Strain Engineering, Flux Design, and Metabolic Production Using Big Data: Ongoing Advances and Opportunities

Recent technological advancements have significantly enhanced the precision of high-throughput instruments, enabling the collection of multiple datasets at an unprecedented scale in the biotechnological domain. These datasets, known in computer science as “Big Data,” are generally complex and heterogeneous. While significant progress has been made in developing scalable computational tools for their acquisition and management, contributions to the integration and analysis of massive datasets in the biotechnological domain emerged more recently and are still evolving. Notwithstanding, the convergence of industrial biotechnology and Big Data analytics is already revolutionizing biological engineering projects. This chapter provides an overview of the relation between Systems Metabolic Engineering and Big Data, with a focus on the role of machine learning advances for data integration, analysis, and modeling across descriptive and predictive ends. Illustrative biotechnological case studies where the ability to integrate and explore large-scale datasets becomes an important factor, strain engineering, flux design, and metabolic production, are finally highlighted.