Predictive Algal Systems Biology: Integrating Omics, Genome-Scale Metabolic Models, and Machine Learning

Algae represent one of the most metabolically diverse and ecologically significant groups of photosynthetic organisms, contributing fundamentally to global biogeochemical cycles while offering major potential for biotechnology applications such as biofuels, nutraceuticals, wastewater remediation, and carbon capture. However, the complexity of algal metabolism, driven by evolutionary diversity, compartmentalized cellular organization, and strong environmental coupling, makes predictive understanding of their physiology challenging. In recent years, systems biology approaches combining omics technologies, genome-scale metabolic models, and data-driven methods have begun to transform algal research from descriptive studies toward predictive frameworks. This review summarizes the current state of algal systems biology, highlighting advances in genomics, transcriptomics, proteomics, and metabolomics that enable mechanistic insights into metabolic regulation and environmental adaptation. We discuss the development, curation, and application of algal GEMs across diverse lineages, emphasizing their role in predicting metabolic flux distributions, nutrient utilization, and lipid biosynthesis. In parallel, machine learning and artificial intelligence approaches have emerged to model algal growth and cultivation performance from large physiological datasets. Finally, we discuss emerging hybrid modeling strategies that integrate mechanistic metabolic networks with data-driven predictions, outlining how these frameworks can enable next-generation predictive algal biotechnology and guide rational design of cultivation and metabolic engineering strategies.