DigiCells: Next-generation digital twins for biological cell environments

Recent advances in imaging and computing have revolutionised how we study the structure and function of living systems. At the same time, rapidly growing volumes of bioimaging data have created a need for new solutions to analyse, integrate and interpret this information.

The DigiCells project aims to establish algorithmic and technological foundations for next-generation modelling and analysis of biological cell environments. By integrating geometric structure, biophysical dynamics and high-resolution imaging data, the project brings together scientific computing, machine learning, computational mathematics, bioimaging and biomedical research.

DigiCells will develop accurate and scalable multigrid algorithms that combine finite element methods with graph neural networks and neural operators. These methods will enable simulation and learning of complex biological interactions directly from imaging data. The project will demonstrate the technology through digital twins for cells-on-a-chip and for ionic processes in cardiology and neuroscience, and will distribute reusable open-source software through the FEniCS and Firedrake projects.

Funding

This project is funded through the Research council of Norway's funding scheme “Researcher projects for ICT Renewal and Development” (forskningsradet.no).