SBIR Phase I: Tracking Algorithms for Uncovering Complex Dynamic Interactions in Live Cells

SBIR 第一阶段：揭示活细胞中复杂动态相互作用的跟踪算法

• 批准号: 1314897
• 负责人: Christopher Calderon
• 金额: $ 15万
• 依托单位: Numerica Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1314897&HistoricalAwards=false
• 关键词: SBIR; Phase; Tracking; Algorithms; Uncovering

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.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.

这个小型企业创新研究计划（SBIR）I期项目将开发算法和原型软件，以跟踪和量化活细胞中单生物分子所经历的力。该方法建立在时间序列分析的最新进展基础上：（1）拟合准确表征基础粒子动力学的2-D/3-D随机微分方程（将提供针对实验数据检查模型的方法）； （2）通过从目标跟踪应用程序扩展最新的算法，从拥挤和嘈杂的图像序列中形成可靠的轨道。当前的单粒子跟踪软件中不存在此类分析工具。由于系统与生物物理学，细胞信号传导和癌症研究的相关性，该工具将用于提取小鼠细胞原发性纤毛中蛋白质运动的新动力学信息。该算法和软件将通过独特的拟合度假设测试指标提供更准确的动力学参数估计。该项目的更广泛的影响/商业潜力将是开发能够通过促进新型信息的细胞来开发对细胞和合成生物系统的强大洞察力的新软件工具。这些见解将对众多研究领域产生积极影响，从增强药物输送到合成生物学应用中的收率提高。该商业软件将使具有各种背景的研究人员能够：（1）从大图像序列中产生可靠的轨道（用指标描述了候选曲目的质量）； （2）提取潜在的动力学信息； （3）测试标准生物物理模型背后的假设。这项工作开发的算法和软件将成为独特的商业产品产品的基础，该产品将满足生物成像市场中未满足的需求。