AI- and Biotechnology-Driven Digital Design of Biohydrogen-Producing Microbiota

Abstract

Biohydrogen, produced via microbial fermentation of biomass waste, is poised to play a pivotal role in China’s green energy transition. Nonetheless, significant obstacles such as high costs, unstable production dynamics, regulatory and metabolic inefficiencies, and limited actual hydrogen yields hinder large-scale application. Addressing these challenges necessitates the integration of machine learning and synthetic biology, forming a robust pathway to enhanced process efficacy and output consistency. The convergence of artificial intelligence (AI) and biotechnology (BT) is revolutionizing biohydrogen production by shifting from traditional empirical methodologies to predictive, engineering-based frameworks. AI equips researchers to interpret and optimize complex metabolic and genetic networks through machine learning and genome-scale modeling. Concurrently, BT is evolving to manipulate microbial communities holistically via synthetic ecology and dynamic modeling. Here, we propose a “digital microbial community” paradigm, intergating multi-scale metabolic modeling and emergent property prediction, AI-powered ecological niche decomposition and closed-loop BT enhanced evolutionary framework for continuous optimization of digital twins through experimental feedback. This fusion facilitates the rational design and real-time optimization of programmable microbial ecosystems, greatly enhancing biohydrogen producing control and efficiency. The transition to digital and data-driven design, utilizing multi-omics and ecosystem-level analytics, further bolsters precision and scalability. While moving from single cells to complex microbial consortia introduces challenges, such as non-linear dynamics and ecosystem stability, the synergy of AI and BT underpins the intelligent, resilient, and sustainable production of biohydrogen, thereby reinforcing its potential as a foundational component of China’s renewable energy landscape.