摘要
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 holisti-cally 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 pro-ducing 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.
