A model-theoretical framework to support decisions and accelerate the Design-Build-Test-Learn cycle in Industrial Biotechnology applications

DEBONAIR

The research objective is to build a model-theoretical framework to accelerate Design-Build-Test-Learn (DBTL) cycles in Industrial Biotechnology applications.

The research introduces breakthroughs to connect underlying sciences with lab experiments and process engineering.

Synthetic biology offers an extremely promising knowledge basis producing substantial volumes of data and information to assist with the development of custom-made biochemistries.

Motivation and challenges

Biorefineries stand in the center of these developments largely depending on biochemical advances for processing technologies and innovations.

Industrial Biotechnology, essentially a convergence of numerous scientific and engineering disciplines, provides biocatalysts and bioprocesses for a wide range of products.

A model-theoretical framework to support decisions and accelerate the Design-Build-Test-Learn cycle in Industrial Biotechnology applications

DEBONAIR

The research objective is to build a model-theoretical framework to accelerate Design-Build-Test-Learn (DBTL) cycles in Industrial Biotechnology applications.

Motivation and challenges

Biorefineries stand in the center of these developments largely depending on biochemical advances for processing technologies and innovations.

Industrial Biotechnology, essentially a convergence of numerous scientific and engineering disciplines, provides biocatalysts and bioprocesses for a wide range of products.

Bioeconomy is worth €2 trillion in EU with an annual turnover to account for approximately 9% of theworkforce (more than 22 million jobs).

The research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the 2nd Call for H.F.R.I. Research Projects to support Faculty Members & Researchers (Project Number: 03817).