Section: Application Domains
Biomedical Knowledge Discovery
Participants : Marie-Dominique Devignes [contact person] , Malika Smaïl-Tabbone [contact person] , Sabeur Aridhi, David Ritchie, Gabin Personeni, Seyed Ziaeddin Alborzi, Kevin Dalleau, Bishnu Sarker, Emmanuel Bresso, Claire Lacomblez, Floriane Odje, Athénaïs Vaginay.
Our main application for Axis 1 : "New Approaches for Knowledge Discovery in Structural Databases", concerns biomedical knowledge discovery. We intend to develop KDD approaches on preclinical (experimental) or clinical datasets integrated with knowledge graphs with a focus on discovering which PPIs or molecular machines play an essentiel role in the onset of a disease and/or for personalized medicine.
As a first step we have been involved since 2015 in the ANR RHU “FIGHT-HF” (Fight Heart Failure) project, which is coordinated by the CIC-P (Centre d'Investigation Clinique Plurithématique) at the CHRU Nancy and INSERM U1116. In this project, the molecular mechanisms that underly heart failure (HF) are re-visited at the cellular and tissue levels in order to adapt treatments to patients' needs in a more personalized way. The Capsid team is in charge of a workpackage dedicated to network science. A platform has been constructed with the help of a company called Edgeleap (Utrecht, NL) in which biological molecular data and ontologies, available from public sources, are represented in a single integrated complex network also known as knowledge graph. We are developing querying and analysis facilities to help biologists and clinicians interpreting their cohort results in the light of existing interactions and knowledge. We are also currently analyzing pre-clinical data produced at the INSERM unit on the comparison of aging process in obese versus lean rats. Using our expertise in receptor-ligand docking, we are investigating possible cross-talks between mineralocorticoid and other nuclear receptors.
Another application is carried out in the context of a UL-funded interdisciplinary project in collaboration with the CRAN laboratory. It concerns the study of the role of estrogen receptors in the development of gliobastoma tumors. The available data is high-dimensional but involves rather small numbers of samples. The challenge is to identify relevant sets of genes which are differentially expressed in various phenoptyped groups (w.r.t. gender, age, tumor grade). The objectives are to infer pathways involving these genes and to propose candidate models of tumor development which will be experimentally tested thanks to an ex-vivo experimental system available at the CRAN.
Finally, simulating biological networks will be important to understand biological systems and test new hypotheses. One major challenge is the identification of perturbations responsible for the transformation of a healthy system to a pathological one and the discovery of therapeutic targets to reverse this transformation. Control theory, which consists in finding interventions on a system in order to prevent it to go in undesirable states or to force it to converge towards a desired state, is of great interest for this challenge. It can be formulated as “How to force a broken system (pathological) to act as it should do (normal state)?”. Many formalisms are used to model biological processes, such as Differential Equations (DE), Boolean Networks (BN), cellular automata. In her PhD thesis, Athenaïs Vaginay investigates ways to find a BN fitting both the knowledge about topology and state transitions “inferred“ from experimental data. This step is known as “boolean function synthesis”. Our aim is to design automated methods for building biological networks and define operators to intervene on them. Our approaches will be driven by knowledge and keep close connection with experimental data.