Education & Training
- Ph.D. from Univrsity of Toronto, 2004
Research Interest Summary
The research focus of Chakra’s group is in translation science and big-data analysis in the form of computational bioimaging and statistical inference.Computational Ciliary Motion Analysis: Chakra’s group, in collaboration with Dr. Cecilia Lo’s group in the Dept. of Developmental Biology, delineated a novel computational approach that combines visual pattern recognition and statistics for the quantification of ciliary motion from high-speed videomicroscopy. In the respiratory epithelia, cells are lined with hair-like cilia projections that beat in synchrony to clear mucus and particulate matter from the respiratory tract. While clinically it is well appreciated that degenerative lung diseases can arise from defects in ciliary motion in the respiratory tract, the current gold standard for the diagnosis of airway ciliary dysfunction remains primitive, relying largely on the visual assessment of ciliary motion that is highly subjective and heavily dependent on reviewer experience. Instead of approaching the quantification problem from a physics-based perspective (such as inferring the forces and kinetic relationships between cilia in moving particulates), Chakra and group pursued an image-based dynamic texture analysis approach by equating ciliary motions to familiar motion patterns such as flickering flames, billowing smoke, or grass in the wind. Using this computational approach, they have recently demonstrated >90% accuracy in identifying abnormal ciliary motion in two cohorts of patients previously diagnosed with abnormal ciliary motion.
Savol, A., Chennubhotla, C.* (2014) Quantifying the sources of kinetic frustration in folding simulations of small proteins. Journal of Chemical Theory and Computation DOI: 10.1021/ct500361w.
Bakan, A., Dutta, A., Mao, W., Liu, Y., Chennubhotla, C., Lezon, T. & Bahar, I. (2014) Evol and Prody for bridging protein sequence evolution and structural dynamics. Bioinformatics DOI: 10.1093/bioinformatics/btu336 PMID: 24849577.
Ramanathan, A., Savol, A., Burger, V., Chennubhotla, C.* & Agarwal, P. K.* (2013) Protein conformational populations and functionally relevant sub-states. Accounts of Chemical Research DOI: 10.1021/ar400084s PMID: 23988159.
Castro, J.*, Ramanathan, A. & Chennubhotla, C.* (2013) Categorical dimensions of human odor descriptor space revealed by non-negative matrix factorization. PLoS ONE 8(9): e73289. doi: 10.1371/journal.pone.0073289. PMID: 24058466 News Media: NBC News, LA Times, Times London, The Independent London, BBC, CBC As It Happens.
Huang, G., Cunningham, K., Benos, T. & Chennubhotla, C.* (2013) Spectral Clustering Strategies for Heterogeneous Disease Data. 18th Pacific Symposium on Biocomputing, Hawaii Jan 3-7, 2013. PMID: 23424126.
Ramanathan, A., Savol, A., Agarwal, P. & Chennubhotla, C.* (2012) Event detection and sub-state discovery in bio-molecular simulations: application to enzyme adenylate kinase. Proteins, 2012 Jun 26. doi: 10.1002/prot.24135. PMID: 22733562.
Burger, V. & Chennubhotla, C.* (2012) Nhs: Network-based hierarchical segmentation for cryo-electron microscopy density maps. Biopolymers. 2012 Sep;97(9):732-41. doi: 10.1002/bip.22041 PMID: 22696408.
Burger, V., Ramanathan, A., Savol, A., Stanley, C. B., Agarwal, P. & Chennubhotla, C.* (2012) Quasi-anharmonic analysis reveals intermediate states in the nuclear co-activator receptor binding domain ensemble. 17th Pacific Symposium on Biocomputing 70-81. PMID: 22174264.
Savol, A., Burger, V., Agarwal, P., Ramanathan, A.* & Chennubhotla, C.* (2011) QAARM: Quasi-anharmonic autoregressive model reveals molecular recognition pathways in ubiquitin. 19th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and 10th European Conference in Computational Biology (ECCB) Proceedings. Vienna, Austria, July 2011. Bioinformatics (2011) 27(13): i61-i68 doi:10.1093/bioinformatics/btr249 PMID: 21685101.
Ramanathan, A., Savol, A., Langmead, C., Agarwal, P.* & Chennubhotla, C.* (2011) “Discovering conformational sub-states relevant to protein function” PLoS ONE 6(1): e15827. Doi: 10.1371/journal.pone.0015827. PMID: 21297978.