Équipe : Stochastic Systems Biology of Gene Regulation
Responsable : Nacho Molina
Laboratoire : UMR 7104/U1258 Institut de Génétique et de Biologie Moléculaire et Cellulaire (Strasbourg)
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Descriptif :
The main research focus of the Molina team is to develop stochastic and large-scale models of eukaryotic gene regulation. The work of the team lays at the interface between machine learning and biophysics. Indeed, we believe that the next breakthrough in modelling biological systems requires a combination of both. Machine learning provides data-driven identification of new principles from which novel large-scale mechanistic models can be built using tools from systems biophysics. This approach allow us to analyse and integrate single-cell genomic and imaging data and generate testable hypotheses based on causal mechanisms.
The main research focus of the Molina team is to develop stochastic and large-scale models of eukaryotic gene regulation. The work of the team lays at the interface between machine learning and biophysics. Indeed, we believe that the next breakthrough in modelling biological systems requires a combination of both. Machine learning provides data-driven identification of new principles from which novel large-scale mechanistic models can be built using tools from systems biophysics. This approach allow us to analyse and integrate single-cell genomic and imaging data and generate testable hypotheses based on causal mechanisms.
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