Computational Physiology
The Computational Physiology Department's mission is to use mathematical modelling to gain insight into human health, disease, and treatment.
In recent years, modelling and simulation of biophysical phenomena have matured in both scope and methodology. These methods have now reached a point where they can contribute significantly to the present the understanding of physiology and disease. The Computational Physiology (ComPhy) department at Simula is an integrated team of researchers working to develop cutting-edge biological simulation tools.
Focus areas
A particular strength of the ComPhy department is its members’ diverse set of backgrounds and skill sets. Our broad expertise means we can enable research into numerical and computational methods to develop state-of-the-art simulation tools, as well as the targeted application of these tools to gain mechanistic insight into diverse biophysical phenomena.
The core focus of the ComPhy department is research into biophysical models of the heart, and modelling efforts span a wide range of spatial and temporal scales, from the investigation of detailed subcellular phenomena to organ-level analysis driven by clinical data.
Patient-Specific Modelling
Creation of computational models that simulate the structure and function of an individual's heart based on their specific anatomical and physiological characteristics. It involves using medical imaging data, such as magnetic resonance imaging (MRI) or computed tomography (CT) scans, to construct a three-dimensional representation of the patient's heart. With the patient-specific anatomical and physiological data, computational algorithms and numerical methods are employed to simulate the behavior of the heart. These simulations can provide insights into various aspects of cardiac function, such as blood flow patterns, wall stresses, electrical activity, and response to therapies.
Data Driven Models
Development of computational models based on data obtained from various sources, such as medical records, clinical measurements, or experimental observations.
Instead of relying on detailed knowledge of the underlying physiological processes, data-driven models extract patterns and relationships directly from the available data to predict or simulate cardiac behaviour. Data-driven modelling of the heart can offer several advantages, such as the ability to capture complex interactions and patterns that may not be explicitly understood or accounted for in mechanistic models. It can also leverage large datasets to uncover novel insights or identify hidden relationships.
Key partners
Selected projects
Computational Physiology selected projects include the development of robust and accurate simulators for the electrical and mechanical behaviour of heart tissue, flow models of the cardiovascular system and fluid-structure interactions, as well as investigation of the role of cellular and tissue structure and function in excitable tissue.