Lagrangian detection of biomolecular interactions

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DESCRIPTION (provided by applicant): Patterns of gene expression and protein profiles are currently revolutionizing the understanding of biology and disease and are likely to become diagnostic and prognostic tools. Once these patterns are established, methods to implement them will be required. We have designed a platform which rapidly matches the characteristics of an unknown sample with established expression or protein profiles. The approach is characterized by computer controlled testing for pattern features with adaptive feedback to increase sensitivity and reliability. It will have greatest utility for identifying established patterns described in terms of the presence or concentration of 100s to 1000s of key molecular structures. Current high-throughput methods for profiling of an unknown sample, such as DNA microarrays for gene expression profiling, can be characterized as Eulerian design - known capture probes are fixed in space by attachment to a solid substrate and hybridized by target diffusion. Biorecognition is inefficient querying only a fraction of the target solution, target-probe interactions occur slowly, and target-probe binding occurs under uniform conditions. This application describes an alternative Lagrangian design - probes are arranged on a filament and moved through nanoliter reaction zones for biorecognition testing, processing, and analysis. The extremely small dimensions enable complete, rapid, and complete target-probe interactions. The flexible and adaptive features permit tailoring reaction conditions to each capture probe-target interaction. We have identified several key components that are critical to the success of this new platform technology. These are the identification of an appropriate fiber, deposition of probe, development of the small volume reaction compartment with immiscible surface tension valves, and a specialized apparatus for computer controlled fiber transport through multiple reaction chambers. Although we have limited experimental data, the data we do have, suggests that each of the key components of the proposed system is feasible. This technology will provide a new tool for the identification of patterns present in gene expression or protein profiles enabling these profiles to be fully utilized for diagnostic and prognostic purposes.