Harnessing dynamic metabolomics for bioprocess prediction and beyond

Abstract

As the youngest of the quartet of systems biology tools alongside genomics, transcriptomics, and proteomics, metabolomics provides an immediate and dynamic recording of cells in response to genetic and/or environmental perturbations. Metabolomics study accelerates learning steps within the iterative design, build, test, and learn (DBTL) cycle for enhancing bioproduction capability. The associations between biological networks and environmental factors facilitate predictive modeling of cellular response, which is the basis for industrial application. This chapter presents an update on the metabolomics-driven biosystems engineering and bioprocess design principle from the metabolic perspective of organisms. Along with the introduction of the isotope dilution mass spectrometry (ID-MS) method to the fast sampling, quenching, and extraction protocol for quantitative metabolomics, metabolomics-assisted engineering biology and the establishment of metabolically structured models are highlighted. Furthermore, a computational framework based on a coupled metabolic-hydrodynamic approach is advocated to assess the interlocking architectures between environmental and biological networks in large-scale bioprocesses and provide suggestions toward bioreactor evaluation, scale-down, and optimization.