Abstract/Details

In silico bacterial gene regulatory network reconstruction from sequence


2012 2012

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Abstract (summary)

DNA sequencing techniques have evolved to the point where one can sequence millions of bases per minute, while our capacity to use this information has been left behind. One particularly notorious example is in the area of gene regulatory networks. A molecular study of gene regulation proceeds one protein at a time, requiring bench scientists months of work purifying transcription factors and performing DNA footprinting studies. Massive scale options like ChIP-Seq and microarrays are a step up, but still require considerable resources in terms of manpower and materials. While computational biologists have developed methods to predict protein function from sequence, gene locations from sequence, and even metabolic networks from sequence, the space of regulatory network reconstruction from sequence remains virtually untouched. Part of the reason comes from the fact that the components of a regulatory interaction, such as transcription factors and binding sites, are difficult to detect. The other, more prominent reason, is that there exists no "recognition code" to determine which transcription factors will bind which sites. I've created a pipeline to reconstruct regulatory networks starting from an unannotated complete genomic sequence for a prokaryotic organism. The pipeline predicts necessary information, such as gene locations and transcription factor sequences, using custom tools and third party software. The core step is to determine the likelihood of interaction between a TF and a binding site using a black box style recognition code developed by applying machine learning methods to databases of prokaryotic regulatory interactions. I show how one can use this pipeline to reconstruct the virtually unknown regulatory network of Bacillus anthracis.

Indexing (details)


Subject
Genetics;
Biomedical engineering;
Bioinformatics
Classification
0369: Genetics
0541: Biomedical engineering
0715: Bioinformatics
Identifier / keyword
Applied sciences; Biological sciences; Binding prediction; DNA sequence; Gene regulation; Protein-DNA interactions; Regulatory network reconstruction; Transcription
Title
In silico bacterial gene regulatory network reconstruction from sequence
Author
Fichtenholtz, Alexander Michael
Number of pages
126
Publication year
2012
Degree date
2012
School code
0017
Source
DAI-B 73/06, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9781267216052
Advisor
Collins, James
University/institution
Boston University
University location
United States -- Massachusetts
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3500341
ProQuest document ID
928117688
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/928117688
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