BNs. Here we consider simultaneously controlling a set of Boolean Models that satisfy the constraints imposed by the underlying biology and the data. This situation arises in a case where the data is assumed to arise by sampling the steady state of the real biological network.
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Biane, Célia. "Reprogrammation comportementale : modèles, algorithmes et application aux maladies complexes". Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLE050.

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Les maladies complexes comme le Cancer et la maladie d'Alzheimer sont causées par des perturbations moléculaires multiples responsables d'un comportement cellulaire pathologique.Un enjeu majeur de la médecine de précision est l'identification des perturbations moléculaires induites par les maladies complexes et les thérapies à partir de leurs conséquences sur les phénotypes cellulaire.Nous définissons un modèle des maladies complexes,appelé la reprogrammation comportementale,assimilant les perturbations moléculaires à des altérations des fonctions dynamiques locales de systèmes dynamiques discrets induisant une reprogrammation de la dynamique globale du réseau. Ce cadre de modélisation s'appuie d'une part, sur les réseaux Booléens contrôlés, qui sont des réseaux Booléens dans lesquels sont insérés des paramètres de contrôle modélisant les perturbations et, d'autre part, sur la définition de modes (Possibilité, Nécessité) permettant d'exprimer les objectifs de cette reprogrammation.A partir de ce cadre, nous démontrons que le calcul des noyaux, i.e., des ensembles minimaux d'actions permettant la reprogrammation selon un mode s'exprime comme un problème d'inférence abductive en logique propositionnelle. En nous appuyant sur les méthodes historiques de calcul d'impliquants premiers des fonctions Booléennes,nous développons deux méthodes permettant le calcul exhaustif des noyaux de la reprogrammation. Enfin, nous évaluons la pertinence du cadre de modélisation pour l'identification des perturbations responsables de la transformation d'une cellule saine en cellule cancéreuse et la découverte de cibles thérapeutiques sur un modèle du cancer du sein. Nous montrons notamment que les perturbations inférées par nos méthodes sont compatibles avec la connaissance biologique en discriminant les oncogènes des gènes suppresseurs de tumeurs et en récupérant la mutation du gène BRCA1. De plus, la méthode récupère le phénomène de létalité synthétique entre PARP1 et BRCA1, qui constitue un traitement anticancéreux optimal car il cible spécifiquement les cellules tumorales
Complex diseases such as cancer and Alzheimer's are caused by multiple molecular perturbations responsible for pathological cellular behavior. A major challenge of precision medicine is the identification of the molecular perturbations induced by the disease and the therapies from their consequences on cell phenotypes. We define a model of complex diseases, called behavioral reprogramming, that assimilates the molecular perturbations to alterations of the dynamic local functions of discrete dynamical systems inducing a reprogramming of the global dynamics of the network. This modeling framework relies on the one hand, on Control Boolean networks, which are Boolean networks containing control parameters modeling the perturbations and, on the other hand, the definition of reprogramming modes (Possibility, Necessity) expressing the objective of the behavioral reprogramming. From this framework, we demonstrate that the computation of the cores, namely, the minimal sets of action allowing reprogramming is a problem of abductive inference in propositional logic. Using historical methods computing the prime implicants of Boolean functions, we develop two methods computing all the reprogramming cores.Finally, we evaluate the modeling framework for the identification of perturbations responsible for the transformation of a healthy cell into a cancercell and the discovery of therapeutic targets ona model of breast cancer. In particular, we showthat the perturbations inferred by our methods a recompatible with biological knowledge by discriminating oncogenes and tumor suppressor genes and by recovering the causal of the BRCA1 gene. In addition, the method recovers the synthetic lethality phenomenon between PARP1 and BRCA1 that constitutes an optimal anti-cancer treatment because it specifically targets tumor cells
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Ghaffari, Noushin. "Genomic Regulatory Networks, Reduction Mappings and Control". Thesis, 2012. http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10726.

