Modeling and simulating brain development: Multiscale, data-driven, and predictive approaches

Brain development is a complex, dynamic, and multiscale process that unfolds from early embryogenesis through adolescence and into early adulthood. Across this trajectory, tightly coordinated mechanisms, including neural proliferation, migration, differentiation, synaptogenesis, pruning, and activity-dependent plasticity, shape the structure and function of the mature brain. These processes emerge through nonlinear interactions between genetic programs, cellular dynamics, environmental influences, and experience.While experimental research has identified key mechanisms and developmental milestones, integrating findings across spatial and temporal scales remains a major challenge. Understanding how molecular signaling cascades give rise to circuit formation, how local plasticity shapes large-scale networks, and how early perturbations alter developmental trajectories requires computational frameworks capable of capturing dynamic, cross-level interactions.Computational modeling and simulation provide powerful tools for addressing this challenge. By integrating data from imaging, electrophysiology, connectomics, and single-cell sequencing, models can bridge molecular, cellular, circuit, and systems levels of analysis. Advances in machine learning, generative modeling, and large-scale simulation now enable the construction of data-driven and predictive models of neurodevelopment, offering new opportunities to test mechanistic hypotheses and forecast developmental outcomes.This Research Topic aims to bring together interdisciplinary contributions that advance computational and theoretical approaches to understanding brain development. We seek studies that simulate, model, or analytically characterize neurodevelopmental processes across scales and that integrate biological realism with predictive power.We welcome contributions including, but not limited to:- Multiscale models linking molecular signaling, cellular interactions, circuit formation, and network dynamics- Simulations of neurogenesis, migration, synapse formation, pruning, and activity-dependent plasticity- Data-driven and AI-based models of developmental trajectories- Generative or predictive frameworks for typical and atypical brain development- Modeling critical periods, experience-dependent development, and environmental influences- Systems-level models of network maturation and functional specialization- Computational approaches to understanding neurodevelopmental disorders- Methodological advances for integrating longitudinal, multimodal, or high-dimensional developmental datasets Topic editors: Ruben A. Tikidji-Hamburyan, Suman Saha, Han Lu Source: https://www.frontiersin.org/research-topics/78921/modeling-and-simulating-brain-development-multiscale-data-driven-and-predictive-approaches