Adaptive Supramolecular Networks: Emergent Sensing from Complex Systems

Abstract

Molecular differentiation by supramolecular sensors is typically achieved through sensor arrays, relying on the pattern recognition responses of large panels of isolated sensing elements. Here we report a new one-pot systems chemistry approach to differential sensing in biological solutions. We constructed an adaptive network of three cross-assembling sensor elements with diverse analyte-binding and photophysical properties. This robust sensing approach exploits complex interconnected sensor-sensor and sensor-analyte equilibria, producing emergent supramolecular and photophysical responses unique to each analyte. We characterize the basic mechanisms by which an adaptive network responds to analytes. The inherently data-rich responses of an adaptive network discriminate among very closely related proteins and protein mixtures without relying on designed protein recognition elements. We show that a single adaptive sensing solution provides better analyte discrimination using fewer response observations than a sensor array built from the same components. We also show the network's ability to adapt and respond to changing biological solutions over time.