Pioneer: Dynamical Systems Biology for Spatiotemporal Omics Data

Dynamical systems biology is an emerging interdisciplinary framework that aims to understand how complex biological processes arise from time-dependent interactions among molecular, cellular, and tissue-level components. The rapid development of high-throughput technologies, including single-cell and spatial multi-omics, has generated rich spatiotemporal datasets that demand theoretical and computational tools beyond static analyses. In this review, we highlight recent advances that connect dynamical systems theory with modern omics measurements, focusing on three complementary directions. First, we summarize dynamical network biomarkers theory for detecting tipping points and early-warning signals of critical transitions during disease progression and cell-fate decisions. Second, we discuss energy-landscape approaches that provide quantitative descriptions of stability, transition barriers, and state switching in noisy biological systems. Third, we review cellular dynamical models for reconstructing continuous trajectories and inferring transition dynamics from single-cell omics data. Together, these perspectives provide a unified dynamical view of living systems and support the development of dynamical virtual cells and dynamical foundation models.