NSF Award
1314897
This Small Business Innovation Research Program (SBIR) Phase I project will develop algorithms and prototype software to track and quantify forces experienced by single-biomolecules in living cells. The approach is built on recent advances in time series analysis to: (1) fit 2-D/3-D stochastic differential equations that accurately characterize the underlying particle kinetics (methods for checking models against experimental data will be provided); and, (2) form reliable tracks from crowded and noisy image sequences containing many molecules by extending state-of-the-art algorithms from target tracking applications. Such analysis tools do not exist in current single particle tracking software. The tools will be utilized to extract new kinetic information on protein motion in the primary cilium of mouse cells due to the system's relevance in biophysics, cell signaling, and cancer research. The algorithms and software will provide more accurate estimates of kinetic parameters with the unique addition of goodness-of-fit hypothesis testing metrics.<br/><br/>The broader impact/commercial potential of this project will be the development of new software tools capable of producing powerful insights into cellular and synthetic biological systems by enabling the extraction of novel information characterizing bimolecular motion in live cells. Such insights will positively influence numerous research areas ranging from enhanced drug delivery to improved yields in synthetic biology applications. The commercial software will enable researchers with various backgrounds to: (1) produce reliable tracks from large image sequences (with metrics describing the quality of the candidate tracks); (2) extract the underlying kinetic information; and, (3) test the assumptions behind standard biophysical models. The algorithms and software developed by this effort will serve as the basis for a unique commercial product offering that will meet unmet demand in the biological imaging market.
