Bulletin of Mathematical Biology

, Volume 80, Issue 4, pp 880–905 | Cite as

Mathematical Model for Glucose Dependence of the Local Renin–Angiotensin System in Podocytes

  • Minu R. Pilvankar
  • Michele A. Higgins
  • Ashlee N. Ford Versypt
Original Article

Abstract

Diabetic kidney disease (DKD) is the primary cause of kidney failure. Diabetic hyperglycemia primarily damages podocyte cells. Podocytes express a local renin–angiotensin system (RAS) that produces angiotensin II (ANG II). ANG II levels are elevated by hyperglycemia, triggering podocyte injury. Quantitative descriptions of glucose dose dependency of ANG II are scarce in the literature. For better understanding of the mechanism of glycemic injury in DKD, a mathematical model is developed to describe the glucose-stimulated local RAS in podocytes. The model of the RAS signaling pathway in podocytes tracks peptides and enzymes without explicit glucose dependence. Local and global sensitivity analyses are used to identify the key parameters to be estimated in the model. Three approaches are explored to incorporate glucose dependency through linear ramp functions for the sensitive parameters. The first approach uses inferences from literature data to estimate the parameter values, while the other approaches reduce the number of assumptions by using least-squares regression to estimate all or a subset of the parameters. Physiological parameter values and RAS peptide concentrations ranges are used to discriminate between plausible models for the glucose dose dependency. This is the first model of the theory of the local RAS mechanism specific to podocyte cells to track ANG II levels in a range of glycemic conditions that may contribute to podocyte damage in DKD. The ability to track ANG II behavior could enable prediction of its downstream effects on podocytes and provide opportunities to better characterize pathophysiological features of DKD progression.

Graphical Abstract

Keywords

Diabetic kidney disease Parameter estimation Microvasculature Chronic kidney disease 

Notes

Acknowledgements

This work supported in part by an award from the Harold Hamm Diabetes Center at the University of Oklahoma Health Sciences Center.

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Copyright information

© Society for Mathematical Biology 2018

Authors and Affiliations

  1. 1.School of Chemical EngineeringOklahoma State UniversityStillwaterUSA
  2. 2.Harold Hamm Diabetes CenterUniversity of Oklahoma Health Sciences CenterOklahoma CityUSA

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