Stochastic gene expression: from single molecules to the proteome

Kaufmann, B.; Oudenaarden, Alexander van

doi:10.1016/j.gde.2007.02.007

Cited by 159 publications

(154 citation statements)

References 41 publications

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“…In the single-cell study of fluorescently tagged E. coli strains mentioned above (Taniguchi et al, 2010), the correlation coefficient between per cell mRNA and protein levels of the same gene averaged zero for the genes tested. There are various reasons for a poor relationship between single-cell mRNAs and protein abundance, including post-transcriptional processing and regulation (Maier et al, 2009), random fluctuations in low-copy mRNAs (Kaufmann and van Oudenaarden, 2007), uneven partitioning of macromolecules during cell division (Golding et al, 2005) and variable translation efficiencies (that is, the number of completed proteins per mRNA per time; Maier et al, 2009). However, the most important factor responsible for poor mRNA-protein correlations is the long half-life of proteins relative to mRNAs.…”

Section: Response Of Mrna Levels To Environmental Cuesmentioning

confidence: 99%

Sizing up metatranscriptomics

et al. 2012

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A typical marine bacterial cell in coastal seawater contains only B200 molecules of mRNA, each of which lasts only a few minutes before being degraded. Such a surprisingly small and dynamic cellular mRNA reservoir has important implications for understanding the bacterium's responses to environmental signals, as well as for our ability to measure those responses. In this perspective, we review the available data on transcript dynamics in environmental bacteria, and then consider the consequences of a small and transient mRNA inventory for functional metagenomic studies of microbial communities.

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Section: Response Of Mrna Levels To Environmental Cuesmentioning

confidence: 99%

Sizing up metatranscriptomics

et al. 2012

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“…It is well known that σ 2 d vanishes like η in the thermodynamic limit [4,10,12]. The switching term σ 2 s accounts for fluctuations transmitted from discrete variables to continuous variables, covering situations described as the bursting or the telegraph scenario [1]. The ω expansion [4] overlooks or underestimates this term because this expansion is valid only when all the species are in large numbers.…”

Section: Time Scales and Effective Fokker-planck Equationmentioning

confidence: 99%

“…Expression "noise" is a critical, biologically relevant property of genetic circuits in both microbial and eukaryotic cells [1]. Many theoretical and experimental works underline the importance of network architecture and feedback for shaping gene expression noise [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Relating network rigidity, time scale hierarchies, and expression noise in gene networks

Radulescu

Innocentini²,

Hornos³

2012

Phys. Rev. E

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Fluctuation-dissipation theorems can be used to predict characteristics of noise from characteristics of the macroscopic response of a system. In the case of gene networks, feedback control determines the "network rigidity," defined as resistance to slow external changes. We propose an effective Fokker-Planck equation that relates gene expression noise to topology and to time scales of the gene network. We distinguish between two situations referred to as normal and inverted time hierarchies. The noise can be buffered by network feedback in the first situation, whereas it can be topology independent in the latter.

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“…(19) in the main text The single expression for the contributions from promoter activation are given by…”

Section: Asymptotic Analysis Of the Partial Contributionsmentioning

confidence: 99%

“…Stochasticity or noise inherent to gene expression seems to be one of the main driving forces for the observed cell-to-cell variability in several experiments which have measured the variance in protein abundances in different cellular systems (4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 18). Considerable confusion stems from diverging experimental results which have identified different origins for the main contribution to gene expression noise (19) such that a complete picture is still missing. For prokaryotes, translational efficiency was identified as the main source of variability of expression levels consistent with a stochastic model in which proteins are produced in sharp and random bursts (20).…”

Section: Introductionmentioning

confidence: 99%

Signatures of gene expression noise in cellular systems

Rausenberger

Fleck

Timmer

et al. 2009

Progress in Biophysics and Molecular Biology

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Noise in gene expression, either due to inherent stochasticity or to varying inter-and intracellular environment, can generate significant cell-to-cell variability of protein levels in clonal populations. We present a theoretical framework, based on stochastic processes, to quantify the different sources of gene expression noise taking cell division explicitly into account. Analytical, time-dependent solutions for the noise contributions arising from the major steps involved in protein synthesis are derived.The analysis shows that the induction level of the activator or transcription factor is crucial for the characteristic signature of the dominant source of gene expression noise and thus bridges the gap between seemingly contradictory experimental results. Furthermore, on the basis of experimentally measured cell distributions, our simulations suggest that transcription factor binding and promoter activation can be modelled independently of each other with sufficient accuracy.

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Stochastic gene expression: from single molecules to the proteome

Cited by 159 publications

References 41 publications

Sizing up metatranscriptomics

Sizing up metatranscriptomics

Relating network rigidity, time scale hierarchies, and expression noise in gene networks

Signatures of gene expression noise in cellular systems

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