Scientific Computing for Image-based Systems Biology

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The Sbalzarini Lab does research in Scientific Computing for Image-Based Systems Biology. They exploit the unifying framework of particle methods for data-driven modelling and numerical simulation of tissue development. Their research focus is in Computer Science and Mathematics, but in close collaboration with experimental groups, to pioneer innovative machine learning, AI, and simulation approaches to challenging biological questions.

Since computational biology comes with a unique set of challenges, from complex geometric shapes to non-equilibrium processes, we develop and apply novel computational methods in a targeted co-design approach with the ultimate mission of understanding the self-organization of tissues.

In our work, Theory, Algorithms, Software, and the biological Application co-evolve. We focus on the following topics as required in image-based systems biology:

  • Adaptive multi-scale simulation methods for spatio-temporal dynamics on complex and moving/deforming 3D geometries and surfaces.
  • Data-driven modeling of biological dynamics in space and time from image data, and development of suitable machine learning algorithms.
  • Parallel and scalable high-performance computing for systems biology, and the software engineering of it.