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Presentation of the CSTB team
Complex Systems are present everywhere around us. They can be defined as interacting reconfigurable entities, structured on several emerging levels of organization, in which the whole cannot be understood without the parts and the parts cannot be understood without the whole.
A "complex system" is generally any system comprising a large number of heterogeneous entities, between which local interactions create multiple levels of structuring and collective organization. Examples include natural systems ranging from biomolecules and living cells to social networks and the ecosphere, as well as sophisticated artificial systems such as the Internet, large power grids or any large scale distributed software.
Biological systems are unique in the complexity of their functioning and regulation, and the integrated study of the multiple levels that contribute to the final behavior of these systems now represents a new challenge for the scientific community. Thanks to the ever increasing quantities of data that describe each component of the system in detail, new opportunities exist to develop descriptive and predictive modeling approaches. These developments are applicable to the whole field of complex systems science, from social networks to finance.
In medicine, this 'systemic' awareness has led to the emergence of a new field of interdisciplinary research: translational medicine. This field aims to understand and exploit the diversity of clinical and phenotypic manifestations of diseases in patients to better understand and model the emergence and evolution of diseases. Ultimately, the aim of these developments is to lead to optimized and personalized treatments.
The "Complex Systems and Translational Bioinformatics" team covers a broad spectrum of research in computer science, from bioinformatics to artificial intelligence.
In this context, CSTB intends to actively participate in 4P developments (Participative, Predictive, Preventive and Personalized) by developing original solutions in the fields of education, health or industry within two research themes:
We have a long experience in biomedical data analysis, annotation and mining. Our goals are mainly focused on knowledge extraction from genetic and medical data, from codes in DNA to patient phenotypes. We focus on the management of massive data (quality control, integration, FAIRification, security...), as well as the exploitation of evolutionary information in sequence-structure-function studies. In two key areas, genetic diseases and biodiversity, we aim to identify associations between genotype and phenotype and to understand patterns and trends in the data. Traditional methods, which have been successful in the study of simple systems, are limiting when applied to complex dynamic systems, where the genetic makeup of individuals underlies a large number of variations that interact with each other producing effects from the atomic level to the organism.
We have expertise in the modeling of complex systems and nature-inspired optimization algorithms, including artificial evolution and artificial immune systems. These inherently massively parallel and asynchronous systems are constitutive of IT in the 21st century, composed of massively parallel computers in networks.
- The applications of nature-inspired complex systems include IT security and the search for patterns (artificial immune systems), optimization and artificial intelligence (artificial evolution) ecosystems for calculation and teaching (biological ecosystems) and of course, participatory, predictive, preventive and personalized translational medicine (which is the case for all complex systems).
- Indeed, on the basis of the observed data ("Participative"), we will try to determine "Predictive" models allowing to implement a "Prevention" in a "Personalized" way, for the Factory of the Future, for IT security, health (patient networks) and education (student / teacher networks).
The team also coordinates the BiGEST-ICube (Bio-Informatics and Genomics) platform of the ICUBE laboratory, offering the community a unique portal to databases and software for bioinformatics, contributes to the plateform GAIA (informatique Graphique, Analyse de données et Intelligence Artificielle), and proposes 2 services for massively parallel computation (EASEA CLOUD) and education (POEM).
Internationally, the team coordinates (with the Havre University) the UniTwin CS-DC of the UNESCO: a Digital Campus Complex Systems with more than 120 universities (>3 million students, >3000 researchers in 28 countries).
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