ID: q-bio/0701002

The role of input noise in transcriptional regulation

December 30, 2006

View on ArXiv
Gasper Tkacik, Thomas Gregor, William Bialek
Quantitative Biology
Molecular Networks
Cell Behavior

Even under constant external conditions, the expression levels of genes fluctuate. Much emphasis has been placed on the components of this noise that are due to randomness in transcription and translation; here we analyze the role of noise associated with the inputs to transcriptional regulation, the random arrival and binding of transcription factors to their target sites along the genome. This noise sets a fundamental physical limit to the reliability of genetic control, and has clear signatures, but we show that these are easily obscured by experimental limitations and even by conventional methods for plotting the variance vs. mean expression level. We argue that simple, global models of noise dominated by transcription and translation are inconsistent with the embedding of gene expression in a network of regulatory interactions. Analysis of recent experiments on transcriptional control in the early Drosophila embryo shows that these results are quantitatively consistent with the predicted signatures of input noise, and we discuss the experiments needed to test the importance of input noise more generally.

Similar papers 1

Mechanical Bounds to Transcriptional Noise

March 10, 2016

89% Match
Stuart A. Sevier, David A. Kessler, Herbert Levine
Subcellular Processes

Over the last several decades it has been increasingly recognized that stochastic processes play a central role in transcription. Though many stochastic effects have been explained, the source of transcriptional bursting (one of the most well-known sources of stochasticity) has continued to evade understanding. Recent results have pointed to mechanical feedback as the source of transcriptional bursting but a reconciliation of this perspective with preexisting views of transcr...

Find SimilarView on arXiv

Transcription and noise in negative feedback loops

August 2, 2007

88% Match
J. C. Nacher, T. Ochiai
Molecular Networks

Recently, several studies have investigated the transcription process associated to specific genetic regulatory networks. In this work, we present a stochastic approach for analyzing the dynamics and effect of negative feedback loops (FBL) on the transcriptional noise. First, our analysis allows us to identify a bimodal activity depending of the strength of self-repression coupling D. In the strong coupling region D>>1, the variance of the transcriptional noise is found to be...

Find SimilarView on arXiv

Diverse spatial expression patterns emerge from common transcription bursting kinetics

December 21, 2017

88% Match
Benjamin Zoller, Shawn C. Little, Thomas Gregor
Subcellular Processes
Molecular Networks

In early development, regulation of transcription results in precisely positioned and highly reproducible expression patterns that specify cellular identities. How transcription, a fundamentally noisy molecular process, is regulated to achieve reliable embryonic patterning remains unclear. In particular, it is unknown how gene-specific regulation mechanisms affect kinetic rates of transcription, and whether there are common, global features that govern these rates across a ge...

Find SimilarView on arXiv

Noise control and utility: from regulatory network to spatial patterning

January 19, 2020

87% Match
Qing Nie, Lingxia Qiao, Yuchi Qiu, ... , Zhao Wei
Molecular Networks
Cell Behavior

Stochasticity (or noise) at cellular and molecular levels has been observed extensively as a universal feature for living systems. However, how living systems deal with noise while performing desirable biological functions remains a major mystery. Regulatory network configurations, such as their topology and timescale, are shown to be critical in attenuating noise, and noise is also found to facilitate cell fate decision. Here we review major recent findings on noise attenuat...

Find SimilarView on arXiv

Post-transcriptional regulation of noise in protein distributions during gene expression

June 21, 2010

87% Match
Tao Jia, Rahul V. Kulkarni
Molecular Networks
Biological Physics

The intrinsic stochasticity of gene expression can lead to large variability of protein levels across a population of cells. Variability (or noise) in protein distributions can be modulated by cellular mechanisms of gene regulation; in particular, there is considerable interest in understanding the role of post-transcriptional regulation. To address this issue, we propose and analyze a stochastic model for post-transcriptional regulation of gene expression. The analytical sol...

Find SimilarView on arXiv

Extending the dynamic range of transcription factor action by translational regulation

July 9, 2015

86% Match
Thomas R. Sokolowski, Aleksandra M. Walczak, ... , Tkačik Gašper
Molecular Networks

A crucial step in the regulation of gene expression is binding of transcription factor (TF) proteins to regulatory sites along the DNA. But transcription factors act at nanomolar concentrations, and noise due to random arrival of these molecules at their binding sites can severely limit the precision of regulation. Recent work on the optimization of information flow through regulatory networks indicates that the lower end of the dynamic range of concentrations is simply inacc...

Find SimilarView on arXiv

Effect of promoter architecture on the cell-to-cell variability in gene expression

August 11, 2010

86% Match
Alvaro Sanchez, Hernan Garcia, Daniel Jones, ... , Kondev Jane'
Molecular Networks

According to recent experimental evidence, the architecture of a promoter, defined as the number, strength and regulatory role of the operators that control the promoter, plays a major role in determining the level of cell-to-cell variability in gene expression. These quantitative experiments call for a corresponding modeling effort that addresses the question of how changes in promoter architecture affect noise in gene expression in a systematic rather than case-by-case fash...

Find SimilarView on arXiv

Facilitated diffusion buffers noise in gene expression

July 22, 2014

86% Match
Armin P. Schoech, Nicolae Radu Zabet
Molecular Networks
Quantitative Methods

Transcription factors perform facilitated diffusion (3D diffusion in the cytosol and 1D diffusion on the DNA) when binding to their target sites to regulate gene expression. Here, we investigated the influence of this binding mechanism on the noise in gene expression. Our results showed that, for biologically relevant parameters, the binding process can be represented by a two-state Markov model and that the accelerated target finding due to facilitated diffusion leads to a r...

Find SimilarView on arXiv

Nonspecific transcription factor binding reduces variability in transcription factor and target protein expression

May 11, 2014

86% Match
Mohammad Soltani, Pavol Bokes, ... , Singh Abhyudai
Subcellular Processes
Molecular Networks

Transcription factors (TFs) interact with a multitude of binding sites on DNA and partner proteins inside cells. We investigate how nonspecific binding/unbinding to such decoy binding sites affects the magnitude and time-scale of random fluctuations in TF copy numbers arising from stochastic gene expression. A stochastic model of TF gene expression, together with decoy site interactions is formulated. Distributions for the total (bound and unbound) and free (unbound) TF level...

Find SimilarView on arXiv

Balancing noise and plasticity in eukaryotic gene expression

September 11, 2012

86% Match
Djordje Bajić, Juan F. Poyatos
Genomics
Molecular Networks

Coupling the control of expression stochasticity (noise) to the ability of expression change (plasticity) can alter gene function and influence adaptation. A number of factors, such as transcription re-initiation, strong chromatin regulation or genome neighboring organization, underlie this coupling. However, these factors do not necessarily combine in equivalent ways and strengths in all genes. Can we identify then alternative architectures that modulate in distinct ways the...

Find SimilarView on arXiv