Science

 

1. Computational Spatial Tumor Pathology

SpectralTilingDigitalPathology Genetic testing flags the presence and type of intra-tumor heterogeneity, but the spatial distribution of the heterogeneity can be appreciated only with in situ imaging of tissue sections or tumor microarrays using methods such as fluorescence-based immunohistochemistry. New technological advances now allow up to 60 proteins and a few RNA or DNA probes—with a theoretically unlimited number possible—to be labeled on the same multicellular tissue slice
yielding large-scale, whole-slide hyperplexed fluorescence images. Imaging at this scale raises several new big-data challenges and opportunities for automated image analysis, including how to quantitate and characterize spatial intra-tumor heterogeneity (ITH), how to harmonize computational methodologies across diagnostic labs and clinical trials, and how to translate ITH analysis into research insights and clinical use

Collaborators:

2. Computational Ciliary Motion Analysis

Motile cilia lining the nasal and bronchial passages beat synchronously to clear mucus and foreign matter from the respiratory tract. This mucociliary defense mechanism is essential for pulmonary health, as respiratory ciliary motion defects, such as those in patients with primary ciliary dyskinesia (PCD) or congenital heart disease, can cause severe sinopulmonary disease necessitating organ transplant. The visual examination of nasal or bronchial biopsies is critical for diagnosis of ciliary motion defects, but these analyses are highly subjective and error-prone. Although ciliary beat frequency can be computed, this metric cannot sensitively characterize ciliary motion defects. Furthermore, PCD can present without any ultrastructural defects, limiting the utility of other detection methods, such as electron microscopy. Therefore, an unbiased, computational method for analyzing ciliary motion is clinically compelling.

Collaborators:

3. Physiochemical Determinants of Odor Perception

A key challenge in olfactory research is to describe the relationship between chemical structure and odor percept. Despite many decades invested in this problem by basic science and industry, there is still no means for systematically and accurately assigning descriptive odor labels – “minty”, “oily”, “woody,” etc – to arbitrary odorants. We are developing new graph theoretic and semi-supervised learning (SSL) methods to accomplish this. In contrast to traditional approaches, which have sought a structure-percept mapping through experiments involving small and idiosyncratic subsets of odorant space, we aim to densely characterize odor space using computational techniques applied to a publically available database (PubChem) of ~3×10^7 chemical compounds. A critical piece of our project will be the use of human judgments of odor quality to test these hypotheses.

Collaborators:

4. Statistical Inference in Molecular Biophysics

A key challenge in molecular biophysics is to discern short-lived, rare intermediate conformations that proteins access in order to natively fold, bind signaling partners, and perform inhibition or catalysis. To this end, we are developing higher-order statistical trajectory analysis toolbox, named anharmonic conformational analysis in combination with network-based approaches, to integrate experimental observations from nuclear magnetic resonance (NMR) relaxation dispersion, small angle neutron scattering (SANS) and single molecule Forster resonance energy transfer (smFRET) techniques with long timescale molecular dynamics simulation trajectories. We are applying these techniques to characterize conformational sub-states and discern the intermediate conformations that are relevant to protein function, including enzyme catalysis, molecular recognition and signaling.

Collaborators: