In silico design of biomass-to-hydrogen Pathways: A review

Abstract

Hydrogen production from biomass has emerged as a promising renewable energy solution. However, significant challenges such as thermodynamic inefficiencies, high raw material costs, low hydrogen molar yields, and difficulties in using lignocellulosic feedstocks hinder its large-scale implementation. Conventional methods have not been able to effectively address these issues, which makes modern approaches, such as in silico strategies, essential. This review explores the role of computational models like genome-scale metabolic modeling, synthetic biology, and metabolic pathway reconstruction in overcoming these barriers. By utilizing vast genomic databases and advanced computational tools, researchers can optimize microbial systems, improve hydrogen yields, and design more efficient biohydrogen production processes. These in silico methods provide a pathway to enhance the efficiency of biomass processing and enable the development of scalable and sustainable hydrogen production technologies. The review highlights recent advancements and discusses the potential of in silico approaches to address key technological and economic limitations, paving the way for the future of biohydrogen energy.