Towards a Physiological Scale of Vocal Fold Agent-Based Models of Surgical Injury and Repair: Sensitivity Analysis, Calibration and Verification.

Agent based models (ABM) were developed to numerically simulate the biological response to surgical vocal fold injury and repair at the physiological level. This study aimed to improve the representation of existing ABM through a combination of empirical and computational experiments. Empirical data of vocal fold cell populations including neutrophils, macrophages and fibroblasts were obtained using flow cytometry up to four weeks following surgical injury. Random Forests were used as a sensitivity analysis method to identify model parameters that were most influential to ABM outputs. Statistical Parameter Optimization Tool for Python was used to calibrate those parameter values to match the ABM-simulation data with the corresponding empirical data from Day 1 to Day 5 following surgery. Model performance was evaluated by verifying if the empirical data fell within the 95% confidence intervals of ABM outputs of cell quantities at Day 7, Week 2 and Week 4. For Day 7, all empirical data were within the ABM output ranges. The trends of ABM-simulated cell populations were also qualitatively comparable to those of the empirical data beyond Day 7. Exact values, however, fell outside of the 95% statistical confidence intervals. Parameters related to fibroblast proliferation were indicative to the ABM-simulation of fibroblast dynamics in final stages of wound healing.

Quantitative analysis of F-actin redistribution in astrocytoma cells treated with candidate pharmaceuticals.

Actin fibers (F-actin) control the shape and internal organization of cells, and generate force. It has been long appreciated that these functions are tightly coupled, and in some cases drive cell behavior and cell fate. The distribution and dynamics of F-actin is different in cancer versus normal cells and in response to small molecules, including actin-targeting natural products and anticancer drugs. Therefore, quantifying actin structural changes from high resolution fluorescence micrographs is necessary for further understanding actin cytoskeleton dynamics and phenotypic consequences of drug interactions on cells. We applied an artificial neural network algorithm, which used image intensity and anisotropy measurements, to quantitatively classify F-actin subcellular features into actin along the edges of cells, actin at the protrusions of cells, internal fibers and punctate signals. The algorithm measured significant increase in F-actin at cell edges with concomitant decrease in internal punctate actin in astrocytoma cells lacking functional neurofibromin and p53 when treated with three structurally-distinct anticancer small molecules: OSW1, Schweinfurthin A (SA) and a synthetic marine compound 23'-dehydroxycephalostatin 1. Distinctly different changes were measured in cells treated with the actin inhibitor cytochalasin B. These measurements support published reports that SA acts on F-actin in NF1(-/-) neurofibromin deficient cancer cells through changes in Rho signaling. Quantitative pattern analysis of cells has wide applications for understanding mechanisms of small molecules, because many anti-cancer drugs directly or indirectly target cytoskeletal proteins. Furthermore, quantitative information about the actin cytoskeleton may make it possible to further understand cell fate decisions using mathematically testable models.

Quantifying the astrocytoma cell response to candidate pharmaceutical from F-ACTIN image analysis.

The distribution, directionality and motility of the actin fibers control cell shape, affect cell function and are different in cancer versus normal cells. Quantification of actin structural changes is important for further understanding differences between cell types and for elucidation of the effects and dynamics of drug interactions. We have developed an image analysis framework for quantifying F-actin organization patterns in confocal microscope images in response to different candidate pharmaceutical treatments. The main problem solved was to determine which quantitative features to compute from the images that both capture the visually-observed F-actin patterns and correlate with predicted biological outcomes. The resultant numerical features were effective to quantitatively profile the changes in the spatial distribution of F-actin and facilitate the comparison of different pharmaceuticals. The validation for the segmentation was done through visual inspection and correlation to expected biological outcomes. This is the first study quantifying different structural formations of the same protein in intact cells. Preliminary results show uniquely significant increases in cortical F-actin to stress fiber ratio for increasing doses of OSW-1 and Schweinfurthin A(SA) and a less marked increase for cephalostatin 1 derivative (ceph). This increase was not observed for the actin inhibitors: cytochalasin B (cytoB) and Y-27632 (Y). Ongoing studies are further validating the algorithms, elucidating the underlying molecular pathways and will utilize the algorithms for understanding the kinetics of the F-actin changes. Since many anti-cancer drugs target the cytoskeleton, we believe that the quantitative image analysis method reported here will have broad applications to understanding the mechanisms of action of candidate pharmaceuticals.