Modeling and optimization of high-sensitivity, low-volume microfluidic-based surface immunoassays.

Microfluidics are emerging as a promising technology for miniaturizing biological assays for applications in diagnostics and research in life sciences because they enable the parallel analysis of multiple analytes with economy of samples and in short time. We have previously developed microfluidic networks for surface immunoassays where antibodies that are immobilized on one wall of a microchannel capture analytes flowing in the microchannel. This technology is capable of detecting analytes with picomolar sensitivity and from sub-microliter volume of sample within 45 min. This paper presents the theoretical modeling of these immunoassays where a finite difference algorithm is applied to delineate the role of the transport of analyte molecules in the microchannel (convection and diffusion), the kinetics of binding between the analyte and the capture antibodies, and the surface density of the capture antibody on the assay. The model shows that assays can be greatly optimized by varying the flow velocity of the solution of analyte in the microchannels. The model also shows how much the analyte-antibody binding constant and the surface density of the capture antibodies influence the performance of the assay. We then derive strategies to optimize assays toward maximal sensitivity, minimal sample volume requirement or fast performance, which we think will allow further development of microfluidic networks for immunoassay applications.