David M. Umulis

David M. Umulis Profile Picture

Dane A. Miller Head and Professor of Biomedical Engineering
Ph.D. in Chemical Engineering, University of Minnesota

Contact Info:

MJIS 3001

Training Group(s):
Cancer Biology

Active Mentor - currently hosting PULSe students for laboratory rotations and recruiting PULSe students into the laboratory; serves on preliminary exam committees

Current Research Interests:

The focus of our research is to investigate the regulation of signal transduction in development. Specifically, we are interested in elucidating mechanisms of robustness, cell fate decisions, and tissue patterning by morphogen gradients. Engineers and biologists with some math/physics background are particularly capable of addressing these questions because development in many contexts relies on fluid flow, mass transport, chemical reactions, process control, and thermodynamics. One major goal of our lab is to foster interdisciplinary research projects to tackle problems in biology using quantitative image analysis and systems biology approaches such as mathematical models and bioinformatics.

Much is known about the molecular components involved in signal transduction and gene expression in a number of model systems in developmental biology, and the focus is now shifting to understanding how these components are integrated into networks, and how these networks transduce the inputs they receive and produce the desired pattern of gene expression. The major question is how the correct genes are turned on at the correct point in space at the correct time in development to produce the numerous cell types present in an adult. While the problem is simply stated, delineating the mechanisms of development that impart the robust control requires sophisticated computational models. To study these problems, we focus on coupling advanced microscopy and image analysis with multi-dimensional finite-element models of biological patterning mechanisms: most recently BMP patterning of Drosophila embryos. The direct incorporation of experimental data into geometrically accurate computational models leads to significant new biological insights that cannot be gained by either approach independently.

Selected Publications:

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