The tissues in the body are lined by the extracellular matrix (ECM), a meshwork of proteins that acts as a physical support and provides them with biochemical and mechanical cues. Cells have the ability to change the composition and mechanical properties of the ECM through synthesis, degradation and rearrangement of its components. These changes are crucial for developing flat sheets of tissues into complex 3-dimensional structures. In adult life, correct ECM composition is vital for maintaining tissue shape and ensuring its correct function. Dysregulation in ECM composition and mechanical properties, either during development or later in life and as a result of disorders such as diabetes, can lead to severe pathological condition, such as kidney failure, hearing loss and blindness.
So far, most of the knowledge about the role of ECM in tissue growth and function has come from animal experiments. However, it is still not possible to follow live changes in ECM structure and composition in the same animal. Therefore, a single study looking at these changes, either through a developmental process or during disease progression, requires sacrifice of many animals. Recently, scientists have been trying to grow organ-like tissues (i.e. organoids) in the lab. These organoids have high clinical potential and can replace animal research in the fields of tissue development and disease. However, majority of current organoid culture techniques rely on the use of extracellular matrix scaffolds derived from animals, which affects their reproducibility and hinders their translation into clinics. Scientists are working to replace these animal-derived matrices with synthetic ones, a process that requires large amount of time and resources.
I propose to develop a multiscale computational platform of tissue growth and function, with explicit implementation of the extracellular matrix mechanics. This will allow researchers to model the growth of their tissue of interest, test the effect of different conditions on its growth and function, and design a minimum set of experiments to carry out in the lab, therefore replacing many animal experiments with computer simulations. The model will also allow researchers to simulate growth of organoids in synthetic matrices with different mechanical properties, and identify the optimal properties that can be further fine-tuned experimentally. This will significantly enhance protocol optimisation steps, allowing researchers to easily tailor-design synthetic ECM matrices for growth of different organoids, and eventually fully replace animal matrices with their synthetic counterparts. Finally, the open-source nature of our model will also allow researchers to incorporate their data into the model to capture more sophisticated processes, and therefore significantly reduce animal use.