Artificial intelligence in microbial biotechnology for food security: current state and challenges
TL;DR
Challenges that beset the current landscape still include overall adoption, difficult-to-understand models or algorithm interpretability, quality input of training data, good ethical practices, high computational cost associated with complex structural simulations, and the need for standardized processes.
Abstract
The global food system is constantly being constrained by biotic and abiotic challenges, resulting in instability and insecurity, particularly in regions like sub-Saharan Africa, where agricultural productivity often remains below global averages. Microbial biotechnology includes many sustainable ways of leveraging the metabolic potential of microorganisms, such as bacteria, fungi, and viruses, to address food insecurity through enhanced crop resilience, precision fermentation, and improved soil health. Keeping up with these demands now requires constant innovative multidisciplinary approaches in the fast-growing field of artificial intelligence (AI). AI is reinventing microbial biotechnology via various applications in the areas of taxonomic profiling, metabolic modeling, and the design of microbial cell factories. This review evaluates the transformative role of AI in optimizing these microbial systems. Current advancements showcase the use of machine learning and deep learning architectures, such as convolutional neural networks and transformers, to accelerate the discovery of novel biofertilizers and biocontrol agents. In precision fermentation, AI-driven models and reinforcement learning are increasingly used to optimize the clustered regularly interspaced short palindromic repeats (CRISPR)-based microbial engineering, as well as bioprospecting for microbes that can improve soil health. However, challenges that beset the current landscape still include overall adoption, difficult-to-understand models or algorithm interpretability, quality input of training data, good ethical practices, high computational cost associated with complex structural simulations, and the need for standardized processes to make sure that AI applications are reliable and applicable in different microbiological settings. While AI is an essential ingredient for futuristic microbial biotechnology, tackling these technical and ethical hurdles is key to achieving stable food security.
