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.

Bisimilar Booleanization of multivalued networks.

Discrete modelling frameworks of Biological networks can be divided in two distinct categories: Boolean and multivalued. Although multivalued networks are more expressive for qualifying the regulatory behaviours modelled by more than two values, the ability to automatically convert them to Boolean network with an equivalent behaviour breaks down the fundamental borders between the two approaches. Theoretically investigating the conversion process provides relevant insights into bridging the gap between them. Basically, the conversion aims at finding a Boolean network bisimulating a multivalued one. In this article, we investigate the bisimilar conversion where the Boolean integer coding is a parameter that can be freely modified. Based on this analysis, we define a computational method automatically inferring a bisimilar Boolean network from a given multivalued one.

Causal Reasoning on Boolean Control Networks Based on Abduction: Theory and Application to Cancer Drug Discovery.

Complex diseases such as Cancer or Alzheimer's are caused by multiple molecular perturbations leading to pathological cellular behavior. However, the identification of disease-induced molecular perturbations and subsequent development of efficient therapies are challenged by the complexity of the genotype-phenotype relationship. Accordingly, a key issue is to develop frameworks relating molecular perturbations and drug effects to their consequences on cellular phenotypes. Such framework would aim at identifying the sets of causal molecular factors leading to phenotypic reprogramming. In this article, we propose a theoretical framework, called Boolean Control Networks, where disease-induced molecular perturbations and drug actions are seen as topological perturbations/actions on molecular networks leading to cell phenotype reprogramming. We present a new method using abductive reasoning principles inferring the minimal causal topological actions leading to an expected behavior at stable state. Then, we compare different implementations of the algorithm and finally, show a proof-of-concept of the approach on a model of network regulating the proliferation/apoptosis switch in breast cancer by automatically discovering driver genes and their synthetic lethal drug target partner.

Networks and games for precision medicine.

Recent advances in omics technologies provide the leverage for the emergence of precision medicine that aims at personalizing therapy to patient. In this undertaking, computational methods play a central role for assisting physicians in their clinical decision-making by combining data analysis and systems biology modelling. Complex diseases such as cancer or diabetes arise from the intricate interplay of various biological molecules. Therefore, assessing drug efficiency requires to study the effects of elementary perturbations caused by diseases on relevant biological networks. In this paper, we propose a computational framework called Network-Action Game applied to best drug selection problem combining Game Theory and discrete models of dynamics (Boolean networks). Decision-making is modelled using Game Theory that defines the process of drug selection among alternative possibilities, while Boolean networks are used to model the effects of the interplay between disease and drugs actions on the patient's molecular system. The actions/strategies of disease and drugs are focused on arc alterations of the interactome. The efficiency of this framework has been evaluated for drug prediction on a model of breast cancer signalling.

Towards a behavioral-matching based compilation of synthetic biology functions.

The field of synthetic biology is looking forward engineering framework for safely designing reliable de-novo biological functions. In this undertaking, Computer-Aided-Design (CAD) environments should play a central role for facilitating the design. Although, CAD environment is widely used to engineer artificial systems the application in synthetic biology is still in its infancy. In this article we address the problem of the design of a high level language which at the core of CAD environment. More specifically the Gubs (Genomic Unified Behavioural Specification) language is a specification language used to describe the observations of the expected behaviour. The compiler appropriately selects components such that the observation of the synthetic biological function resulting to their assembly complies to the programmed behaviour.

Matrix-bound PAI-1 supports cell blebbing via RhoA/ROCK1 signaling.

The microenvironment of a tumor can influence both the morphology and the behavior of cancer cells which, in turn, can rapidly adapt to environmental changes. Increasing evidence points to the involvement of amoeboid cell migration and thus of cell blebbing in the metastatic process; however, the cues that promote amoeboid cell behavior in physiological and pathological conditions have not yet been clearly identified. Plasminogen Activator Inhibitor type-1 (PAI-1) is found in high amount in the microenvironment of aggressive tumors and is considered as an independent marker of bad prognosis. Here we show by immunoblotting, activity assay and immunofluorescence that, in SW620 human colorectal cancer cells, matrix-associated PAI-1 plays a role in the cell behavior needed for amoeboid migration by maintaining cell blebbing, localizing PDK1 and ROCK1 at the cell membrane and maintaining the RhoA/ROCK1/MLC-P pathway activation. The results obtained by modeling PAI-1 deposition around tumors indicate that matrix-bound PAI-1 is heterogeneously distributed at the tumor periphery and that, at certain spots, the elevated concentrations of matrix-bound PAI-1 needed for cancer cells to undergo the mesenchymal-amoeboid transition can be observed. Matrix-bound PAI-1, as a matricellular protein, could thus represent one of the physiopathological requirements to support metastatic formation.

