We are dedicated to developing a synthetic biology toolbox for bacteria, fungi, and yeast, aimed at comprehensively understanding sophisticated cellular function and cleverly reprogramming it to achieve desired functionalities.
At our core, we are driven by a commitment to harnessing the power of synthetic biology to address pressing challenges facing our planet. Through our concerted efforts, we have made significant contributions to the advancement of synthetic biology tools and methods tailored specifically for pathway design. Our mission is clear: to pave the way for a more sustainable future by leveraging cutting-edge technologies and techniques. Central to our approach is the development of synthetic promoters, biosensors, genome editing tools, and combinatorial optimization methodologies. We combine these tools with automatic bioprocessing and droplet-based single-cell analysis techniques to establish a comprehensive platform for microbial bioengineering.
Our group pioneers sustainable manufacturing of secondary natural products using yeast through synthetic biology optimization, shortening the Design-Build-Test-Learn cycle and accelerating biomanufacturing
Secondary natural products (NPs) are produced by various organisms including plants, fungi, and bacteria as part of their defense mechanisms or for inter-species communication. Due to their diverse chemical structure, NPs show a wide range of biological activities, spanning from pharmaceutical applications to pesticidal properties. However, the low abundance of NPs makes their extraction from nature inefficient, while chemical synthesis is challenging and unsustainable. Saccharomyces cerevisiae, Pichia pastoris and Yarrowia lipolytica are excellent manufacturing systems for the production of NPs. Our group is dedicated to advancing the potential of synthetic biology in providing sustainable manufacturing processes in the yeasts through system-associated optimization at four levels: genetics, temporal controllers, productivity screening, and scalability. Multilevel data of the optimized system are then fed back into learning and design engines, shortening the synthetic biology Design-Build-Test-Learn (DBTL) cycle of biomanufacturing projects.
We are interested in developing microbial strains geared towards maximizing yield and productivity of target chemicals, alongside optimizing production of multi-subunit protein complexes and unraveling the complexities of gene regulatory networks.
Although one of our main aims is development of microbial strains able to optimize and maximize yield and productivity of target chemicals, e.g. biofuels, biomaterials and, medicines, we also aim to maximize production of multi-subunit protein complexes like P450s, with huge application, using combinatorial optimization methodologies. We are also deeply committed to unraveling the complexities of gene regulatory networks (GRNs). Smartly designed combinatorial libraries can generate huge number of GRN variants, where the optimal expression level of regulators of networks can be monitored. Moreover, to overcome limitations regarding the transferability and expression of all involved systems in one chassis, a promising alternative solution is to focus on parallel optimization of metabolic pathways divided among different cells in synthetic microbial consortia.
