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

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.