HedN Game, a Relational Framework for Network Based Cooperation

This paper proposes a new framework for cooperative games based on mathematical relations. Here cooperation is defined as a supportive partnerships represented by a directed network between players (aka hedonic relation). We examine in a specific context, modeled by abstract games how a change of supports induces a modification of strategic interactions between players. Two levels of description are considered: the first one describes the support network formation whereas the second one models the strategic interactions between players. Both are described in a unified formalism, namely CP~game. Stability conditions are stated, emphasizing the connection between these two levels. We also stress the interaction between updates of supports and their impact on the evolution of the context.Cooperative Game, Network, Stability, Hedonic Relation

Chromatic Community Structure Detection

The detection of community structure is probably one of the hottest trends in complex network research as it reveals the internal organization of people, molecules or processes behind social, biological or computer networks\dots The issue is to provide a network partition representative of this organization so that each community presumably gathers nodes sharing a common mission, purpose or property. Usually the identification is based on the difference between the connectivity density of the interior and the boundary of a community. Indeed, nodes sharing a common purpose or property are expected to interact closely. Although this rule appears mostly relevant, some fundamental scientific problems like disease module detection highlight the inability to determine significantly the communities under this connectivity rule. The main reason is that the connectivity density is not correlated to a shared property or purpose. Therefore, another paradigm is required for properly formalize this issue in order to meaningfully detect these communities. In this article we study the community formation from this new principle. Considering colors formally figures the shared properties, the issue is thus to maximize group of nodes with the same color within communities.. We study this novel community framework by introducing new measurement called \emph{chromarity} assessing the quality of the community structure regarding this constraint. Next we propose an algorithm solving the community structure detection based on this new community formation paradigm

Modular organisation of interaction networks based on asymptotic dynamics

This paper investigates questions related to the modularity in discrete models of biological interaction networks. We develop a theoretical framework based on the analysis of their asymptotic dynamics. More precisely, we exhibit formal conditions under which agents of interaction networks can be grouped into modules. As a main result, we show that the usual decomposition in strongly connected components fulfils the conditions of being a modular organisation. Furthermore, we point out that our framework enables a finer analysis providing a decomposition in elementary modules

TaBooN -- Boolean Network Synthesis Based on Tabu Search

Recent developments in Omics-technologies revolutionized the investigation of biology by producing molecular data in multiple dimensions and scale. This breakthrough in biology raises the crucial issue of their interpretation based on modelling. In this undertaking, network provides a suitable framework for modelling the interactions between molecules. Basically a Biological network is composed of nodes referring to the components such as genes or proteins, and the edges/arcs formalizing interactions between them. The evolution of the interactions is then modelled by the definition of a dynamical system. Among the different categories of network, the Boolean network offers a reliable qualitative framework for the modelling. Automatically synthesizing a Boolean network from experimental data therefore remains a necessary but challenging issue. In this study, we present taboon, an original work-flow for synthesizing Boolean Networks from biological data. The methodology uses the data in the form of Boolean profiles for inferring all the potential local formula inference. They combine to form the model space from which the most truthful model with regards to biological knowledge and experiments must be found. In the taboon work-flow the selection of the fittest model is achieved by a Tabu-search algorithm. taboon is an automated method for Boolean Network inference from experimental data that can also assist to evaluate and optimize the dynamic behaviour of the biological networks providing a reliable platform for further modelling and predictions

GUBS, a Behavior-based Language for Open System Dedicated to Synthetic Biology

In this article, we propose a domain specific language, GUBS (Genomic Unified Behavior Specification), dedicated to the behavioral specification of synthetic biological devices, viewed as discrete open dynamical systems. GUBS is a rule-based declarative language. By contrast to a closed system, a program is always a partial description of the behavior of the system. The semantics of the language accounts the existence of some hidden non-specified actions possibly altering the behavior of the programmed device. The compilation framework follows a scheme similar to automatic theorem proving, aiming at improving synthetic biological design safety.Comment: In Proceedings MeCBIC 2012, arXiv:1211.347

When a collective outcome triggers a rare individual event: a mode of metastatic process in a cell population

A model of early metastatic process is based on the role of the protein PAI-1, which at high enough extracellular concentration promotes the transition of cancer cells to a state prone to migration. This transition is described at the single cell level as a bi-stable switch associated with a subcritical bifurcation. In a multilevel reaction-diffusion scenario, the microenvironment of the tumor is modified by the proliferating cell population so as to push the concentration of PAI-1 above the bifurcation threshold. The formulation in terms of partial differential equations fails to capture spatio-temporal heterogeneity. Cellular-automata and agent-based simulations of cell populations support the hypothesis that a randomly localized accumulation of PAI-1 can arise and trigger the escape of a few isolated cells. Far away from the primary tumor, these cells experience a reverse transition back to a proliferative state and could generate a secondary tumor. The proposed role of PAI-1 in controlling this metastatic cycle is candidate to explain its role in the progression of cancer

Biomodélisation / Biological modelling Modéliser les interactions moléculaires par la théorie des réseaux de jeux

Abstract Modelling molecular interactions with game networks' theory. We present a method to model biological systems, the theory of games networks. It extends game theory by multiplying the number of games, and by allowing agents to play several games simultaneously. Some important notions of biological systems, such as locality of interactions and modularity, can then be modelled. Abridged English version Introduction The study of molecular networks by 'high-throughput' techniques is based on coupling in-silico modelling and experimental validation. This interdisciplinary ap-* Auteur correspondant. Adresse e-mail : [email protected] (F. Delaplace). proach leads us to revisit the model-experimentation cycle by integrating computing for the formulation of assumptions. The interaction, resulting from the need to treat automatically a large amount of biological data, gives rise to new problems focused on the conception of computational frameworks for biological modelling. Hence, the question is to correctly interpret data by these formal models, in accordance with methods, biological observations, and possible therapeutic applications. The interpretation may lead to a better under-1631-0691/$ -see front matte