Comparaison de réseaux biologiques

The comparison of biological networks is now one of the most promising approaches that help in understanding the functioning of living organisms. It appears as the expected continuation of the comparison of biological sequences, whose study represents in reality only the genomic aspect of the information manipulated by biologists. In this thesis, we propose an innovative approach allowing to compare two biological networks modeled respectively by a directed graph D and an undirected graph G, and provided with a correspondence function f between the vertices of both graphs. The approach consists in extracting automatically a biologically significant structure in D whose vertices induce in G a biologically significant structure as well. We realize an algorithmic study of the problem arising in our approach by starting with its variant in which D is acyclic (DAG). We provide polynomial algorithms for several cases and we show that other cases are algorithmically difficult (NP-completes). In order to solve the difficult instances, we propose a reliable heuristic and an exact algorithm based on the branch-and-bound method. To deal with the case where D is cyclic, we introduce a method motivated by biological hypotheses and consisting in decomposing D into DAGs such that the vertices of each DAG induce in G a connected subgraph. We also study in this thesis, the problem of signaling pathways inference by combining the information on causes and effects of extra-cellular events. We model this problem by a problem of mixed graphs orientation and we perform a complexity study allowing to identify the easy and the difficult instances.La comparaison de réseaux biologiques est actuellement l'une des approches les plus prometteuses pour aider à la compréhension du fonctionnement des organismes vivants. Elle apparaît comme la suite attendue de la comparaison de séquences biologiques dont l'étude ne représente en réalité que l'aspect génomique des informations manipulées par les biologistes. Dans cette thèse, nous proposons une approche innovante permettant de comparer deux réseaux biologiques modélisés respectivement par un graphe orienté D et un graphe non-orienté G, et dotés d'une fonction f établissant la correspondance entre les sommets des deux graphes. L'approche consiste à extraire automatiquement une structure dans D, biologiquement significative, dont les sommets induisent dans G, par f, une structure qui soit aussi biologiquement significative. Nous réalisons une étude algorithmique du problème issu de notre approche en commençant par sa version dans laquelle D est acyclique (DAG). Nous proposons des algorithmes polynomiaux pour certains cas, et nous montrons que d'autres cas sont algorithmiquement difficiles (NP-complets). Pour résoudre les instances difficiles, nous proposons une bonne heuristique et un algorithme exact basé sur la méthode branch-and-bound. Pour traiter le cas où D est cyclique, nous introduisons une méthode motivée par des hypothèses biologiques et consistant à décomposer D en DAGs tels que les sommets de chaque DAG induisent dans G un sous-graphe connexe. Nous étudions également dans cette thèse, l'inférence des voies de signalisation en combinant les informations sur les causes et sur les effets des événements extra-cellulaires. Nous modélisons ce problème par un problème d'orientation de graphes mixtes et nous effectuons une étude de complexité permettant d'identifier les instances faciles et celles difficiles

Algorithms for subnetwork mining in heterogeneous networks

International audienceSubnetwork mining is an essential issue in network analysis, with specific applications e.g. in biological networks, social networks, information networks and communication networks. Recent applications require the extraction of subnetworks (or patterns) involving several relations between the objects of interest, each such relation being given as a network. The complexity of a particular mining problem increases with the different nature of the networks, their number, their size, the topology of the requested pattern, the criteria to optimize. In this emerging field, our paper deals with two networks respectively represented as a directed acyclic graph and an undirected graph, on the same vertex set. The sought pattern is a longest path in the directed graph whose vertex set induces a connected subgraph in the undirected graph. This problem has immediate applications in biological networks, and predictable applications in social, information and communication networks. We study the complexity of the problem, thus identifying polynomial, NP-complete and APX-hard cases. In order to solve the difficult cases, we propose a heuristic and a branch-and-bound algorithm. We further perform experimental evaluation on both simulated and real data

