Analysis of the Spatio-Temporal Dynamics of Infection
| dc.contributor.author | Stolley, Danielle L. | |
| dc.contributor.author | May, Elebeoba E. | |
| dc.date.accessioned | 2018-02-23T19:36:23Z | |
| dc.date.available | 2018-02-23T19:36:23Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | Spatio-temporal dynamics are vital in understanding the course of infection, particularly for infections that lead to the formation of granulomas such as Mycobacterium tuberculosis which significantly impact the course of infection. In in vitro studies, the observable data is gathered at the global environment level (a single well), but this lacks the correlation and relationship between an individual cell, its local neighborhood and its global environment. Traditional 2D models of infection allow for easily replication and rapid sampling but, devoid of an extracellular matrix (ECM) are unable to fully replicate the spatial dynamics of an in vivo system. In vivo models, while providing multi-cellular response and spatial dynamics do not allow the freedom of sampling granted in vitro. We aim to develop corresponding in vitro and in silico platforms to adequately capture and analyze the multidimensional nature of immune response to infection. By connecting the in vitro and in silico platforms with confocal imaging, we are able to observe, quantify, and correlate cellular behaviors on all levels and determine the characterizes that lead to different outcomes of infection. | |
| dc.description.department | Biomedical Engineering, Department of | |
| dc.identifier.uri | http://hdl.handle.net/10657/2338 | |
| dc.language.iso | en_US | |
| dc.title | Analysis of the Spatio-Temporal Dynamics of Infection | |
| dc.type | Poster |