| dc.identifier.uri | http://hdl.handle.net/11401/77319 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.abstract | With the advance of synthetic biology has come increased interest in designing synthetic genes which optimize protein expression. We propose new algorithms for gene design under several different constraints. Our optimization criteria include finding minimum energy and maximum energy RNA structures for a given gene sequence, optimizing the amount of tRNA auto-correlation in genes and designing maximum and minimum auto-correlated sequences. We also develop methods to analyze and interpret tiled microarray genome expression data. Statistical analysis of the viral genome expression data enables us to discover unknown facts about its life cycle, its impact on the host cell shutoff mechanism. We work on identifying novel housekeeping genes and finding differentially expressed genes. We further seek to cluster genes at different experimental conditions based on the expression changes across the array. Ribosome profiling is a recently developed popular method, which gives us a global picture of the active ribosomes inside a cell. Study of the ribosome profile data helps us interpret the overall translation mechanism and determine delays at different steps of the translation process. We analyze ribosome footprint data to predict relative residency times of ribosome (RRT) at different codons and show that RRT is correlated with the usage bias of the codons based on experimental analysis of yeast. We extend our work to predict the impact of codon-pair bias on translation process and the effect of RNA secondary structure on ribosome footprint pile-up. We also work on predicting tRNA auto-correlation effect on the translation mechanism based on the analysis results obtained from the ribosome profile data. | |
| dcterms.available | 2017-09-20T16:52:30Z | |
| dcterms.contributor | Skiena, Steven | en_US |
| dcterms.contributor | Ramakrishnan, I. V. | en_US |
| dcterms.contributor | Chowdhury, Rezaul | en_US |
| dcterms.contributor | Futcher, Bruce. | en_US |
| dcterms.creator | Yeasmin, Rukhsana | |
| dcterms.dateAccepted | 2017-09-20T16:52:30Z | |
| dcterms.dateSubmitted | 2017-09-20T16:52:30Z | |
| dcterms.description | Department of Computer Science. | en_US |
| dcterms.extent | 158 pg. | en_US |
| dcterms.format | Application/PDF | en_US |
| dcterms.format | Monograph | |
| dcterms.identifier | http://hdl.handle.net/11401/77319 | |
| dcterms.issued | 2015-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:52:30Z (GMT). No. of bitstreams: 1
Yeasmin_grad.sunysb_0771E_12307.pdf: 4542048 bytes, checksum: 31ffe8b2abec400852a219e61419ad6b (MD5)
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | Computer science | |
| dcterms.subject | Computational Biology, Gene Design, Microarray Gene Expression Data, Ribosome Profile Data, Statistical Analysis, Synthetic Biology | |
| dcterms.title | Synthetic Gene Design: Optimization and Analysis | |
| dcterms.type | Dissertation | |
