Abstract: In this talk we look at the combinatorics and statistics of patterns that computational biologists discover at different levels in biological data be it nucleic acid sequence, microarray data or other formal structures. Is there a commonality that runs across these various domains? Can we apply the lessons learned in one domain in another? In this talk we focus on an interesting class of patterns called permutation patterns. We apply these mathematical structures to some problems arising naturally in the area of computational biology such as the problem of common gene clusters across species, phylogeny within populations, and the task of modeling complex control of transcriptions via motifs. In each of these cases we identify the underlying mathematical problems and show some promising results of applying the proposed solutions to biological data. We also discuss the problem of formulating and computing the statistical significance of the permutations motifs in the different domains.