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 modeling. In this undertaking, the network provides a suitable framework for modeling the interactions between molecules. A biological network comprises 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 modeled by the definition of a dynamical system. Among the different network categories, the Boolean network offers a reliable qualitative framework for modeling the biological systems. Automatically synthesizing a Boolean network from experimental data, therefore, remains a necessary but challenging issue. This study, presents 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 regarding 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 helps biologists synthesize a reliable model faster and assist in evaluating and optimizing the biological networks' dynamic behavior, further modeling and predictions.

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.

ANDy, Activity Networks with Delays, is a discrete framework aiming at the qualitative modeling of time-dependent activities. The modular and expressive syntax makes ANDy suitable for a concise and natural modeling of time-dependent biological systems (i.e., regulatory pathways). Activities involve entities playing the role of activators, inhibitors or products of biochemical network operation. Activities may have a given duration, i.e., the time required to obtain results. An entity may represent an object (e.g., an agent, a biochemical species or a family of thereof) with a local attribute, a state denoting its level (e.g., concentration, strength). Entity levels may change as a result of an activity or may decay gradually as time passes by. The semantics of ANDy is formally given via high-level Petri nets ensuring this way some modularity. As main results we show that ANDy systems have finite state representations even for potentially infinite processes and it well adapts to the modeling of toxic behaviors. As an illustration, we present a classification of toxicity properties and give some hints on how they can be verified on ANDy systems with existing tools. A case study on blood glucose regulation is provided to exemplify the ANDy framework and the toxicity properties.

Mismatches between on-chip high performance CPU and data access times is the basic reason for the increasing gap between peak and sustained performance in distributed memory parallel computers. We propose the concept of balanced architectures, based on a network with a dynamic topology and communication patterns determined at compile time. The corresponding processing element is a cacheless CPU, which can achieve a 1 FLOP/clock cycle rate. Network and PE features are presented. An example shows that balanced architectures keep efficiency when scaling.