Computational Strategies for Maximizing Biomass and Metabolite Yields for Bioproduction

The chapter offers a succinct yet thorough background on the evolution of bio-production, emphasizing how traditional methods often fell short in terms of yield optimization and resource efficiency. It then delves into the paradigm shift brought about by the integration of computational strategies, highlighting key concepts such as metabolic engineering, systems biology, and synthetic biology. The core of the chapter is dedicated to detailed discussions of various computational techniques, including machine learning algorithms, genome-scale metabolic models (GSMMs), and flux balance analysis (FBA). Each method is illustrated through case studies and real-world applications, demonstrating their effectiveness in optimizing biomass production and metabolite yields in various organisms, from micro-algae to complex multicellular systems. In conclusion, the chapter provides a forward-looking perspective, envisaging future developments in computational approaches for bio-production. It speculates on how emerging technologies, like artificial intelligence and advanced genetic engineering, could further revolutionize biomass and metabolite production, offering insights into potential new frontiers in bio-manufacturing and sustainability. This chapter is a valuable resource for offering a rich blend of theoretical knowledge and practical insights into the future of bio-production.