Quantifying how post-transcriptional noise and gene copy number variation bias transcriptional parameter inference from mRNA distributions

eLife, Journal Year: 2022, Volume and Issue: 11

Published: Oct. 17, 2022

Transcriptional rates are often estimated by fitting the distribution of mature mRNA numbers measured using smFISH (single molecule fluorescence in situ hybridization) with predicted telegraph model gene expression, which defines two promoter states activity and inactivity. However, fluctuations strongly affected processes downstream transcription. In addition, assumes one copy but experiments, cells may have copies as replicate their genome during cell cycle. While it is presumed that post-transcriptional noise number variation affect transcriptional parameter estimation, size error introduced remains unclear. To address this issue, here we measure both nascent distributions GAL10 yeast classify each according to its cycle phase. We infer parameters from distributions, without accounting for phase compare results live-cell transcription measurements same gene. find that: (i) correcting dynamics decreases switching initiation rate, increases fraction time spent active state, well burst size; (ii) additional correction leads further a large reduction errors estimation. Furthermore, outline how correctly adjust measurement due uncertainty site localisation when introns cannot be labelled. Simulations data, corrected phases noise, autocorrelation functions agree those obtained imaging.

Language: Английский

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A comprehensive review of computational cell cycle models in guiding cancer treatment strategies

npj Systems Biology and Applications, Journal Year: 2024, Volume and Issue: 10(1)

Published: July 5, 2024

Abstract This article reviews the current knowledge and recent advancements in computational modeling of cell cycle. It offers a comparative analysis various paradigms, highlighting their unique strengths, limitations, applications. Specifically, compares deterministic stochastic models, single-cell versus population mechanistic abstract models. detailed helps determine most suitable framework for research needs. Additionally, discussion extends to utilization these models illuminate cycle dynamics, with particular focus on viability, crosstalk signaling pathways, tumor microenvironment, DNA replication, repair mechanisms, underscoring critical roles progression optimization cancer therapies. By applying crucial aspects therapy planning better outcomes, including drug efficacy quantification, discovery, resistance analysis, dose optimization, review highlights significant potential insights enhancing precision effectiveness treatments. emphasis intricate relationship between therapeutic strategy development underscores pivotal role advanced techniques navigating complexities dynamics implications therapy.

Language: Английский

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Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations

Journal of The Royal Society Interface, Journal Year: 2021, Volume and Issue: 18(178), P. 20210274 - 20210274

Published: May 1, 2021

The chemical master equation and the Gillespie algorithm are widely used to model reaction kinetics inside living cells. It is thereby assumed that cell growth division can be modelled through effective dilution reactions extrinsic noise sources. We here re-examine these paradigms developing an analytical agent-based framework of growing dividing cells accompanied by exact simulation algorithm, which allows us quantify dynamics virtually any intracellular network affected stochastic size control noise. find solution equation—including static noise—exactly agrees with formulation when under study exhibits concentration homeostasis , a novel condition generalizes in deterministic systems higher order moments distributions. illustrate for range common gene expression networks. When this not met, we demonstrate extending linear approximation models dependence on qualitatively deviate from equation. Surprisingly, total approach still well approximated models.

Language: Английский

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Approximating solutions of the Chemical Master equation using neural networks

iScience, Journal Year: 2022, Volume and Issue: 25(9), P. 105010 - 105010

Published: Aug. 28, 2022

Language: Английский

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Modeling bursty transcription and splicing with the chemical master equation

Biophysical Journal, Journal Year: 2022, Volume and Issue: 121(6), P. 1056 - 1069

