Events - Daily
|Monday, March 11|
Henrik Flyvbjerg, Danish Technical U, Optimal estimation of diffusion coefficients from noisy single-particle trajectories
Einstein argued in 1905 that the mean squared displacement of a microscopic particle suspended in a fluid at rest is proportional to time with the constant of proportionality giving the particle's diffusion coefficient. Since then, diffusion coefficients have been determined from particle trajectories by fitting a straight line to the observed mean squared displacement. This procedure is accurate, but not precise in any of its incarnations, yet dominates practice.
We present a simpler, optimal, and unbiased estimator of diffusion coefficients of freely diffusing particles in homogeneous media. It takes time-lapse recorded single-particle trajectories as input, is vastly superior to estimates based on the mean squared displacement as function of time, and is superior to Maximum Likelihood estimation for short trajectories. We extend this estimator to taught fluctuating substrates in a manner that removes the false contribution from substrate motion to the measured diffusion coefficient.
As a pertinent practical illustration of its power, this estimator reveals a two-state kinetics in the diffusion of hOGG1 protein on flow-stretched DNA, a fluctuating substrate. This kinetics is found in data that previously were analyzed with the mean squared displacement, which revealed only simple diffusion.
Joseph Henry Room, Jadwin Hall · 12:00 p.m.– 1:00 p.m.
Kevin White, U Chicago, Integrating Genomic Networks to Identify Biomarkers and Drug Targets
Director, Institute for Genomics & Systems Biology, UofC and Argonne National Laboratory (ANL)
Systems level approaches to construct abstract molecular networks can lead to predictions about genetic and biochemical functions in cells, organisms and in disease states. We have used integrated experimental and computational approaches to construct large scale functional networks in both model organisms and human cancer cells. Our network models are based on a combination of gene expression, transcription factor DNA binding site mapping, automated literature mining and protein-protein interaction mapping. We provide a strategy for reducing the dimensionality of the massive networks that result from such integrated whole genome analyses. I will present examples from both Drosophila and human breast cancer cell lines that illustrate how one can translate systems biology-driven findings in model systems to useful tools for diagnosing human diseases. I will also discuss our use of large scale genome sequence data in the context of systems approaches to developing prognostic signatures for breast cancer, and the use of cloud computing to manage and mine ‘omics data.
Carl Icahn Lab 101 · 4:15 p.m.– 5:15 p.m.