Systems Biology of Dehalococcoides: Using Network Inference Modeling to Integrate Omics Datasets Under Varied Conditions.

摘要

The overall objectives of this project were to 1. obtain a systems- biology level understanding of gene networks in the Dehalococcoides and 2. develop assays for quantitative biomarkers of chloroethene detoxification steps and rates that could be deployed at sites undergoing in situ bioremediation utilizing this important group of microbes. After collecting genome-wide microarray expression data along with metabolite and chloroethene data, under a wide range of conditions (n = 53), we employed Bayesian inference algorithms to reconstruct the gene-gene and gene-metabolite subnetworks that are most supported by the expression data. These networks then lead to the discovery of robust biomarker candidates of respiration as well as of stress. Quantitative assays for these biomarkers were then developed and applied to examine the power of protein and/or RNA biomarker levels to serve as estimators of bulk culture respiration rates.