A Bicinchoninic acid assay was performed on each lysate and the lysates were diluted such that 20 ug of protein selleck chem inhibitor lysate was used in each ELISA assay. The lysis buffer was made by combining 20 ml of PBS, 1nM of EDTA, 5 mM NaF, 6 M Urea, 0. 1 ml of Triton 100, and 2 packets on Halt protease/phosphatase inhibitors from Thermo Scientific. Briefly, the antibody pairs for the ELISA assays were optimized on 384 well ELISA plates from Santa Cruz Biotechnology using the accompanied positive control samples. An eight point standard curve was gener ated and fitted using a second order polynomial. The amount of phosphoprotein in ng per 20 ug of total protein lysate was then determined by comparing the measured absorbance of the sample to the standard curve.

Data analysis Following data acquisition, calibration to the ELISA stand ard curve, and normalization to total protein content, the data was imported into Matlab where both protein and survival data were mean centered and unit variance scaled. The data was arranged such that each column of the X matrix represented a phosphoprotein at a specific time. The rows represent Inhibitors,Modulators,Libraries the cell treatments with the values in the X matrix corresponding to phosphorylation levels and the rows of the Y matrix corresponding to relative cell sur vival in response to that treatment. The X and Y matrices were then inputted into a function which utilizes the native plsregress function packaged with Matlab to employ the SIMPLS algorithm and calculate the regression coefficients. This was repeated with each row left out.

The calculated model was applied to the left out data to determine a predicted Y value. The R2 value was Inhibitors,Modulators,Libraries then calculated using the measured and predicted survival data. Partial least squares regression is a multiple regression algorithm which attempts to explain the Y matrix Inhibitors,Modulators,Libraries by finding a multidimensional direction in the X space which explains the maximum variation in both matrices. This algo rithm is especially suited to applications Inhibitors,Modulators,Libraries where the X matrix contains many more variables than observations, or when many of the X variables are multicollinear, as is often the case in cell signaling data. An approach for calculating significance in PLS regres sion models was employed which randomizes the X matrix as compared to the Y matrix and performs regression analysis. From this randomized regression a R2 is calculated Inhibitors,Modulators,Libraries and saved.

We repeated this procedure 3,000 times and determined a mean R2 and standard deviation for these calculated ran dom models. The randomized R2 selleck values were assumed to follow a normal distribution. Using the mean and standard deviation from the R2 values calculated for randomized regression, and the R2 of the correctly calculated model, the number of standard deviations away from the random mean was determined, and from this a p value determined.