Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks

International audienceIncreasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system

Principles of Periodontology

Periodontal diseases are among the most common diseases affecting humans. Dental biofilm is a contributor to the etiology of most periodontal diseases. It is also widely accepted that immunological and inflammatory responses to biofilm components are manifested by signs and symptoms of periodontal disease. The outcome of such interaction is modulated by risk factors (modifiers), either inherent (genetic) or acquired (environmental), significantly affecting the initiation and progression of different periodontal disease phenotypes. While definitive genetic determinants responsible for either susceptibility or resistance to periodontal disease have yet to be identified, many factors affecting the pathogenesis have been described, including smoking, diabetes, obesity, medications, and nutrition. Currently, periodontal diseases are classified based upon clinical disease traits using radiographs and clinical examination. Advances in genomics, molecular biology, and personalized medicine may result in new guidelines for unambiguous disease definition and diagnosis in the future. Recent studies have implied relationships between periodontal diseases and systemic conditions. Answering critical questions regarding host‐parasite interactions in periodontal diseases may provide new insight in the pathogenesis of other biomedical disorders. Therapeutic efforts have focused on the microbial nature of the infection, as active treatment centers on biofilm disruption by non‐surgical mechanical debridement with antimicrobial and sometimes anti‐inflammatory adjuncts. The surgical treatment aims at gaining access to periodontal lesions and correcting unfavorable gingival/osseous contours to achieve a periodontal architecture that will provide for more effective oral hygiene and periodontal maintenance. In addition, advances in tissue engineering have provided innovative means to regenerate/repair periodontal defects, based upon principles of guided tissue regeneration and utilization of growth factors/biologic mediators. To maintain periodontal stability, these treatments need to be supplemented with long‐term maintenance (supportive periodontal therapy) programs

Reasoning on the response of logical signaling networks with Answer Set Programming

International audienceThis chapter focuses on modeling the response of logical signaling networks by means of automated reasoning using answer set programming (ASP). In this context, the problem consisting of learning logical networks is crucial in order to achieve unbiased and robust discoveries. Furthermore, it shows that many networks can be compatible with a given set of experimental observations. The chapter discusses how ASP can be used to exhaustively enumerate all these logical networks. Next, in order to gain control over the system, it look for intervention strategies that force a set of target species into a desired steady state. Altogether, this constitutes a pipeline for reasoning on logical signaling networks providing robust insights to system biologists. The chapter illustrates the usage of ASP for solving the aforementioned problems and discusses the novelty of our approach with respect to existing methods