tracked for each EGFP-Paxillin cells, 8145 adhesions for each EGFP-PaxillinS178A cells and 5184 adhesions for each EGFP- FAK cells. The differences in the average number of adhesions are due to longer experiments in the EGFP-PaxillinS178A and EGFP- FAK data sets. Representative videos are included in Supporting Information (Videos S1, S2, S3). Calculating Assembly and Disassembly Rates With the adhesions tracked through each experiment, the characteristic properties determined for each adhesion in each frame of the time-lapse movie are collected into a set of data time series representing the properties of each adhesion through time. One type of time series follows the mean intensity of Paxillin through time, making it possible to estimate the rates of assembly and disassembly of Paxillin for each adhesion. An automated method to estimate the rates of assembly and disassembly was developed. This program automatically fits linear models to the log-transformed time series of Paxillin intensity values for both the assembly and disassembly phases of the FA life cycle. A log-linear fitting method was adapted and extended to allow for the automated determination of assembly and disassembly phase lengths [7]. Briefly, log-linear models are fit to all the possible assembly and disassembly phases greater than or equal to a user specified minimum length. The assembly phase is assumed to occur at the beginning of the time series, whereas the disassembly phase is assumed to end with the last point in the time series. Each of the fits collected were normalized by either the first (assembly rate calculations) or last point (disassembly rate calculations) in the time series and log-transformed, as described [7]. In the second part of the algorithm, the optimum lengths of the assembly and disassembly phases were determined via a search for the maximum sum of adjusted R2 values of the model fits. It was assumed that the assembly and disassembly phases did not overlap. In the rare cases where there are multiple combinations of assembly and disassembly phase lengths that produce the highest sum of adjusted R2 values, the combination with the longest combined assembly and disassembly phase lengths is selected. The stability/maturity period was then defined as the length of time between the assembly and disassembly periods. Results Filtering Several filters are used to analyze the data sets collected with these analysis methods. When determining the assembly and disassembly rates, only adhesions with at least 20 Paxillin intensity time points were analyzed. This ensured that there was sufficient data available to correctly detect the assembly and disassembly rates. Adhesions whose birth was the result of a split event with another adhesion were also excluded from the assembly rate calculations, while adhesions whose lineage ended with a merge event were excluded from the disassembly rate calculation. Assembly and disassembly fits whose linear model p-values were above 0.05, indicating that the slope of the linear model was not significantly different from zero, were also excluded from the data set. A separate set of filters was used to determine the length of each phase (assembly, stability and disassembly) in the adhesion intensity time series data. In order to estimate the length of time an adhesion spends in the stability phase, we required that both the assembly and disassembly phases be observed. In addition, the adhesion birth could not have been the result of a split event and the death of the adhesion not the result of a merge. The filter also excluded those adhesions where the p-value of either the assembly or disassembly linear model was greater than 0.05. Quantifying Focal Adhesion Dynamics Parameter Testing To test the sensitivity of results on parameters used for defining the threshold for adhesion detection, the minimum length of the assembly and disassembly phases and the rate of image sampling, we re-executed our analysis while varying these parameters. The threshold for adhesion detection was varied between 0.05 and 0.10, with no significant effect on the percentage change between the wild-type cells and the S178A mutant cells in either the assembly or disassembly rates (Figure S5 and Figure S6). Varying the required length for assembly and disassembly rate calculation similarly had no significant effect on percentage change between the wild-type and S178A mutant cells of the rates of assembly or disassembly (Figure S7 and Figure S8). Finally, we tested the results of changing the image sampling rate by discarding every other collected image in the same set of experiments (Figure S9). Discarding half of the images did not significantly affect the assembly or disassembly rates, but did have a slight effect on the distribution of the adjusted R2 values (Figure S10). From these parameter testing examples, we concluded that selection of a single set of parameters as determined by the user, provided a robust description for any of the differences between cell lines in terms of assembly and disassembly rates. Software Testing In order to test the baseline performance of the algorithms, a set of gold standard images were produced with sets of FAs having specific, predefined properties. In general, validation tests consisted of simulating a time-lapse microscope field of view that mimicked the observed properties of the adhesions (Figure S11A). Since our results are consistent with prior findings based on manual methods of adhesion identification, the simulated range of properties was set to be similar to those observed in the experimental data. For all simulated experiments, a Gaussian noise model (mean 0, variance of 2*1023) was used as a background to simulate the cell environment. These parameters were chosen as they produced distributions of short-lived adhesions that were empirically similar to those observed experimentally. Also, all simulated adhesions were circular and the same background noise model was used to perturb intensities assigned by the software to each simulated adhesion. Three types of simulations were conducted: stationary, moving and kinetic. The stationary simulation consisted of simulating a field of view that included rows and columns of unmoving adhesions. The intensity of the adhesions were varied along the columns between mean intensities of 0.05 and 0.47 (95% of the detected adhesions in the experimental data fall between normalized average Paxillin intensities of 0.21 and 0.52). Ten different adhesion radii were simulated along the rows, varying between 0.5 and 5 pixels. The adhesions at low mean intensity values were not reliably discernable below intensity level 0.17. Adhesions above this level were readily detected with both the predicted intensities and sizes (Figure S11). The moving simulation was designed to probe the tracking algorithm’s performance in following adhesions of various sizes and intensities. The simulation consisted of sliding the adhesions across the field of view at different rates (Figure S12A). As expected, the smaller adhesions were more difficult to track, with a nearly linear relationship between the ability to track an adhesion moving at a certain rate and its corresponding radius (Figure S12B). As long as the adhesion is detectable, there does not appear to be any differences in the intensity versus tracking accuracy (data not shown). To conduct the adhesion kinetics tests, sets of adhesions were simulated that went through logarithmic assembly and disassembly PLoS ONE | www.plosone.org 11 July 2011 | Volume 6 | Issue 7 | e22025