scholarly journals Probing the effect of promoters on noise in gene expression using thousands of designed sequences

2014 ◽  
Vol 24 (10) ◽  
pp. 1698-1706 ◽  
Author(s):  
Eilon Sharon ◽  
David van Dijk ◽  
Yael Kalma ◽  
Leeat Keren ◽  
Ohad Manor ◽  
...  
Keyword(s):  
Gene Expression ◽  
Noise In Gene Expression

scholarly journals Contribution of RNA Degradation to Intrinsic and Extrinsic Noise in Gene Expression

Cell Reports ◽  
2019 ◽  
Vol 26 (13) ◽  
pp. 3752-3761.e5 ◽  
Author(s):  
Antoine Baudrimont ◽  
Vincent Jaquet ◽  
Sandrine Wallerich ◽  
Sylvia Voegeli ◽  
Attila Becskei
Keyword(s):  
Gene Expression ◽  
Rna Degradation ◽  
Extrinsic Noise ◽  
Noise In Gene Expression ◽  
Intrinsic And Extrinsic Noise

scholarly journals Tuning noise in gene expression

2015 ◽  
Vol 11 (5) ◽  
pp. 805 ◽  
Author(s):  
Sanjay Tyagi
Keyword(s):  
Gene Expression ◽  
Noise In Gene Expression

scholarly journals Hsf1 phosphorylation generates cell-to-cell variation in Hsp90 levels and promotes phenotypic plasticity

2017 ◽  
Author(s):  
Xu Zheng ◽  
Ali Beyzavi ◽  
Joanna Krakowiak ◽  
Nikit Patel ◽  
Ahmad S. Khalil ◽  
...  
Keyword(s):  
Gene Expression ◽  
Phenotypic Plasticity ◽  
Important Determinant ◽  
Phenotypic Diversity ◽  
Yeast Cells ◽  
Wide Range ◽  
Differential Tuning ◽  
Gene Expression Heterogeneity ◽  
Noise In Gene Expression ◽  
Cell Variation

ABSTRACTClonal populations of cells exhibit cell-to-cell variation in the transcription of individual genes. In addition to this “noise” in gene expression, heterogeneity in the proteome and the proteostasis network expands the phenotypic diversity of a population. Heat shock transcription factor (Hsf1) regulates chaperone gene expression, thereby coupling transcriptional noise to proteostasis. Here we show that cell-to-cell variation in Hsf1 activity is an important determinant of phenotypic plasticity. Budding yeast cells with high Hsf1 activity were enriched for the ability to acquire resistance to an antifungal drug, and this enrichment depended on Hsp90 – a known “phenotypic capacitor” and canonical Hsf1 target. We show that Hsf1 phosphorylation promotes cell-to-cell variation, and this variation – rather than absolute Hsf1 activity – promotes antifungal resistance. We propose that Hsf1 phosphorylation enables differential tuning of the proteostasis network in individual cells, allowing populations to access a wide range of phenotypic states.

Integrative analysis of gene expression and DNA methylation using unsupervised feature extraction for detecting candidate cancer biomarkers

2018 ◽  
Vol 16 (02) ◽  
pp. 1850006 ◽  
Author(s):  
Myungjin Moon ◽  
Kenta Nakai
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Feature Extraction ◽  
Biomarker Discovery ◽  
Extraction Methods ◽  
Integrative Analysis ◽  
Differentially Expressed ◽  
Improved Performance ◽  
Noise In Gene Expression ◽  
Unsupervised Feature Extraction

Currently, cancer biomarker discovery is one of the important research topics worldwide. In particular, detecting significant genes related to cancer is an important task for early diagnosis and treatment of cancer. Conventional studies mostly focus on genes that are differentially expressed in different states of cancer; however, noise in gene expression datasets and insufficient information in limited datasets impede precise analysis of novel candidate biomarkers. In this study, we propose an integrative analysis of gene expression and DNA methylation using normalization and unsupervised feature extractions to identify candidate biomarkers of cancer using renal cell carcinoma RNA-seq datasets. Gene expression and DNA methylation datasets are normalized by Box–Cox transformation and integrated into a one-dimensional dataset that retains the major characteristics of the original datasets by unsupervised feature extraction methods, and differentially expressed genes are selected from the integrated dataset. Use of the integrated dataset demonstrated improved performance as compared with conventional approaches that utilize gene expression or DNA methylation datasets alone. Validation based on the literature showed that a considerable number of top-ranked genes from the integrated dataset have known relationships with cancer, implying that novel candidate biomarkers can also be acquired from the proposed analysis method. Furthermore, we expect that the proposed method can be expanded for applications involving various types of multi-omics datasets.

scholarly journals Extrinsic noise and heavy-tailed laws in gene expression

2019 ◽  
Author(s):  
Lucy Ham ◽  
Rowan D. Brackston ◽  
Michael P.H. Stumpf
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Extrinsic Noise ◽  
Individual Gene ◽  
Copy Numbers ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Intrinsic Stochasticity ◽  
The Individual ◽  
Noise In Gene Expression

AbstractNoise in gene expression is one of the hallmarks of life at the molecular scale. Here we derive analytical solutions to a set of models describing the molecular mechanisms underlying transcription of DNA into RNA. Our Ansatz allows us to incorporate the effects of extrinsic noise – encompassing factors external to the transcription of the individual gene – and discuss the ramifications for heterogeneity in gene product abundance that has been widely observed in single cell data. Crucially, we are able to show that heavy-tailed distributions of RNA copy numbers cannot result from the intrinsic stochasticity in gene expression alone, but must instead reflect extrinsic sources of variability.

scholarly journals Diffusion of Transcription Factors Can Drastically Enhance the Noise in Gene Expression

2006 ◽  
Vol 91 (12) ◽  
pp. 4350-4367 ◽  
Author(s):  
Jeroen S. van Zon ◽  
Marco J. Morelli ◽  
Sorin Tǎnase-Nicola ◽  
Pieter Rein ten Wolde
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Noise In Gene Expression
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