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Front Immunol. 2015 Aug 3;6:385. doi: 10.3389/fimmu.2015.00385. eCollection 2015.

Computational Biology: Modeling Chronic Renal Allograft Injury.

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Department of Surgery, von Liebig Transplant Center, Mayo Clinic , Rochester, MN , USA.
Queen Elizabeth Hospital Centre, Nephrology , Birmingham , UK.


New approaches are needed to develop more effective interventions to prevent long-term rejection of organ allografts. Computational biology provides a powerful tool to assess the large amount of complex data that is generated in longitudinal studies in this area. This manuscript outlines how our two groups are using mathematical modeling to analyze predictors of graft loss using both clinical and experimental data and how we plan to expand this approach to investigate specific mechanisms of chronic renal allograft injury.


chronic renal allograft dysfunction; computational biology; immunology; mathematical modeling; renal transplantation

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