Robust Characterization of Moving Objects for Subcellular Time-lapse Assays

Award Information
Agency:
Department of Health and Human Services
Amount:
$100,000.00
Program:
SBIR
Contract:
1R43GM077774-01
Solitcitation Year:
2006
Solicitation Number:
PHS2006-2
Branch:
N/A
Award Year:
2006
Phase:
Phase I
Agency Tracking Number:
GM077774
Solicitation Topic Code:
N/A
Small Business Information
SVISION, LLC.
3633 136TH PLACE SE SUITE 300, BELLEVUE, WA, 98006
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
N/A
Principal Investigator
 SHIHJONG LEE
 (425) 450-1014
 jamesl@svisionllc.com
Business Contact
 SAMUEL ALWORTH
Phone: (425) 450-1014
Email: SAMA@SVISIONLLC.COM
Research Institution
N/A
Abstract
DESCRIPTION (provided by applicant): A new generation of microscope and fluorescent probe technologies are enabling the quantitative characterization of the dynamics of discreet proteins and organelles in living cells. Though these assays are increasingly common, the state of the art image informatics tools, which use outdated detection and tracking technologies not originally designed for the challenging subcellular environment, fail to provide adequate robustness, accuracy and automation. Consequently, quantitative kinetic analysis of such data remains largely manual; which is tedious, time consuming and subjective. The lack of an adequate subcellular time-lapse assay characterization tool has become a critical bottleneck in cell biology and disease research. We are developing a next generation microscopy image analysis product, SVCell, focusing on informatics innovation in the most challenging applications including subcellular and live cell, time-lapse analyses. We propose to extend SVCell's technology for robust, accurate and highly automated kinetic characterization of multiple subcellular moving objects in time-lapse assays. The specific aims are: 1) Complete the development of the adaptive spatio-temporal object detection of biological objects; 2) Complete the development of the robust tracking in time-lapse microscopy image sequences; and 3) Quantitatively evaluate adaptive object detection and robust tracking performance in an integrated SVCell Kinetic alpha using multiple data sets. The innovation of this proposal includes robust tracking algorithms utilizing the complete image sequence and adaptive elastic object detection. SVCell Kinetics will be one in a suite of SVCell image informatics tools that we will market to the broad life sciences community. This tool could have particular impact on HIV-1, HPV, herpes simplex, adenovirus, influenza and other viruses, as well as diabetes, cancer and neurodegenerative disease related research such as in Alzheimer's, Huntington's and Parkinson's' disease and the AIDs dimentia complex. It could also increase the efficiency of target discovery in drug development, and enhance viral vector based gene delivery systems research and production.

* information listed above is at the time of submission.

Agency Micro-sites

US Flag An Official Website of the United States Government