SBIR Phase I: Computational Tools for Analyzing the Dynamics of Biological Processes

SBIR 第一阶段：用于分析生物过程动力学的计算工具

• 批准号: 1621065
• 负责人: Anastasia Deckard
• 金额: $ 22.49万
• 依托单位: Mimetics, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1621065&HistoricalAwards=false
• 关键词: SBIR; Phase; Computational; Tools; Analyzing

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project will be the development of software tools for the analysis of the dynamics of biological networks involved in cell-level processes. These tools will be used to increase the understanding of how living organisms respond to environmental changes and stresses. For example, the tools to be built will help to show how crop plants respond to drought stress at the level of the cellular networks that control plant growth and development. These tools also will provide information that allows commercially important biological processes, such as fermentation used in industries from food and beverage to biosynthesis of fine chemicals to pharmaceuticals, to be more accurately controlled, making them more reliable, more efficient, and more predictable. In addition, these tools will help researchers analyze potential side effects of proposed drug treatments at an early stage. The power of these computational tools may multiply the value of costly experiments and trials by speeding up development, reducing costs, and bringing the benefits of advanced biological research to society. This SBIR Phase I project proposes to develop a biological data analysis pipeline that takes times series datasets as inputs, preprocesses these to deal with noise and missing data, runs a suite of analytic tools, and integrates, interprets and visualizes the results. The objectives of this project are to build test tools to help users select features of interest in data they provide; implement additional node and edge finding algorithms to complement those already in the pipeline; develop algorithms for combining information from replicate experiments and measurements to improve the statistical power of the methods; develop modules that permit users to incorporate a wide variety of prior biological information to reduce the size of the computational space to be explored; test the full pipeline on known data to ensure accuracy and stability of results and identify computational bottlenecks; select, implement, and test visualization tools to enable users to examine probable network topologies; and apply all of the above to non-periodic systems such as fermentation data to determine modifications that may be required for this type of data.

该小企业创新研究 (SBIR) 项目更广泛的影响/商业潜力将是开发用于分析细胞级过程中涉及的生物网络动态的软件工具。 这些工具将用于增进对生物体如何应对环境变化和压力的理解。 例如，即将构建的工具将有助于展示农作物如何在控制植物生长和发育的细胞网络水平上应对干旱胁迫。 这些工具还将提供信息，使商业上重要的生物过程（例如从食品和饮料到精细化学品生物合成到药品等行业中使用的发酵）能够得到更准确的控制，使它们更可靠、更高效、更可预测。 此外，这些工具将帮助研究人员在早期阶段分析拟议药物治疗的潜在副作用。这些计算工具的强大功能可能会加速开发、降低成本并将先进生物研究的好处带给社会，从而使昂贵的实验和试验的价值倍增。该 SBIR 第一阶段项目建议开发一个生物数据分析管道，以时间序列数据集作为输入，对这些数据集进行预处理以处理噪声和缺失数据，运行一套分析工具，并对结果进行集成、解释和可视化。 该项目的目标是构建测试工具来帮助用户在他们提供的数据中选择感兴趣的特征；实施额外的节点和边缘查找算法来补充管道中已有的算法；开发结合重复实验和测量信息的算法，以提高方法的统计能力；开发模块，允许用户合并各种先前的生物信息，以减少要探索的计算空间的大小；对已知数据测试整个流程，以确保结果的准确性和稳定性并识别计算瓶颈；选择、实施和测试可视化工具，使用户能够检查可能的网络拓扑；并将上述所有内容应用于非周期性系统，例如发酵数据，以确定此类数据可能需要的修改。