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All high-level living organisms are made of small cell units, containing DNA, RNA, genes, proteins etc. Genes are important components of the cells and it is necessary to understand the inter-gene relations, in order to comprehend, predict and ultimately intervene in the cells’ dynamics. Genetic regulatory networks (GRN) represent the gene interactions that dictate the cell behavior. Translational genomics aims to mathematically model GRNs and one of the main goals is to alter the networks’ behavior away from undesirable phenotypes such as cancer. The mathematical framework that has been often used for modeling GRNs is the probabilistic Boolean network (PBN), which is a collection of constituent Boolean networks with perturbation, BNp. This dissertation uses BNps, to model gene regulatory networks with an intent of designing stationary control policies (CP) for the networks to shift their dynamics toward more desirable states. Markov Chains (MC) are used to represent the PBNs and stochastic control has been employed to find stationary control policies to affect steady-state distribution of the MC. However, as the number of genes increases, it becomes computationally burdensome, or even infeasible, to derive optimal or greedy intervention policies. This dissertation considers the problem of modeling and intervening in large GRNs. To overcome the computational challenges associated with large networks, two approaches are proposed: first, a reduction mapping that deletes genes from the network; and second, a greedy control policy that can be directly designed on large networks. Simulation results show that these methods achieve the goal of controlling large networks by shifting the steady-state distribution of the networks toward more desirable states. Furthermore, a new inference method is used to derive a large 17-gene Boolean network from microarray experiments on gastrointestinal cancer samples. The new algorithm has similarities to a previously developed well-known inference method, which uses seed genes to grow subnetworks, out of a large network; however, it has major differences with that algorithm. Most importantly, the objective of the new algorithm is to infer a network from a seed gene with an intention to derive the Gene Activity Profile toward more desirable phenotypes. The newly introduced reduction mappings approach is used to delete genes from the 17-gene GRN and when the network is small enough, an intervention policy is designed for the reduced network and induced back to the original network. In another experiment, the greedy control policy approach is used to directly design an intervention policy on the large 17-gene network to beneficially change the long-run behavior of the network. Finally, a novel algorithm is developed for selecting only non-isomorphic BNs, while generating synthetic networks, using a method that generates synthetic BNs, with a prescribed set of attractors. The goal of the new method described in this dissertation is to discard isomorphic networks.
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Libros sobre el tema "Control Boolean network"

1

Cheng, Daizhan, Hongsheng Qi y Zhiqiang Li. Analysis and Control of Boolean Networks. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-097-7.

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Shmulevich, Ilya. Probabilistic boolean networks: The modeling and control of gene regulatory networks. Philadelphia: Society for Industrial and Applied Mathematics, 2010.

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Shmulevich, Ilya. Probabilistic boolean networks: The modeling and control of gene regulatory networks. Philadelphia: Society for Industrial and Applied Mathematics, 2010.

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Shmulevich, Ilya. Probabilistic boolean networks: The modeling and control of gene regulatory networks. Philadelphia: Society for Industrial and Applied Mathematics, 2010.

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R, Dougherty Edward y Society for Industrial and Applied Mathematics., eds. Probabilistic boolean networks: The modeling and control of gene regulatory networks. Philadelphia: Society for Industrial and Applied Mathematics, 2010.

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Shmulevich, Ilya. Probabilistic boolean networks: The modeling and control of gene regulatory networks. Philadelphia: Society for Industrial and Applied Mathematics, 2010.

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Zhang, Zhihua. Observer Design for Control and Fault Diagnosis of Boolean Networks. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-35929-4.

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Cheng, Dai-Zhan. Analysis and control of boolean networks: A semi-tensor product approach. London: Springer, 2011.

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Li, Zhiqiang, Daizhan Cheng y Hongsheng Qi. Analysis and Control of Boolean Networks. Springer, 2011.

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Tatsuya, Akutsu. Algorithms for Analysis, Inference, and Control of Boolean Networks. World Scientific Publishing Co Pte Ltd, 2018.

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Capítulos de libros sobre el tema "Control Boolean network"

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Cheng, Daizhan, Hongsheng Qi y Zhiqiang Li. "Topological Structure of a Boolean Network". En Communications and Control Engineering, 103–40. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-097-7_5.

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Biane, Célia y Franck Delaplace. "Abduction Based Drug Target Discovery Using Boolean Control Network". En Computational Methods in Systems Biology, 57–73. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67471-1_4.

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Cheng, Daizhan, Hongsheng Qi y Zhiqiang Li. "Random Boolean Networks". En Communications and Control Engineering, 431–50. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-097-7_19.

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Darke, Priyanka, Bharti Chimdyalwar, Sakshi Agrawal, Shrawan Kumar, R. Venkatesh y Supratik Chakraborty. "VeriAbsL: Scalable Verification by Abstraction and Strategy Prediction (Competition Contribution)". En Tools and Algorithms for the Construction and Analysis of Systems, 588–93. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30820-8_41.

