From The ChuangLab
- Kourosh Zarringhalam
- Biology Department
- Boston College, Higgins Hall 420A
- 140 Commonwealth Ave, Chestnut Hill, MA 02467
- kourosh.zarringhalam [AT] bc [DOT] edu
I am a postdoctoral research fellow in Computational Biology with the Chuang lab here at BC. I obtained my PhD in Mathematics from the University of New Hampshire. Please click here for a copy of CV.
I am interested in the application of computational algorithms and mathematical models in biological systems. I am actively working in the following areas:
High Throughput Lipidomics
Lipidomics is a rapidly emerging field, requiring more advanced integration of mathematical and computational approaches to interpret accruing datasets of complex distributions of lipidmolecular species. I am interested in developing mathematical and computational models to infer the complex network of acyl chain interactions and the remodeling pathway of phospholipids.
RNA secondary structure
I am currently collaborating with Clote Lab in developing novel algorithms for improving the prediction of RNA secondary structure. We have developed a novel method to integrate chemical or enzymatic footprinting data (e.g. SHAPE, PARS, in-line probing, etc.) into thermodynamics-based RNA secondary structure algorithms. I am extending this algorithm to include the information from RNA "mutate-and-map" experiments developed by Das Lab.
MicroRNAs are short endogenous RNAs that regulate gene expression post-transcriptionally. I am working on development of novel algorithms for the discovery of miRNAs and their targets, in particular in coding sequences.
Computer Vision and Behavioral Analysis
During 2009-2010 academic year, I worked with Oliver King at the Boston Biomedical Research Institute BBRI where I developed a computer vision based software and statistical models for automatic quantification of mouse home-cage behaviors, suitable for continuous 24hr video monitoring. We validated our program by assessing behavioral changes in the R6/2 transgenic mouse model of Huntington’s disease over a ten-week period. I am working on integrating more machine learning algorithms into our software for detection of more complex behaviors.
openCage is an open source computer vision based software developed in C++ for quantifying mouse home-cage behaviors.
RNAsc is an open source software written in C++ that uses chemical probing data and determines the RNA secondary structure with very high accuracy.
microBF is a software package written in python that uses a SVM classifier, trained on the Boltzmann Features of the pre-miRNA hairpin to detect novel MicroRNAs.
- Dynamics of the ethanolamine glycerophospholipid remodeling network, under revision.
- Integrating chemical footprinting data into RNA secondary structure prediction, In Press, PLoS ONE.
- Statistical analysis of processes controlling choline and ethanolamine glycerophospholipids molecular species composition, PLoS ONE, 2012.
- An open system for automatic home-cage behavioral analysis and its application to male and female mouse models of Huntington’s disease, Behavioural Brain Research, 2012.
- Sequential Adaptive Compressed Sampling Via Huffman Codes, Sampling Theory in Signal and Image Processing, 2011. http://arxiv.org/abs/0810.4916
- Nonlinear Least Squares in RN, Acta Applicandae Mathematicae, 2009.
- Compressed sampling via Huffman codes, Proceedings of the 2009 International Conference on Sampling Theory and Application SAMPTA09, 2009.
- Generating an adaptive multiresolution image analysis with compact cupolets, Journal of Nonlinear Dynamics, 2007.
Work Under Progress
- RNA secondary structure prediction using two-dimensional SHAPE data.
- microBF, a MicroRNA identification algorithm using Boltzmann triplet features.