Paul A Jensen
Assistant Professor of Bioengineering
Affiliate Professor of Microbiology
B.Bm.E. (Biomedical Engineering), B.Ch.E. (Chemical Engineering), University of Minnesota, 2008.
Ph.D. (Biomedical Engineering), University of Virginia, 2013.
Postdoc (Microbiology), Boston College, 2015.
Stress responses in the oral microbiome; computational systems biology; laboratory automation.
My research addresses three open questions regarding the structure and function of microbial stress response networks. First, why are transcriptional changes rarely observed for phenotypically important genes, and vice versa? We hypothesize that shielding phenotypically important genes from large transcriptional changes increases the robustness of the overall stress response network. In this case, the bacterium would be sacrificing network responsiveness to increase overall stability.
Even if our robustness hypothesis were true, there would still need to be some form of coordination between the transcriptional and phenotypic stress responses. Indeed, we discovered that transcriptional changes can be linked to phenotypically important genes using network modeling. If the intracellular network connects transcriptional and phenotypic stress responses, can we use a network model to predict important genes using only transcriptome data? It is far easier to collect transcriptome data using RNA-seq than to measure genome-wide fitness changes using transposon mutagenesis sequencing (Tn-seq). Tn-seq requires culturing and transforming a bacterium in vitro, while RNA-seq can be applied in vivo and to complex microbial communities. If phenotypic important genes can be identified using only a transcriptome and a network model, we would accelerate the discovery of stress response targets by removing the need to genetically manipulate individual species.
Finally, most studies on stress responses focus on intracellular responses of single species. We do not understand how interactions in microbial communities rewire stress response networks. A multiscale approach is necessary to understand how bacteria combine intracellular and intercellular networks to determine the stability and composition of complex communities.