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Microbial production of target molecules has advanced significantly in recent years driven by innovations in enzyme engineering, DNA synthesis, and genomic editing. However, to access the massive potential of microbial production, a vast parametric space remains to be investigated to optimize these biobased processes for a robust bioeconomy. Here, we review the current state of the art, some key challenges and possible solutions. We see a critical role of automation, high-throughput technologies, self-driving and cloud labs, and data management to enable Artificial Intelligence/Machine Learning and mechanistic models to overcome the design space challenges and accelerate the development of novel bio-based solutions. Accurate models will expedite the development and scale-up of engineered microbes for a range of final products from many starting materials. • Microbial conversion can enable production of a large number of products from a range of starting products contributing to a strong manufacturing economy. • The optimization space to maximize many microbial conversions for technology transfer is vast. • Automation and rapid workflows can enable access to optimization space not possible using the throughput allowed by traditional laboratory work. • Automated and high-throughput workflows also generate robust data for AI-ML approaches. • Despite considerable breakthroughs in automated and high-throughput workflows, implementation in microbial synthetic biology still has a few hurdles that need to be overcome.
Petzold et al. (Tue,) studied this question.