Interlocking synthetic biology, systems biology and artificial intelligence to develop a more efficient metabolic engineering workflow: a highly efficient biotechnological production platform for monoclonal chitooligosaccharides.

Today, synthetic biology (SynBio) and systems biology (SysBio) are developing at an unprecedented pace into major drivers of industrial biotechnology. Despite these recent advances, the transformation of wild type organisms into highly efficient microbial cell factories (MCFs) remains a truly daunting task due to the enormous complexity of microbial cells. Typically, to steer the strain engineering process a “Design-Build-Test-Learn” (DBTL) cycle is iteratively run. However, due to the little insight in the complexity of the microorganism’s metabolism, the running of the DBTL cycle results in many “try-fail” cycles. Here, to move more successfully trough the DBTL cycle, and, hence, to more successfully engineer MCFs, we will introduce cutting-edge and cross-disciplinary techniques from the fields of SynBio, SysBio combined with artificial intelligence (AI) techniques, creating a more reliable and efficient metabolic engineering work flow. This will certainly be beneficial for the exploitation of the hexosamine biosynthesis pathway (HBP), which requires precursors from all over the metabolism and, moreover, is tightly regulated on all levels of regulatory control. This will be demonstrated using the development of MCFs for the production of the HBP-derived chitooligosaccharides (COS) as proof-of-concept. COS have unique characteristics resulting in numerous (potential) applications in the fields of cosmetics, feed, food and pharma.