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