Biological organization persists despite rapid dynamics and component turnover. Distinguishing assembly and persistence timescales, we propose that persistence can reside in relationships rather than components. In genomic, neural, developmental, and ecological systems, higher-order interactions can propagate conditional dependencies across timescales, allowing slow regulatory contexts to modulate faster dynamics.
With the advancement of metabolic modeling techniques, models are evolving from static, single-dimensional approaches to dynamic, multidimensional, and hybrid intelligence frameworks, thereby laying the foundation for next-generation metabolic models. The integration of multi-constraint modeling, dynamic simulations, and artificial intelligence methods has substantially improved the predictive accuracy and application scope of whole-cell models. These integrated approaches have been demonstrated to optimize model parameters, enhance the understanding of genotype-phenotype relationships, and offer powerful tools for synthetic biology, precision medicine, and biomanufacturing. Nevertheless, current models continue to encounter challenges, including parameter uncertainty, computational efficiency, and scalability issues. Future research will focus on integrating multiscale data with dynamic simulations to enable a transition from descriptive characterization to predictive modeling. This review summarizes the key advances, typical applications, and current challenges of next-generation metabolic modeling, while also outlining future trends.
