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Computation 2017, 5(4), 46; doi:10.3390/computation5040046

Dynamic Data-Driven Modeling for Ex Vivo Data Analysis: Insights into Liver Transplantation and Pathobiology

1
Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
2
McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
3
Department of Surgery, Thomas E. Starzl Transplantation Institute, Pittsburgh, PA 15213, USA
*
Author to whom correspondence should be addressed.
Received: 15 October 2017 / Revised: 12 November 2017 / Accepted: 16 November 2017 / Published: 23 November 2017
(This article belongs to the Section Computational Biology)
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Abstract

Extracorporeal organ perfusion, in which organs are preserved in an isolated, ex vivo environment over an extended time-span, is a concept that has led to the development of numerous alternative preservation protocols designed to better maintain organ viability prior to transplantation. These protocols offer researchers a novel opportunity to obtain extensive sampling of isolated organs, free from systemic influences. Data-driven computational modeling is a primary means of integrating the extensive and multivariate data obtained in this fashion. In this review, we focus on the application of dynamic data-driven computational modeling to liver pathophysiology and transplantation based on data obtained from ex vivo organ perfusion. View Full-Text
Keywords: transplantation; extracorporeal organ perfusion; computational modeling transplantation; extracorporeal organ perfusion; computational modeling
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Sadowsky, D.; Abboud, A.; Cyr, A.; Vodovotz, L.; Fontes, P.; Zamora, R.; Vodovotz, Y. Dynamic Data-Driven Modeling for Ex Vivo Data Analysis: Insights into Liver Transplantation and Pathobiology. Computation 2017, 5, 46.

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