Resumen
The project SMARTBIOFAB will develop and apply methods and tools for designing, building and testing genetic synthetic
circuits for optimal bioproduction of metabolites of interest, with an emphasis on the use of machine learning-based
modelling, design, optimization and feedback regulation. Engineering biology (EngBio, an interdisciplinary area,
encompassing synthetic and systems biology and bringing engineering and biology together) emphasizes the use of
engineering principles and methodologies in the design, construction and characterization of biological systems to be
applied in industrial, health, environmental and other applications1. Its underpinning enabling tools and technologies are
systems engineering and automation, computational design and modelling, machine learning, genetic engineering and
assembly, and analytic omics. Thus, EngBio implies a multidisciplinary approach, bringing together expertise from biology,
engineering and computer science. As seen in the project goals and the corresponding tasks, SMARTBIOFAB is a
challenging project addressing problems related to applied biotechnology, machine learning, and automation and feedback
control. It thus requires the added value provided by the members of the research group allowing for critical mass and
complementary expertise.
