CAREER: Learning spatiotemporal dynamics of tissue regrowth at multiple spatial scales

This project will develop a multi-faceted computational framework to understand how certain animals such as axolotls can regrow entire limbs after injury, a capability humans largely lack. By combining mathematical equations, computer simulations, and imaging data, the research will uncover how cells move, divide, and mechanically reorganize to rebuild tissues during regeneration. The project will develop new open-source computational tools that connect individual cell behavior to large-scale tissue growth, with potential applications in biotechnology, regenerative medicine, wound healing, and engineered tissues. These tools may ultimately help identify strategies for promoting tissue repair in humans. The work also includes educational and community impacts through undergraduate research opportunities, quantitative learning modules for life science students, and hands-on outreach activities that introduce computational thinking and biological pattern formation to local K-12 students. Together, the project advances both regenerative biology research and broader access to computational approaches in the life sciences. A central challenge in regeneration biology is that distinct cellular mechanisms (including directed migration, proliferation, structural and mechanical rearrangement) can produce similar large-scale tissue morphologies, making them difficult to distinguish experimentally. To address this problem, multiscale mathematical and computational frameworks will be developed to identify the cellular and mechanical processes governing blastema formation, the first and decisive step in tissue regrowth. The project integrates continuum mechanics, stochastic agent-based modeling, numerical free-boundary methods, generative AI approaches with neural network operator learning, and data-driven parameter inference to connect cell-scale dynamics with tissue-scale morphogenesis. The research has three interconnected objectives: (1) develop continuum theories for multilayer growing tissues with coupled viscoelastic and active mechanical responses; (2) construct agent-based models that incorporate cell proliferation, migration, polarity, and mechanically regulated interactions to infer cellular behaviors from observed shape changes; and (3) extend these approaches to scalable 3D simulations of tissue growth and pattern formation over longer timescales. The models will be calibrated and validated using imaging data from regenerating axolotl limbs obtained through experimental collaboration. In addition to advancing the mathematical foundations of morphogenesis and regeneration, the resulting computational pipeline will establish generalizable tools for extracting active and passive tissue dynamics from biological imaging data, with broader relevance to wound repair, biomaterials, and engineered tissue systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2542804 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Calina Copos | Institution: Northeastern University, BOSTON, MA | Award Amount: $674,161 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2542804 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2542804.html