A computational framework to investigate the mechanical role of the extracellular matrix in tissue development and disease

Lead Research Organisation: University College London
Department Name: Lab for Molecular Cell Bio MRC-UCL


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.

Technical Summary

Most tissues are surrounded by the extracellular matrix (ECM), a complex dynamic protein meshwork that acts as a physical scaffold for the tissue cells to adhere to, as well as providing a platform for biochemical and mechanical cues. The ECM plays an important role during development and throughout adult life. Dysregulation in ECM composition and mechanical properties can lead to developmental defects and pathological conditions in infants and adults. So far, most of this insight has come from animal studies. However, these studies are limited in capturing changes in the ECM in vivo, requiring sacrifice of many animals to study a single developmental process or disease progression. A potential solution to replace animal models is use of organoids, where a step-by-step understanding of ECM structure and mechanics, and their effect on tissue growth and function can be obtained. However, most of the current methods rely on animal-derived matrices for organoid culture, where the poorly-defined composition of these matrices affects reproducibility and hinders their translational potential. Recently, a number of techniques have been developed to replace animal-derived matrices with controllable synthetic matrices. However, the protocols are yet to be optimised for different organoids.

I propose to replace animal use by developing a computational platform of tissue growth and function with explicit implementation of ECM mechanics. The model will be developed with a Graphic User Interface, making it accessible to researchers with limited computational skills. Researchers will be able to computationally test the effect of different variables on tissue growth and function, replacing the use of animals in hypothesis-testing experiments. The model can also be used to find the optimal synthetic matrix properties for organoid growth in silico, which can be fine-tuned in vivo. This will eventually help to fully replace animal-derived matrices with their synthetic counterparts.


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