Mathematics cell
A unit dedicated to the
Mathematical modeling
TBI's Mathematics Unit focuses on the mathematical modeling of biochemical and microbial systems.
Expertises
Mathematical modeling biochemical and microbial systems (bacterial metabolism, microbial growth, enzymatic cocktails, isotopic labelling simulation to estimate metabolic fluxes (13C-fluxomique)
Estimation of parameters
Artificial intelligence (AI) with a focus on machine learning, encompasses supervised learning for predicting outcomes based on labelled data, unsupervised learning for identifying patterns and structures within unlabelled data, and incremental learning, which allows models to adapt and learn from new data over time without requiring complete retraining. Additionally, explainable AI (XAI) focuses on making AI systems transparent and interpretable, ensuring that complex models provide clear and understandable insights.
Offers / technologies / tools
- Translation of biological models into mathematical models/software
- (Co-)development of scientific software (R, Python, C++, ...)
- (Co-)writing articles with a mathematical component
- Co-supervision of PhD theses with a focus on artificial intelligence, covering machine learning in the following areas:
- Supervised learning
- Unsupervised learning
- Incremental learning
- Explainable AI
Metabohub, Université de Pennsylvanie, Wageningen University & Research, Universidad de Granada
– TBI: Equipe TIM (Transfert, Interface, Mélange) et l’equipe CIMES, Equipe METASYS (TBI), Equipe SYMBIOSE (TBI), METATOUL
DynaFluxR : de mesures cinétiques de métabolites vers des vitesses de réactions sans modèle de régulation
The BIOINDUSTRY 4.0 project will develop standards for generating high-quality, interoperable, multi-scale bioprocess data and metadata. By developing data-driven approaches and leveraging artificial intelligence to enhance new decision support systems and digital twins, the project will enable real-time control of bioprocesses.