To provide kinome array datasets for which correct analysis results are known, we can generate synthetic kinome array datasets. Synthetic data are invaluable in evaluation and assessment of various systems, algorithms and scientific methodologies. Synthetic data sets make it possible to meet specific needs or certain conditions that may not be found in the original, real data. The main criticism of synthetic data is that it may be oversimplified or biased in a way that does not preserve characteristics of actual or original data. In this paper, we propose an algorithm to generate synthetic kinome array data that relies on actual intensity measurements from kinome microarray experiments to preserve subtle characteristics of the original kinome microarray data. One of these characteristics is within-array technical replicate variability. As will be shown, measurements for within-array technical replicates in the synthesized data have the same distribution as that of data from actual kinome arrays.