The speadsheet contains the original micropipette aspiration measurement of red blood cell membrane.  There are five days of experiment each on its own tab, the first three of which are reported in the manuscript.  The MatLab code used to generate theoretical predictions based on our new microstructurebased constitutive model are also included.   This is the code used to generate least squared regression values for the material coefficients.

The projection length of a portion of the red cell membrane aspirated into a micropipette was measured as a function of the aspiration pressure.  Measurements were obtained on five different days using large (~0.85 mm inside diameter) and small (~0.56 mm inside diameter) micropipettes.  Cells were obtained from human donors by simple finger stick under a protocol approved by the Office of Human Subject Protection at the University of Rochester.  Cells were suspended in phosphate buffered saline with 4% v/v of fetal bovine serum to avoid attachments between the cells and glass surfaces.  Micropipettes were formed from glass capillaries using a Kopf microelectrode puller and a custom-made microforge.  Micropipettes were filled with buffer matching the cell suspension buffer by capillarity, then connected via a continuous, bubble-free water pathway to a water-filled manometer. Zero pressure was set by adjusting the height of the manometer reservoir until suspended particles were motionless in the pipette lumen.  Aspiration pressures were applied by adjusting the height of the reservoir using a micrometer with a resolution of 0.01 mm.  Cells were observed using an inverted microscope with a 100x oil immersion objective and monochromatic illumination (480 nm).  The experiments were recorded using standard video and the length of the projection as a function of aspiration pressure was measured from the recordings.  For each day of experiments approximately eight cells were measured using the same pressure sequence, and the projection lengths at each pressure were averaged.  There was some variability in the pressures applied for different cells, so the exact number of points used to construct average values and standard deviations varies slightly from experiment to experiment.  This is evident from the spreadsheets.

For the MatLab code, the main script file for performing least squared regressions is DoMuMatchFitLSQRd.  The DoOnceThrough series of scripts was used to generate individual figures.  b_vs_Lmax_fit fits the best fit pairs of values for different values of smax to a functinal relationship defined in Fig. 7 of the paper.  Code for generating individual figures is indicated by the figure number.