Kevin A. Janes, PhD

Kevin A. Janes, PhD

[Dynamic Data - Faculty Directory ]

Systems approaches for studying epithelial-cell responses

Our group develops experimental and computational techniques for quantitatively monitoring signaling networks as they become activated by diverse stimuli and perturbations.  These tools allow us to collect complex datasets, which can be analyzed by "data-driven" modeling to address network-level questions about signal transduction.  Understanding how signaling networks enable cells to respond to their environment is important for diseases such as cancer, where the molecular "signal processing" has gone awry and cellular responses are inappropriate. We are working in the following areas:

  1. Network control of epithelial-tissue function-We have an ongoing effort in quantifying the information content of important classes of cellular signals (kinases, phosphatases, and transcription factors) and their role in cell death, proliferation, and differentiation.  We are developing high-throughput assays that can quantify signal-activation states for hundreds of experimental conditions in a culture model of the colonic epithelium.  These projects rely heavily on the Biomolecular Research and Flow Cytometry core facilities at the Cancer Center.
  2. 2. Molecular dichotomies in mammary-gland morphogenesis and tumorigenesis-We have recently developed a systematic approach (called "stochastic sampling") for identifying molecular differences among cells that otherwise appear identical.  We are investigating the possibility that such dichotomies give rise to heterogeneous cell fates in cancer by using a three-dimensional culture model of the mammary-epithelial acinar morphogenesis.  We use the Research Histology and Tissue Research core facilities to examine the clinical relevance of the biological mechanisms discovered in vitro.

    3. Data-driven modeling of signal-transduction networks-Our datasets often require new computational-modeling approaches to maximize the knowledge gained from each large-scale experimental study.  We are adapting multivariate and biostatistical approaches to help suggest hypotheses that can be immediately tested by directed experiments.