Grant: Canada Research Chairs

Understanding cellular life demands an interdisciplinary approach as eloquently expressed by mathematician Alan Turing who explained how the well-known physical laws, including Newton’s laws, are critical for living systems. From the physical perspective, cells are made of the same atoms and molecules as what we call regular matter. Cells also operate under non-equilibrium conditions. It is established that mechanical forces play a key role in cellular phenomena from the molecular level up to cancer cell metastasis. Local stresses and tensions influence the motions and shapes of molecules in cells and interactions between cells. This enables cells to communicate with each other and induce motions, such as durotaxis, and thus self-organization. Although a plethora of models exist describing individual mechanobiological phenomena, there is no comprehensive theoretical or computational model. Such models are vital for understanding the controlling physical mechanisms under different conditions, and making accurate predictions of the behaviours of cells and cellular aggregates. It is hard to overemphasize the need for better multiscale models as they are critical for improving human life, e.g., understanding cell-cell communication, developing synthetic biology and understanding how cancers grow and spread. The next terrain of cancer treatment is targeting the mechanical properties of cells. This proposal aims at such targets. Here, we will build the first coherent multiscale framework for mathematical and computational modeling of cells and cellular dynamics. This will cover microscopic properties arising from linker and junction proteins, membranes and the cytoskeleton, up to cell-cell chemical signalling and mechanical forces that influence cellular behaviour across length scales. This will be achieved through development and integration of efficient and accurate computational techniques and their inter-linking that combines microscopic molecular level description with mechanistic modeling and reaction-diffusion modeling. New simulation methods and methodologies will be developed, including state-of-the-art machine learning and high-performance computing methods. The immediate applications include radiation therapy and cancer cell dynamics. Importantly, the computational and machine learning methods can also be applied to many other soft (and even hard) materials influencing even more scientific discoveries. All software will be released as open source.