Unexpected Correlations in Biological Networks

Oregon State University, Corvallis OR

Investigators

Abstract

As a result of rapid growth in the field of molecular biology, recent technological advances enable measuring the expression of thousands of genes simultaneously. However, simply measuring the expressions of multiple individual genes is insufficient to understand complex biological systems. To relate gene expressions to physiological states and environmental factors, gene expression networks are utilized. These networks enable the identification of previously unknown macroproperties of biological systems that emerge from interactions of multiple genes. Revealing these emerging properties relies on the precision of network reconstruction from experimental data. The major limitation for network reconstruction is usually the number of samples available for the study. This project develops a mathematical foundation for a new method of biological network reconstruction that relies on fundamental principles that connect correlation and causality. Reconstructing gene networks will lead to greater understanding of complex biological systems. This project develops a new and more precise method of biological network reconstruction based on measuring the Proportion of Unexpected Correlations (PUC). This research extends into the mathematical theory of correlation inequalities and its applications. By using probability theory and statistical mechanics, this project will improve the precision in identifying the proportion of false gene connections attributed to noisy data in gene networks. This project has four primary components: (i) PUC investigations of interactions between the microbiome and host transcriptome; (ii) experimental validation the mathematical approach using metagenomic data; (iii) the development of a Cytoscape plug-in for the PUC analysis of biological data; (iv) interdisciplinary student research training in Systems Biology and Mathematics.