Selective tuning of the sizes, surface densities, and chemistries of 10-nanometer scale heterogeneities on planar surfaces that interact with colloidal objects in shear flow allows control of the adhesion of the colloidal particles. Motivated by the need to develop artificial pattern recognition constructs for microfluidic sensor applications, systems have been designed that exhibit tunable dynamic behavior on renewable surfaces, which allows colloidal objects to be distinguished by their characteristic adhesion signatures and rates. Adhesion is reversible in a substantial portion of parameter space, and surface features can give rise to particle skipping and rolling on the surfaces. These dynamics are captured by a new model that incorporates both hydrodynamic forces on the particles and the spatially varying physicochemical interactions between the particles and heterogeneous surfaces. The threshold for adhesion is governed by the matching of the length scales of the patch spacing and the interactive surface area between the particle and surface, which is reminiscent of pattern recognition, although the patch distribution on the collector is random. Spatial fluctuations in the patch density are shown to play a critical role in the dynamic adhesion and rolling behavior (e.g., allowing adhesion on a net-repulsive surface), which prevents this behavior from being predicted by a mean-field approach. This talk highlights important developments in the experimental and modeling efforts and focuses on the fundamental dynamics of particle interaction with these sensing surface features in shear flow at low Reynolds number.