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.

[The PAI-1 swing: microenvironment and cancer cell migration]. / Microenvironnement cellulaire, PAI-1 et migration cancéreuse.

Cancer is a complex and dynamic process caused by a cellular dysfunction leading to a whole organ or even organism vital perturbation. To better understand this process, we need to study each one of the levels involved, which allows the scale change, and to integrate this knowledge. A matricellular protein, PAI-1, is able to induce in vitro cell behaviour modifications, morphological changes, and to promote cell migration. PAI-1 influences the mesenchymo-amaeboid transition. This matricellular protein should be considered as a potential 'launcher' of the metastatic process acting at the molecular, cellular, tissular levels and, as a consequence, at the organism's level.

Exogean: a framework for annotating protein-coding genes in eukaryotic genomic DNA.

BACKGROUND: Accurate and automatic gene identification in eukaryotic genomic DNA is more than ever of crucial importance to efficiently exploit the large volume of assembled genome sequences available to the community. Automatic methods have always been considered less reliable than human expertise. This is illustrated in the EGASP project, where reference annotations against which all automatic methods are measured are generated by human annotators and experimentally verified. We hypothesized that replicating the accuracy of human annotators in an automatic method could be achieved by formalizing the rules and decisions that they use, in a mathematical formalism. RESULTS: We have developed Exogean, a flexible framework based on directed acyclic colored multigraphs (DACMs) that can represent biological objects (for example, mRNA, ESTs, protein alignments, exons) and relationships between them. Graphs are analyzed to process the information according to rules that replicate those used by human annotators. Simple individual starting objects given as input to Exogean are thus combined and synthesized into complex objects such as protein coding transcripts. CONCLUSION: We show here, in the context of the EGASP project, that Exogean is currently the method that best reproduces protein coding gene annotations from human experts, in terms of identifying at least one exact coding sequence per gene. We discuss current limitations of the method and several avenues for improvement.

Declarative simulation of dynamicals systems: the 812 programming language and its application to the simulation of genetic networks.

A major part of biological processes can be modeled as dynamical systems (DS), that is, as a time-varying state. In this article, we advocate a declarative approach for prototyping the simulation of DS. We introduce the concepts of collection, stream and fabric. A fabric is a multi-dimensional object that represents the successive values of a structured set of variables. A declarative programming language, called 8 1/2 has been developed to support the concept of fabrics. Several examples of working 8 1/2 programs are given to illustrate the relevance of the fabric data structure for simulation applications and to show how recursive fabric definitions can be easily used to model various biological phenomena in a natural way (a resolution of PDE, a simulation in artificial life, the Turing diffusion-reaction process and various examples of genetic networks). In the conclusion, we recapitulate several lessons we have learned from the 8 1/2 project.

Hyperstructures, genome analysis and I-cells.

New concepts may prove necessary to profit from the avalanche of sequence data on the genome, transcriptome, proteome and interactome and to relate this information to cell physiology. Here, we focus on the concept of large activity-based structures, or hyperstructures, in which a variety of types of molecules are brought together to perform a function. We review the evidence for the existence of hyperstructures responsible for the initiation of DNA replication, the sequestration of newly replicated origins of replication, cell division and for metabolism. The processes responsible for hyperstructure formation include changes in enzyme affinities due to metabolite-induction, lipid-protein affinities, elevated local concentrations of proteins and their binding sites on DNA and RNA, and transertion. Experimental techniques exist that can be used to study hyperstructures and we review some of the ones less familiar to biologists. Finally, we speculate on how a variety of in silico approaches involving cellular automata and multi-agent systems could be combined to develop new concepts in the form of an Integrated cell (I-cell) which would undergo selection for growth and survival in a world of artificial microbiology.