Research has shown that cells have the ability to sense and respond to changes in topographical, chemical and mechanical cues in their environment. The physical, chemical and mechanical properties of the extracellular matrix (ECM) on which cells are cultured are thus crucial in directing cellular functions in vivo. Based on these observations, in order to trigger certain cellular functions, characteristics and differentiation outside of the body, there is a need to develop biocompatible materials with tunable properties and patterns as well as engineered substrates with micro or nanopatterns for directed and controlled cell behaviour.
In order to determine the best material features (e.g. micro or nanopatterns), experimental evaluations of cell behaviour in response to the features are required. However, very few methods are currently available for the accurate modelling and prediction of cell behaviour. This, is turn, translates to extensive cell test iterations and resources required.
In this technology, a multi-component virtual cell model is developed which has the capabilities to predict the changes of cell characteristics (e.g. cell nucleus shape, direction and also chromatin conformation) for a range of cell culture substrates. The modelling data captured has been correlated with experimental cell culture outcomes and demonstrates the reliability of this model in reflecting the qualitative behaviour of mesenchymal stem cells (MSCs). This model can thus provide an efficient and fast high-throughput method of developing optimal substrates for many cellular applications, one of which is stem cell differentiation.
A multi-component cell model with features like nucleus membranes, cytoskeleton and chromatin fibers is developed in this technology. This platform has the ability to predict the cell behaviour in response to the substrate characteristics on which they are cultured. Information of the cell behaviour in response to the different substrates such as cell and nucleus shape variations and chromatin conformations can be easily tracked and captured. With adequate and proper parameters being used, the model is not only capable of qualitative prediction of the shape and conformation of the cells but can also provide quantitative results. The results predicted based on this model is also proven to correlate well with experimental outcomes and could be an efficient and fast high-throughput method of optimising parameters in the fabrication of optimal cell culture substrates.
This technology allows for a better understanding of how cells sense and respond to extracellular matrix. The mechanisms involved in shape-induced physical differentiation of stem cells can also be better understood with this predictive model. Such knowledge can then be applied to the development of optimised cell substrates in an efficient and high-throughput manner. In turn, these substrates have translational impact as it is able to easily produce target desired stem cell phenotypes. Further applications include the efficient prevention or prediction of diseases via more efficient in vitro drug screening and cell therapies.
Computational biology is the application of mathematical modelling, theoretical methods and computer aided simulation to study biological systems. With the increased need to more efficiently understand biological systems at a molecular level, the use of computational biology and its application in improving clinical outcomes is expected to grow rapidly, at a CAGR of 21.3% worldwide.