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The Combinatorics of Transcriptional...

Rydenfelt, Mattias.

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The Combinatorics of Transcriptional Regulation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	The Combinatorics of Transcriptional Regulation./
Author:	Rydenfelt, Mattias.
Description:	186 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-09(E), Section: B.
Contained By:	Dissertation Abstracts International75-09B(E).
Subject:	Biophysics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3621565
ISBN:	9781303927546

The Combinatorics of Transcriptional Regulation.
Rydenfelt, Mattias.

The Combinatorics of Transcriptional Regulation. - 186 p.

Source: Dissertation Abstracts International, Volume: 75-09(E), Section: B.

Thesis (Ph.D.)--California Institute of Technology, 2014.

This item must not be sold to any third party vendors.

The ability to regulate gene expression is of central importance for the adaptability of living organisms to changes in their internal and external environment. At the transcriptional level, binding of transcription factors (TFs) in the vicinity of promoters can modulate the rate at which transcripts are produced, and as such play an important role in gene regulation. TFs with regulatory action at multiple promoters is the rule rather than the exception, with examples ranging from TFs like the cAMP receptor protein (CRP) in E. coli that regulates hundreds of different genes, to situations involving multiple copies of the same gene, such as on plasmids, or viral DNA. When the number of TFs heavily exceeds the number of binding sites, TF binding to each promoter can be regarded as independent. However, when the number of TF molecules is comparable to the number of binding sites, TF titration will result in coupling ("entanglement") between transcription of different genes. The last few decades have seen rapid advances in our ability to quantitatively measure such effects, which calls for biophysical models to explain these data. Here we develop a statistical mechanical model which takes the TF titration effect into account and use it to predict both the level of gene expression and the resulting correlation in transcription rates for a general set of promoters.

ISBN: 9781303927546Subjects--Topical Terms:

518360
Biophysics.

The Combinatorics of Transcriptional Regulation.
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020 $a 9781303927546
035 $a (MiAaPQ)AAI3621565
035 $a AAI3621565
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100 1 $a Rydenfelt, Mattias. $3 3174686
245 1 4 $a The Combinatorics of Transcriptional Regulation.
300 $a 186 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-09(E), Section: B.
500 $a Adviser: Rob Phillips.
502 $a Thesis (Ph.D.)--California Institute of Technology, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a The ability to regulate gene expression is of central importance for the adaptability of living organisms to changes in their internal and external environment. At the transcriptional level, binding of transcription factors (TFs) in the vicinity of promoters can modulate the rate at which transcripts are produced, and as such play an important role in gene regulation. TFs with regulatory action at multiple promoters is the rule rather than the exception, with examples ranging from TFs like the cAMP receptor protein (CRP) in E. coli that regulates hundreds of different genes, to situations involving multiple copies of the same gene, such as on plasmids, or viral DNA. When the number of TFs heavily exceeds the number of binding sites, TF binding to each promoter can be regarded as independent. However, when the number of TF molecules is comparable to the number of binding sites, TF titration will result in coupling ("entanglement") between transcription of different genes. The last few decades have seen rapid advances in our ability to quantitatively measure such effects, which calls for biophysical models to explain these data. Here we develop a statistical mechanical model which takes the TF titration effect into account and use it to predict both the level of gene expression and the resulting correlation in transcription rates for a general set of promoters.
520 $a To test these predictions experimentally, we create genetic constructs with known TF copy number, binding site affinities, and gene copy number; hence avoiding the need to use free fit parameters. Our results clearly prove the TF titration effect and that the statistical mechanical model can accurately predict the fold change in gene expression for the studied cases. We also generalize these experimental efforts to cover systems with multiple different genes, using the method of mRNA fluorescence in situ hybridization (FISH). Interestingly, we can use the TF titration affect as a tool to measure the plasmid copy number at different points in the cell cycle, as well as the plasmid copy number variance.
520 $a Finally, we investigate the strategies of transcriptional regulation used in a real organism by analyzing the thousands of known regulatory interactions in E. coli. We introduce a "random promoter architecture model" to identify overrepresented regulatory strategies, such as TF pairs which coregulate the same genes more frequently than would be expected by chance, indicating a related biological function. Furthermore, we investigate whether promoter architecture has a systematic effect on gene expression by linking the regulatory data of E. coli to genome-wide expression censuses.
590 $a School code: 0037.
650 4 $a Biophysics. $3 518360
650 4 $a Bioinformatics. $3 553671
690 $a 0786
690 $a 0715
710 2 $a California Institute of Technology. $b Physics. $3 2100645
773 0 $t Dissertation Abstracts International $g 75-09B(E).
790 $a 0037
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3621565