Probabilistic Approach to Reverse Engineering of Spaceflight Molecular Networks

Reverse engineering of molecular networks is one of the most challenging tasks in systems biology and bioinformatics. This is mainly due to the complex nature of biological systems which involve many factors and uncertainties. However, with the rapid biotechnological advancements, large-scale high-throughput biological data have become available. These data have enabled researchers to deduce and understand how interactions among the vast array of components in biological systems relate and affect each other. It is a general consensus among system biologists and bioinformaticians that such interactions form networks that aim at capturing the dependencies between the interacting entities. Therefore, existing literature provides a strong prior biological knowledge to conduct research in this area. However, for Space Biology there is very little research focus on the development of computational and modeling tools that enable the discovery and inference of space environment-specific molecular networks. Although, the ground-based system biology computational tools can be adapted for reverse engineering spaceflight molecular networks, they are largely inadequate to capture biophysical parameters relating to space environment. Thus, the goal of this research is to develop an approach for reconstructing, inferring and analyzing spaceflight molecular networks using data from the NASA GeneLab data system. In addition, we adapt our preliminary probabilistic graphical model tool to validate and quantify how well the inferred networks explain the observed experimental data.

Intellectual merit: The proposed work seeks to grow a research area crossing frontiers in probabilistic methods, graph theory, algorithms, biology and data science. Focusing on the reconstruction, inference and analysis of spaceflight molecular networks for which very little literature exists, the work has the potential to advance knowledge and understanding across several related fields. Furthermore, it provides an opportunity to translate decades of biological data from experiments performed in space environments into information that foster innovation in Space Biology. Specifically, the significance of the proposed activity is as follows:

• Developing computational tools for the discovery of biological networks influenced by space conditions. • Establishing mathematical framework for the analysis and validation of spaceflight biological networks. • Developing algorithmic tools for generating biologically realistic spaceflight expression data. • Predicting biochemical and kinetic parameters of biological processes in spaceflight.

Broader impacts: The PI believes that the proposed work has far-reaching implications on basic science, education and technology that will benefit society, in general. The broader impacts include: 1. Advances understanding of key biological mechanisms that enable biological organisms to thrive in space environments, 2. Training of graduate students in bioinformatic data analysis, and 3. Integration of research results in the classes taught by the PI and to improve the curriculum at Wichita State University

This is a mature technology (TRL 7+) — the realistic path in is usually NASA's Technology Transfer Program: licensing an existing NASA patent, or a Space Act Agreement to use NASA facilities/expertise directly. NASA also runs a startup licensing program with no upfront fee for companies formed to commercialize a specific NASA technology.

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