I aim to extract meaningful patterns and information from large biological datasets using various supervised and unsupervised machine learning methods:
- Developing a sequence-based classifier to accelerate high-throughput protein production
- Identifying independent regulatory modes in E. coli
- Analysis and integration of multi-omic data to incorporate transcriptional regulation into constraint-based models
University of California, San Diego: PhD student
Carnegie Mellon University: B.S. with double major in Chemical Engineering and Biomedical Engineering
Sastry A, Monk J, Tegel H, Uhlen M, J. Rockberg, B.O. Palsson, E. Brunk. Machine Learning in Computational Biology to Accelerate High-Throughput Protein Expression. Bioinformatics. 2017. doi:10.1093/bioinformatics/btx207.
Fang X*, Sastry A*, Tan J, Yurkovich JT, Lloyd CJ, Mih N, Kim D, Yang L, Palsson BO. A high-confidence global transcriptional regulatory network for Escherichia coli is consistent with transcriptomic data. under review.
Ebrahim A*, Brunk E*, Tan J*, O'Brien EJ, Kim D, Szubin R, Lerman JA, Lechner A, Sastry A, Bordbar A, Feist AM, Palsson BO. Multi-omic data integration enables discovery of hidden biological regularities. Nature Communications. 2016;7:13091. doi:10.1038/ncomms13091.
Monk J, Lloyd C, Brunk E, Mih N, Sastry A, King Z, Takeuchi R, Nomura W, Mori H, Feist AM, Palsson BO. Computable knowledgebase of Escherichia coli metabolism and its structural proteome: Meeting the big data to knowledge challenge. under review.
Prior Work Experience
Software Consultant, Novo Nordisk Foundation Center for Biosustainability
San Diego, CA. Oct 2014-May 2015
- Software development of the MASS Toolbox kinetic modeling package
- Development Engineer, Emerald Therapeutics Menlo Park, CA. Summer 2013 & 2014
- Developed various tools in Mathematica to facilitate laboratory automation
- Designed and implemented an automated process to to cleave biopolymers from resin
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