Abstract
Our lab uses computer-aided design to build in silico libraries of surface topographies, which we reproduce on polymeric chips and analyse for cellular responses using high content imaging and machine learning. In addition, we use transcriptomics and mass spectrometry to obtain a holistic view of biomaterial-mediated cellular responses and build gene regulatory networks thereof. This approach enables us to parameterize both the biomaterial properties as well as the cell response and to correlate them using computational tools. We think that this approach can be translated to other biomaterial platforms, such as polymer arrays, and foresee large scale crosstalk between them if we can standardize our methodology to describe the materials and to analyse the cells. To this end, we have started cBIT, the compendium for biomaterial-induced transcriptomics, an open-source database in which scientists can deposit and search material-induced transcriptomics data. The meta-analyses that cBIT enables, could lead to the identification of genes, pathways or expression profiles that can inform the design and development of new biomaterials. As such, by generating new information and simultaneously accumulating it in cBIT, we expect it is possible to one day predict cell responses to biomaterials.
