Biological systems are unique in the complexity of their functions and their regulation. As a consequnece, the integrated study of the multiple levels that contribute to the final behavior of these systems today represents a new challenge for the scientific community. Thanks to the ever increasing amounts of data that describe in detail each of the system components, new opportunities are available to develop descriptive and predictive modeling approaches. These developments are applicable to the entire field of complex systems science, from social networks to finance.
In medicine, this 'systems' awareness has given rise to a new interdisciplinary research field: translational medicine. This domain aims to understand and exploit the diversity of the clinical and phenotypic manifestations of diseases in patients to better understand and model the emergence and evolution of disease. Ultimately, these developments should result in more optimized and personalized treatments.
The "Complex Systems and Translational Bioinformatics" team therefore covers a broad spectrum of research in computer science, ranging from bioinformatics to artificial intelligence. The questions we address are focused firstly, on how to identify the critical points in a complex biological system, and secondly, on how to predict the impact of these perturbations (mutations, drugs, for example) on the stability and behavior of the system. This requires theoretical and multi-scale multi-modal modeling of the biological functions and their regulation underlying the observed phenotypes, while taking into account the context of their dynamic interactions with the environment.
Our research axes are:
LBGI has extensive experience in the analysis, annotation and mining of biomedical data. In particular, in the field of rare genetic diseases, the LBGI seeks to identify relations between genotype and phenotype and understand the patterns and trends in the data. The traditional methods, which have been successful in the study of simple systems, reach their limits when applied to complex dynamic systems, where the genetic background of each patient implies a large number of variations that interact with each other to produce effects from the atomic level up to the organism.
SONIC has a complementary expertise in complex systems modeling and nature-inspired optimization algorithms, with applications in various fields, ranging from the determination of crystallographic structures to the optimization of learning paths in e-learning or the management of digital medical records.
The team members participates in research projects, often involving collaborations with other laboratories or companies. Two software platforms support this work.
Team life consists of team meetings, visiting researchers, and other events. The team regularly offers internships, PhD and post-doc positions.