Mapping Drug Physico-Chemical Features to Pathway Activity Reveals Molecular Networks Linked to Toxicity Outcome

The identification of predictive biomarkers is at the core of modern toxicology. So far, a number of approaches have been proposed. These rely on statistical inference of toxicity response from either compound features (i.e., QSAR), in vitro cell based assays or molecular profiling of target tissues (i.e., expression profiling). Although these approaches have already shown the potential of predictive toxicology, we still do not have a systematic approach to model the interaction between chemical features, molecular networks and toxicity outcome. Here, we describe a computational strategy designed to address this important need. Its application to a model of renal tubular degeneration has revealed a link between physico-chemical features and signalling components controlling cell communication pathways, which in turn are differentially modulated in response to toxic chemicals. Overall, our findings are consistent with the existence of a general toxicity mechanism operating in synergy with more specific single-target based mode of actions (MOAs) and provide a general framework for the development of an integrative approach to predictive toxicology

Knowledge management for systems biology a general and visually driven framework applied to translational medicine

<p>Abstract</p> <p>Background</p> <p>To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory.</p> <p>Results</p> <p>To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data.</p> <p>Conclusions</p> <p>We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.</p

Understanding skeletal muscle adaptation in health and chronic disease: a multi-omics based systems biology perspective

Mammalian skeletal muscle has a major impact on whole-body metabolic homeostasis. Hence, maintenance of a metabolically active muscle mass is key for optimal health. Notably, both muscle function and mass are profoundly negatively affected by environmental factors such as chronic smoking and physical inactivity. RNA abundance integrates genetic, epigenetic and environmental influences. Therefore, while true understanding of physiological adaptation likely require the integration between multi-level datasets, the transcriptome represents a powerful investigative tool in determining the underlying molecular mechanisms behind complex phenotypic traits. The overarching aim of this thesis was to evaluate, using omics-based systems biology approaches, the global regulation of RNAs during exogenous modulation of mammalian muscle phenotype in order to characterize local homeostatic processes as well as identify robust biomarker signatures. The first part of this thesis deals with smoke-induced peripheral muscle wasting. Initially, biological domain knowledge is used to validate a pre-clinical smoking model. Then, specific cytokines are statistically linked to limb muscle energy metabolism; a testable hypothesis supported by both animal and human data. The second part deals with the development of ‘molecular predictors’ of endurance training adaptability. Two complex clinically relevant traits are considered, namely whole-body insulin sensitivity and plasma triglyceride content. Promisingly, quantitative multi-gene predictors of response to training for both traits of interest were developed