Physio-Analytics Laboratory

Physio-Analytics Laboratory

The Physio-Analytics Laboratory is interested in understanding physiology and biology in an integrative and holistic model, using cutting-edge data analytical tools. We are a highly collaborative group that integrates multiple ‘omics’ techniques in a broad range of human and animal studies.  Currently, a major research focus is centered on understanding how the ecology of the gut microbiome is shaped under various conditions (e.g., diet, host energy regulation, exercise) and identifying mechanisms that underlie this interaction.

The R Statistical Language is our primary tool due to its flexibility, reproducibility, and more recent advances in interactive data visualization (Shiny, RMarkdown).  The Physio-Analytics Lab works hand-in-hand with the ACNC Informatics Team (also led by Dr. Piccolo), to develop interactive HTML-based apps to streamline our data analytics approaches.  The Lab and Informatics Team also provides user-friendly tools for collaborating scientists to analyze high-dimensional data with little or no understanding of R.  We have recently released DAME, a free Shiny app that allows for rapid and interactive exploration of 16S rRNA amplicon sequencing data.  Future apps are in development for other data pipelines.

Brian Piccolo, Ph.D., Assistant Professor and Lab Director

Informatics Team partnerSree V. Chintapalli, Ph.D., Assistant Professor

Current Projects

The gut microbiome has been implicated as a potential regulator of host energy regulation, but the mechanisms in which it influences host metabolism is not well understood.  Furthermore, most of our understanding is uni-directional (i.e., microbial signals to host) and very little is known about host-mediated signals that alter the ecology of the gut microbiome.  We are using several models to better understand microbial and host crosstalk in order to optimize the metabolic influence of the gut microbiome through diet and exercise.  Ongoing studies include identifying specific bacteria and metabolites that discriminate the progression of type-2-diabetes in the UC-Davis Type-2-Diabetes Mellitus Rat Model and identifying whether the gut microbiome and metabolome is modified by early exercise in post-weaned mice.

Shiny Apps/Documents

Dynamic Assessment of Microbial Ecology (DAME); Github

Group Correlation Matrix