2014- 2017 WRF and Moore/Sloan Postdoctoral Fellow
Biological Sciences, Statistics
David Williams was a Moore/Sloan Data Science and WRF Innovation in Data Science Postdoctoral Fellow from 2014-2017. Currently his title is “Scientist: theory and modeling” at the new Allen Institute of Cell Science.
Department of Biology
Thomas L. Daniel, Biology
Magdalena Balazinska, Computer Science & Engineering
NSF Postdoctoral Fellowship in Mathematical Biology, Harvard Univ., 2012-14
Ph.D., Physiology and Biophysics, UW, 2012
B.A., Physics, Reed College, 2006
Muscle is a uniquely regulated system. We are used to thinking of biological processes as primarily controlled by chemical signals. The processes in which these chemical signals are coupled to mechanical stresses and strains, such as bone growth, typically occur over time periods of days to months.
David Williams’ work focused on the physical regulation of muscle contraction: a process controlled by forces on the sub-millisecond time scale. This field has traditionally relied on expert hand-digitization of experiment images. Williams was increasing replicability, throughput, and the questions we can ask about muscle regulation through the introduction of automated analysis techniques drawn from cross-discipline collaborations.
Williams and his team have developed a processing tool chain, described in an accepted conference paper, which first segments the diffraction image into regions of interest using highly conserved features and then samples possible parameter values with a Markov chain Monte Carlo approach. This work was at the time being applied to our first high-temporal resolution data set with further refinement and application in the coming year.