Aug 2026· Frontiers in Bacteriology· 0 citations· 105 references
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.
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.
Carmine Giardino, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 175 citations· ⚡19
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
It is found that roles of MVPs in startups were not fully aware by entrepreneurs, and entrepreneurs should consider a systematic approach to fully explore the value of MVP, as a multiple facet product (MFP).
Anh Nguyen-Duc, P. Abrahamsson· International Conference on...· 93 citations· ⚡9
It is found that what perceived as biggest challenges by software startups do vary across different life cycle stages, even though its significance decreases when the learning focuses of the startups move from problem to solution and their products mature.
Xiaofeng Wang, Henry Edison, Sohaib Shahid Bajwa et al.· International Conference on...· 62 citations· ⚡6
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
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