Our main interests are in:
- Dynamic regulation of gene expression for control of variability in gene expression and regulation of novel routes to obtain complex chemicals
Multi-objective optimization-based tuning of synthetic genetic circuits and its application to the standard characterization of parts and modules
- Machine learning for modeling and design of metabolic pathways. Automated workflows (Retropath), selection of enzymes (Selenzyme), design of DNA (promoters, RBS, etc) and optimal experimental design
- Machine learning based prediction of chemical diversity including the discovery of new bio-products and bioparts.
- Automation of the DBTL cycle. Protocol development for optimal process design biofabrication.
Analysis of metabolic networks using possibilistic methods and network theory for Metabolic Flux Analysis and Flux Balance Analysis
Sliding-mode observers and controllers for estimation and control in biosystems