DBTL4SynBioCON will develop a Design-Build-Test-Learn workflow for design, experimental characterization, and optimal tuning of dynamic gene expression regulation mechanisms in bacterial cells used as microbial devices and biofactories.

The project will emphasize contributions in four main pillars:

• (I) Improved modular characterization (model identification) of gene synthetic circuits (feedback loops),
  devices (biosensors and biocontrollers), and their DNA building blocks (bioparts). The project will focus on context-aware methods considering the coupling interactions caused by competition for shared cell resources and the effect of cell growth and environmental changes in a bioreactor setup. To this end, DBTL4SynBioCON will
  • (a) develop hybrid mechanistic and data-driven context-aware dynamic models,
  • (b) develop machine learning (ML) tools for mapping from models of bioparts to their corresponding DNA sequences, and
  • (c) will leverage these tools for the modular modeling and identification of complex of gene synthetic circuits.
• (II) develop biomolecular high-gain controllers for dynamic regulation of gene expression in bacterial cells based on molecular annihilation mechanisms, time-scale separation, and generation of ultrasensitive responses.
• (III) Implement an iterative DBTL workflow to iteratively characterize and build complex gene synthetic devices and feedback regulation loops with desired performance, and
• (IV) Standardisation of the DBTL4SynBioCON workflow, with focus on:
  • (a) improved protocols for standard calibrated measurement,
  • (b) definition of automated liquidhandling protocols, and
  • (c) refinement of operational protocols for widespread adoption and efficiency.
    

DBTL4SynBioCON will validate its workflow methods and tools using relevant case studies using the bacterial host E. coli, including the dynamic regulation of the production of naringenin and heparosan, compounds of nutraceutical, biomedical, and therapeutical interest with different needs for regulation.