Project 4. Computational and experimental approaches for identification of gene networks (D. S. Lun, Computer Science; E. A. Klein, Biology)

Background. Unraveling the structure of biological networks is a formidable challenge that holds the promise of transforming biology into a predictive science, allowing living organisms to be engineered for revolutionary biotechnological applications [1, 2]. A well-known biological network is that controlling gene expression—known as the gene network. Since the advent of high-throughput genomic technologies, much attention has focused on the interconnections that control gene expression [3-7]. The DREAM (Dialogue on Reverse Engineering Assessment and Methods) competition, promoting new methods for gene networks [8], has shown that state-of-the-art methods show accuracy significantly higher than random guessing, but too low to make high-confidence claims of genetic interactions. Moreover, the improvement over the years is marginal [6, 9].

Research. Students will work on the identification of gene networks through an interdisciplinary coupling of computational and experimental methods. In contrast to standard methods, based on inferring gene networks from a given set of experimental, they we use a computational model of gene networks to identify what measurements to perform and how to use them to identify gene networks (see Figure 1). Our model is based on the stochastic differential equation (SDE) model of gene networks used by the program GeneNetWeaver, which generates artificial data for DREAM [10]. We will also develop gene identification strategies, computationally test them [11, 12] and perform proof-of-concept experiments in E. coli to demonstrate the experimental feasibility of the strategy.

Figure 1. A combined computational and experimental approach to gene network identification.

Student activities. Students will undertake a combination of computational and experimental activities. They will be guided through the use of the SDE-based computational gene network model, which they will use to experiment with gene network identification strategies. They will implement a selected strategy by constructing E. coli mutants and measuring their gene expression. They will construct knockout, knockdown, and over-expression mutants of E. coli. Gene expression will be measured through a combination of qPCR and RNA-seq. The students will then analyze and interpret their results and discuss their implications for gene network identification.