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AbstractWe present VeriAbsL, a reachability verifier that performs verification in three stages. First, it slices the input code using a combination of two slicers, then it verifies the slices using predicted strategies, and at last, it composes the result of verifying the individual slices. We introduce a novel shallow slicing technique that uses variable reference information of the program, and data and control dependencies of the entry function to generate slices. We also introduce a novel strategy prediction technique that uses machine learning to predict a strategy. It uses boolean features to describe a program to a neural network that predicts a strategy. We use the portfolio of VeriAbs, a reachabiltiy verifier with manually defined strategies. In sv-comp 2023, VeriAbsL verified 227 (Without witness validation.) more programs than VeriAbs, and 475 (Without witness validation.) programs that VeriAbs could not verify.
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Cheng, Daizhan, Hongsheng Qi y Zhiqiang Li. "Realization of Boolean Control Networks". En Communications and Control Engineering, 233–48. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-097-7_10.

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Cheng, Daizhan, Hongsheng Qi y Zhiqiang Li. "Identification of Boolean Control Networks". En Communications and Control Engineering, 389–407. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-097-7_17.

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Zhang, Kuize, Lijun Zhang y Lihua Xie. "Observability of Boolean Control Networks". En Discrete-Time and Discrete-Space Dynamical Systems, 87–104. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25972-3_4.

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Zhang, Kuize, Lijun Zhang y Lihua Xie. "Detectability of Boolean Control Networks". En Discrete-Time and Discrete-Space Dynamical Systems, 105–15. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25972-3_5.

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Taou, Nadia S., David W. Corne y Michael A. Lones. "Towards Intelligent Biological Control: Controlling Boolean Networks with Boolean Networks". En Applications of Evolutionary Computation, 351–62. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31204-0_23.

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Cheng, Daizhan, Hongsheng Qi y Zhiqiang Li. "Feedback Decomposition of Boolean Control Networks". En Communications and Control Engineering, 297–311. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-097-7_13.

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Actas de conferencias sobre el tema "Control Boolean network"

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Sutavani, S., K. Sarda, A. Yerudkar y N. Singh. "Interpretation of complex reaction networks in Boolean network framework". En 2018 Indian Control Conference (ICC). IEEE, 2018. http://dx.doi.org/10.1109/indiancc.2018.8307945.

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Li, Zhiqiang, Huimin Xiao y Jinli Song. "Uniformly partial stability of Boolean network". En 2014 33rd Chinese Control Conference (CCC). IEEE, 2014. http://dx.doi.org/10.1109/chicc.2014.6895597.

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He, Yun y Junmi Li. "Observability of a temporal Boolean network". En 2014 33rd Chinese Control Conference (CCC). IEEE, 2014. http://dx.doi.org/10.1109/chicc.2014.6895632.

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Li, Zhiqiang, Jinli Song y Jian Yang. "Partial stability of probabilistic Boolean network". En 2014 26th Chinese Control And Decision Conference (CCDC). IEEE, 2014. http://dx.doi.org/10.1109/ccdc.2014.6852489.

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Sridharan, S., R. Layek, A. Datta y J. Venkatraj. "Boolean network model of oxidative stress response pathways". En 2012 American Control Conference - ACC 2012. IEEE, 2012. http://dx.doi.org/10.1109/acc.2012.6315168.

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Lin, Lin y Jinde Cao. "Controllability of Switched Boolean Control Network via Sampled-Data Control". En 2019 Chinese Control Conference (CCC). IEEE, 2019. http://dx.doi.org/10.23919/chicc.2019.8865411.

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Sonam, K., S. Sutavani, S. R. Wagh, F. S. Kazi y N. M. Singh. "Optimal Control of Probabilistic Boolean Network using Embedding Framework". En 2021 American Control Conference (ACC). IEEE, 2021. http://dx.doi.org/10.23919/acc50511.2021.9483140.

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Fangfei, Li, Zhao Shouwei, Li Chunxiang y Yu Zhaoxu. "Partial stabilization for Boolean network with state feedback control". En 2015 34th Chinese Control Conference (CCC). IEEE, 2015. http://dx.doi.org/10.1109/chicc.2015.7259846.

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Chen, Xudong, Zuguang Gao y Tamer Basar. "Asymptotic behavior of a reduced conjunctive Boolean network". En 2017 IEEE 56th Annual Conference on Decision and Control (CDC). IEEE, 2017. http://dx.doi.org/10.1109/cdc.2017.8264308.

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Wu, Shizhen, Lulu Li, Jianquan Lu y Daniel W. C. Ho. "Partial Synchronization for Boolean Network Based on Pinning Control Strategy". En 2018 37th Chinese Control Conference (CCC). IEEE, 2018. http://dx.doi.org/10.23919/chicc.2018.8483659.

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