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

这个小企业创新研究（SBIR）项目的更广泛的影响/商业潜力将是开发软件工具，用于分析细胞级过程中所涉及的生物网络的动态。 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了解。 例如，将要建立的工具将有助于显示作物如何在控制植物生长和发育的细胞网络水平上对干旱胁迫作出反应。 这些工具还将提供信息，允许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制药行业中使用的发酵，以更准确地控制，使其更可靠，更有效，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。这些计算工具的力量可以通过加速开发，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成倍增加。SBIR第一阶段项目提出开发一个生物数据分析管道，该管道将时间序列数据集作为输入，对这些数据进行预处理以处理噪声和缺失数据，运行一套分析工具，并集成，解释和可视化结果。 该项目的目标是建立测试工具，帮助用户选择他们提供的数据中感兴趣的特征;实施更多的节点和边缘查找算法，以补充已经在管道中的算法;开发算法，用于合并来自重复实验和测量的信息，以提高这些方法的统计能力;开发模块，允许用户结合各种各样的先前生物信息，以减少要探索的计算空间的大小;在已知数据上测试整个管道，以确保结果的准确性和稳定性，并确定计算瓶颈;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;并将上述所有应用于非周期性系统，例如发酵数据，以确定这种类型的数据可能需要的修改。

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.