Abstract

Mathematical modeling has been useful in studies of the progression of type II diabetes. However, not many studies have been done using electric cell‐substrate impedance sensing (ECIS) technology to validate mathematical modeling in bone cells exposed to high concentrations of glucose. In this study we hypothesized that ECIS technology can be used as a predictive tool to evaluate the effects of glucose on cell membrane impedance in UMR cell cultures by mathematical analysis through predictive mathematical modeling. Briefly, UMR cells were plated at 2.5 × 105 cells/mL in eight‐well (8W) ECIS array plates. Designated wells were treated with different concentrations of glucose (10, 5, and 3 mM). Cell cultures were incubated in a humidified chamber at 37° C with infusion of 5% CO2. Membrane resistance, time of attachment and rate of spreading of the cells were monitored and measured in vitro, using the ECIS machine. Based on the data generated by the ECIS machine, a mathematical formula was derived to predict the resistance of the UMR cell membrane for any concentration of glucose used. The formula was tested based on the generated data. F test ANOVA was employed to compare variances between all experimental trials; all values in the results are expressed as means ±SD. The results show that the highest concentration of glucose caused a decreased time of attachment, rate of spreading and an increased cell membrane resistance in the UMR cells. The following formulas were derived based on the ECIS data: dR/dt=kR0ekt and R[G]=R0e+sG (where dR/dt=rate of spreading, k=rate constant, R0=initial resistance, t=time, [G]=glucose concentration, and s=rate constant (1/[glucose]). The formulas were found to be of predictive value. The results suggest that the ECIS technology is a potentially useful tool in the mathematical analysis of physiological phenomena in bone‐like cells in vitro.Support or Funding InformationThis work was supported by the Ryckman Endowment Student Research Fund of the Biology Department at La Sierra University in Riverside, CA.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

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