Comparaison de réseaux biologiques

The comparison of biological networks is now one of the most promising approaches that help in understanding the functioning of living organisms. It appears as the expected continuation of the comparison of biological sequences, whose study represents in reality only the genomic aspect of the information manipulated by biologists. In this thesis, we propose an innovative approach allowing to compare two biological networks modeled respectively by a directed graph D and an undirected graph G, and provided with a correspondence function f between the vertices of both graphs. The approach consists in extracting automatically a biologically significant structure in D whose vertices induce in G a biologically significant structure as well. We realize an algorithmic study of the problem arising in our approach by starting with its variant in which D is acyclic (DAG). We provide polynomial algorithms for several cases and we show that other cases are algorithmically difficult (NP-completes). In order to solve the difficult instances, we propose a reliable heuristic and an exact algorithm based on the branch-and-bound method. To deal with the case where D is cyclic, we introduce a method motivated by biological hypotheses and consisting in decomposing D into DAGs such that the vertices of each DAG induce in G a connected subgraph. We also study in this thesis, the problem of signaling pathways inference by combining the information on causes and effects of extra-cellular events. We model this problem by a problem of mixed graphs orientation and we perform a complexity study allowing to identify the easy and the difficult instances.La comparaison de réseaux biologiques est actuellement l'une des approches les plus prometteuses pour aider à la compréhension du fonctionnement des organismes vivants. Elle apparaît comme la suite attendue de la comparaison de séquences biologiques dont l'étude ne représente en réalité que l'aspect génomique des informations manipulées par les biologistes. Dans cette thèse, nous proposons une approche innovante permettant de comparer deux réseaux biologiques modélisés respectivement par un graphe orienté D et un graphe non-orienté G, et dotés d'une fonction f établissant la correspondance entre les sommets des deux graphes. L'approche consiste à extraire automatiquement une structure dans D, biologiquement significative, dont les sommets induisent dans G, par f, une structure qui soit aussi biologiquement significative. Nous réalisons une étude algorithmique du problème issu de notre approche en commençant par sa version dans laquelle D est acyclique (DAG). Nous proposons des algorithmes polynomiaux pour certains cas, et nous montrons que d'autres cas sont algorithmiquement difficiles (NP-complets). Pour résoudre les instances difficiles, nous proposons une bonne heuristique et un algorithme exact basé sur la méthode branch-and-bound. Pour traiter le cas où D est cyclique, nous introduisons une méthode motivée par des hypothèses biologiques et consistant à décomposer D en DAGs tels que les sommets de chaque DAG induisent dans G un sous-graphe connexe. Nous étudions également dans cette thèse, l'inférence des voies de signalisation en combinant les informations sur les causes et sur les effets des événements extra-cellulaires. Nous modélisons ce problème par un problème d'orientation de graphes mixtes et nous effectuons une étude de complexité permettant d'identifier les instances faciles et celles difficiles

On the Complexity of two Problems on Orientations of Mixed Graphs

Abstract Interactions between biomolecules within the cell can be modeled by biological networks, i.e. graphs whose vertices are the biomolecules (proteins, genes, metabolites etc.) and whose edges represent their functional relationships. Depending on their nature, the interactions can be undirected (e.g. protein-protein interactions, PPIs) or directed (e.g. protein-DNA interactions, PDIs). A physical network is a network formed by both PPIs and PDIs, and is thus modeled by a mixed graph. External cellular events are transmitted into the nucleus via cascades of activation/deactivation of proteins, that correspond to paths (called signaling pathways) in the physical network from a source protein (cause) to a target protein (effect). There exists experimental methods to identify the cause-effect pairs, but such methods do not provide the signaling pathways. A key challenge is to infer such pathways based on the cause-effect informations. In terms of graph theory, this problem, called MAXIMUM GRAPH ORIENTATION (MGO), is defined as follows: given a mixed graph G and a set P of source-target pairs, find an orientation of G that replaces each (undirected) edge by a single (directed) arc in such a way that there exists a directed path, from s to t, for a maximum number of pairs (s, t) ∈ P. In this work, we consider a variant of MGO, called S-GO, in which we ask whether all the pairs in P can be connected by

