Bridging the Multiscale Gap: Identifying Cellular Parameters from Multicellular Data
Document Type
Article
Journal/Book Title
IEEE Computational Intelligence in Bioinformatics and Computational Biology
Publication Date
8-12-2015
Abstract
Multiscale models that link sub-cellular, cellular and multicellular components offer powerful insights in disease development. Such models need a realistic set of parameters to represent the physical and chemical mechanisms at the sub-cellular and cellular levels to produce high fidelity multicellular outcomes. However, determining correct values for some of the parameters is often difficult and expensive using high-throughput microfluidic approaches. This work presents an alternative approach that estimates cellular parameters from spatiotemporal data produced from bioengineered multicellular in vitro experiments. Specifically, we apply a search technique to an integrated cellular and multicellular model of retinal pigment epithelial (RPE) cells to estimate the binding rate and auto-regulation rate of vascular endothelial growth factor (VEGF). Understanding VEGF regulation is critical in treating age-related macular degeneration and many other diseases. The method successfully identifies realistic values for autoregulatory cellular parameters that reproduce the spatiotemporal in vitro experimental data.`
Recommended Citation
Baker QB, Podgorski GJ, Johnson CD, Vargis E, NS Flann+. Bridging the Multiscale Gap: Identifying Cellular Parameters from Multicellular Data. IEEE Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
