(496d) Using Probaility Models to Represent Process Engineering Phenomena

This presentation highlights how probabilily models have been used successfully to represent randomly varying phenomena in process engineering.  It is presented as an appropirate tribute to the late professor Reuel Shinnar one of hte earliest pioneers of probabilistic modeling in chemical engineering.

Using confocal microscopy and image analysis to quantify the size, shape, and location of integrin clusters, we observed that cells exhibit large variability in these integrin cluster properties. To describe these heterogeneous populations of clusters quantitatively, we identified appropriate probability models to characterize the size, shape, and location of integrin clusters in a population of adherent cells. We determined that integrin cluster sizes are lognormally distributed, integrin cluster eccentricities are Beta distributed, and the distances of integrin clusters from the cell center are Gamma distributed.  We estimated the parameters corresponding to these probability models based on our measurement of integrin clusters in a population of cells, and the resulting best fit probability models describe the heterogeneous populations of clusters and provide the means to create an accurate mathematical description of spatially-localized integrin signaling compartments for use in computational models.