Comparaison de réseaux biologiques

La comparaison de réseaux biologiques est actuellement l une des approches les plus prometteuses pour aider à la compréhension du fonctionnement des organismes vivants. Elle apparaît comme la suite attendue de la comparaison de séquences biologiques dont l étude ne représente en réalité que l aspect génomique des informations manipulées par les biologistes. Dans cette thèse, nous proposons une approche innovante permettant de comparer deux réseaux biologiques modélisés respectivement par un graphe orienté D et un graphe non-orienté G, et dotés d une fonction f établissant la correspondance entre les sommets des deux graphes. L approche consiste à extraire automatiquement une structure dans D, biologiquement significative, dont les sommets induisent dans G, par f , une structure qui soit aussi biologiquement significative. Nous réalisons une étude algorithmique du problème issu de notre approche en commençant par sa version dans laquelle D est acyclique (DAG). Nous proposons des algorithmes polynomiaux pour certains cas, et nous montrons que d autres cas sont algorithmiquement difficiles (NP-complets). Pour résoudre les instances difficiles, nous proposons une bonne heuristique et un algorithme exact basé sur la méthode branch-and-bound. Pour traiter le cas où D est cyclique, nous introduisons une méthode motivée par des hypothèses biologiques et consistant à décomposer D en DAGs tels que les sommets de chaque DAG induisent dans G un sous-graphe connexe. Nous étudions également dans cette thèse, l inférence des voies de signalisation en combinant les informations sur les causes et sur les effets des événements extra-cellulaires. Nous modélisons ce problème par un problème d orientation de graphes mixtes et nous effectuons une étude de complexité permettant d identifier les instances faciles et celles difficiles.The comparison of biological networks is now one of the most promising approaches that help in understanding the functioning of living organisms. It appears as the expected continuation of the comparison of biological sequences, whose study represents in reality only the genomic aspect of the information manipulated by biologists. In this thesis, we propose an innovative approach allowing to compare two biological networks modeled respectively by a directed graph D and an undirected graph G, and provided with a correspondence function f between the vertices of both graphs. The approach consists in extracting automatically a biologically significant structure in D whose vertices induce in G a biologically significant structure as well. We realize an algorithmic study of the problem arising in our approach by starting with its variant in which D is acyclic (DAG).We provide polynomial algorithms for several cases and we show that other cases are algorithmically difficult (NP-completes). In order to solve the difficult instances, we propose a reliable heuristic and an exact algorithm based on the branch-and-bound method. To deal with the case where D is cyclic, we introduce a method motivated by biological hypotheses and consisting in decomposing D into DAGs such that the vertices of each DAG induce in G a connected subgraph. We also study in this thesis, the problem of signaling pathways inference by combining the information on causes and effects of extra-cellular events. We model this problem by a problem of mixed graphs orientation and we perform a complexity study allowing to identify the easy and the difficult instances.NANTES-BU Sciences (441092104) / SudocSudocFranceF

Finding Supported Paths in Heterogeneous Networks

Subnetwork mining is an essential issue in the analysis of biological, social and communication networks. Recent applications require the simultaneous mining of several networks on the same or a similar vertex set. That is, one searches for subnetworks fulfilling different properties in each input network. We study the case that the input consists of a directed graph D and an undirected graph G on the same vertex set, and the sought pattern is a path P in D whose vertex set induces a connected subgraph of G. In this context, three concrete problems arise, depending on whether the existence of P is questioned or whether the length of P is to be optimized: in that case, one can search for a longest path or (maybe less intuitively) a shortest one. These problems have immediate applications in biological networks and predictable applications in social, information and communication networks. We study the classic and parameterized complexity of the problem, thus identifying polynomial and NP-complete cases, as well as fixed-parameter tractable and W[1]-hard cases. We also propose two enumeration algorithms that we evaluate on synthetic and biological data

Toward Scalable Blockchain for Data Management in VANETs

International audienceVehicular Ad-hoc NETworks (VANETs) are developing at a rapid pace due to the recent breakthroughs in digital fields such as sensor technology and the internet of things. The main technical challenges of current VANETs can be reduced to a lack of decentralization, data integrity, and privacy protection. Since the blockchain is decentralized and designed to be immutable, this paper proposes a novel architecture for secure data management in VANETs using blockchain. Our approach is based on a consortium blockchain, and adaptations have been made to deal with the VANETs context. We also exploit the notions of micro blocks and transactions to reduce the amount of exchanged messages. The performance of the proposed approach in terms of scalability, security, and resource consumption is assessed through simulations. Results have indicated that our approach succeeds in addressing the above VANETs challenges, and its performance is good enough to be used in real-world contexts

Ordonnancement multi-projets à contraintes de ressources partagées multi-compétences

International audienc

Méthodes exactes pour la détermination d'un plus long chemin DG-consistant dans des réseaux biologiques

National audienc

Improved approaches to solve the One-To-One SkewGraM problem

International audienc