Biological networks axis

Transcriptomics involves studying all RNA molecules in a cell to understand which proteins are being transcribed from DNA. Together with Prof. Daniel Bolnick, Dr. Rogini Runghen is working on evaluating changes in gene expression networks through time and divergence in populations over time. To do this, we collect samples from all source and recipient lakes each year to analyse transcriptomic data from head kidneys and liver tissues. Network changes over time and between populations will underlie shifts in higher-order phenotypes. This work is done in collaboration with Prof. Jesse Weber and Emily Kerns, we are also evaluating plasticity and heritability of co-expression network differences. 

We partnered with the Gordon and Betty Moore Foundation to generate metabolomic data from stickleback in 2023. In collaboration with Dr. Marwa Tawfiq, we are using Liquid Chromatography Mass-Spec (LC-MS-MS) to quantify liver metabolomics of stickleback from each experimental lake. The data will be related to diet, parasite status, gene expression, and microbiota using a multi-layer network analysis approach.  We are interested in knowing how these networks differ between phenotypes (sex, size, infection status, etc) and between populations.

 Led by Dr. Miaoyan Wang and Jiaxin Hu, we have developed new methods for doing gene expression Quantitative Trait Locus (QTL) mapping to find genes that are polymorphic and alter the overall structure of the gene co-expression network. These high-leverage loci within the network require statistical comparisons between multiple networks. 

 Merging all of the above, we hope to evaluate how changes in gene expression networks,  metabolomic networks, microbiota networks, and diet and infection networks, collectively are inter-related. This will use multi-level network analysis to identify connections between the different types of biological networks.