Published: Feb. 7, 2022

Language: Английский

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Coupling gene expression dynamics to cell size dynamics and cell cycle events: Exact and approximate solutions of the extended telegraph model

iScience, Journal Year: 2022, Volume and Issue: 26(1), P. 105746 - 105746

Published: Dec. 7, 2022

The standard model describing the fluctuations of mRNA numbers in single cells is telegraph which includes synthesis and degradation mRNA, switching gene between active inactive states. While commonly used, this does not describe how are influenced by cell cycle phase, cellular growth division, other crucial aspects biology. Here, we derive analytical time-dependent solution an extended that explicitly considers doubling copy upon DNA replication, dependence rate on volume, dosage compensation, partitioning molecules during cell-cycle duration variability, cell-size control strategies. Based solution, obtain distributions transcript for lineage population measurements steady-state also find a linear relation Fano factor volume fluctuations. We show generally cannot be accurately approximated extrinsic noise models, i.e. with parameters drawn from probability distributions. This because lifetime often small enough compared to erase memory division replication. Accurate approximations possible when weak, e.g. genes bursty expression there sufficient compensation replication occurs.

Language: Английский

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Concentration fluctuations in growing and dividing cells: Insights into the emergence of concentration homeostasis

PLoS Computational Biology, Journal Year: 2022, Volume and Issue: 18(10), P. e1010574 - e1010574

Published: Oct. 4, 2022

Intracellular reaction rates depend on concentrations and hence their levels are often regulated. However classical models of stochastic gene expression lack a cell size description cannot be used to predict noise in concentrations. Here, we construct model product dynamics that includes growth, division, size-dependent expression, dosage compensation, control mechanisms can vary with the cycle phase. We obtain expressions for approximate distributions power spectra concentration fluctuations which lead insight into emergence homeostasis. find (i) conditions necessary suppress division-induced oscillations difficult achieve; (ii) mRNA number have different modes; (iii) two-layer strategies such as sizer-timer or adder-timer ideal because they maintain constant mean whilst minimising noise; (iv) accurate homeostasis requires fine tuning replication timing, expression; (v) deviations from perfect show up distribution gamma distribution. Some these predictions confirmed using data E. coli, fission yeast, budding yeast.

Language: Английский

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Studying stochastic systems biology of the cell with single-cell genomics data

Cell Systems, Journal Year: 2023, Volume and Issue: 14(10), P. 822 - 843.e22

Published: Sept. 25, 2023

Language: Английский

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Cell size distribution of lineage data: Analytic results and parameter inference

iScience, Journal Year: 2021, Volume and Issue: 24(3), P. 102220 - 102220

Published: Feb. 24, 2021

Recent advances in single-cell technologies have enabled time-resolved measurements of the cell size over several cycles. These data encode information on how cells correct aberrations so that they do not grow abnormally large or small. Here, we formulate a piecewise deterministic Markov model describing evolution many generations, for all three homeostasis strategies (timer, sizer, and adder). The is solved to obtain an analytical expression non-Gaussian distribution lineage; theory used understand shape influenced by parameters controlling dynamics cycle choice tracking protocol. theoretical found provide excellent match experimental E. coli lineage collected under various growth conditions.

Language: Английский

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Pathway dynamics can delineate the sources of transcriptional noise in gene expression

eLife, Journal Year: 2021, Volume and Issue: 10

Published: Oct. 12, 2021

Single-cell expression profiling opens up new vistas on cellular processes. Extensive cell-to-cell variability at the transcriptomic and proteomic level has been one of stand-out observations. Because most experimental analyses are destructive we only have access to snapshot data states. This loss temporal information presents significant challenges for inferring dynamics, as well causes variability. In particular, typically cannot separate dynamic from within cells (‘intrinsic noise’) across population (‘extrinsic noise’). Here, make this non-identifiability mathematically precise, allowing us identify set-ups that can assist in resolving non-identifiability. We show multiple generic reporters same biochemical pathways (e.g. mRNA protein) infer magnitudes intrinsic extrinsic transcriptional noise, identifying sources heterogeneity. Stochastic simulations support our theory, demonstrate ‘pathway-reporters’ compare favourably well-known, but often difficult implement, dual-reporter method.

Language: Английский

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