Combinatorial augmentation for a multi-pathogen biosensor: signal analysis and design

Recent advances in combinatorial chemistries have revolutionized approaches to drug candidate synthesis and screening. Combinatorial approaches are also beginning to be used to increase the performance of diagnostic devices for both clinical and field uses. The use of combinatorial technologies is motivated by a general desire to detect as many different pathogens using the smallest, most inexpensive and fastest system possible. We examine the potential for rational design approaches to enhance the performance and miniaturization of biosensors. We describe novel combinatorial biosensor systems, in addition to mathematical frameworks for their optimization and performance prediction. The biosensors are assumed to be composed of multiple detection channels with the following characteristics. Each channel has a single output and can be dynamically set to respond to some or all of a set of pathogens. Regardless of the number of pathogens detected, however, there is a single numerical output from a channel. We evaluate the amount of ambiguity of positive signals produced as a result of increasing both the number of channels and the number of pathogens detected per channel and the effect this ambiguity has on system performance. We further discuss strategies for disambiguating positive signals. Finally we cite specific biosensor configurations that exploit the findings above and compare them to “brute force” approaches. Overall we suggest the approach we refer to as “n-squared” to simultaneously optimize device cost, speed and reagent usage.