scholarly journals Population growth affects intrinsic and extrinsic noise in gene expression

2018 ◽  
Author(s):  
Philipp Thomas
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Intrinsic Noise ◽  
Integrated Approach ◽  
Time Lapse ◽  
Biochemical Networks ◽  
Intrinsic Variability ◽  
Extrinsic Noise ◽  
Growing Population ◽  
Intrinsic And Extrinsic Noise

Clonal cells of exponentially growing populations vary substantially from cell to cell. The main drivers of this heterogeneity are the population dynamics and stochasticity in the intracellular reactions, which are commonly studied separately. Here we develop an agent-based framework that allows tracking of the biochemical dynamics in every single cell of a growing population that accounts for both of these factors. Apart from the common intrinsic variability of the biochemical reactions, the framework also predicts extrinsic noise arising from fluctuations in the histories of cells without the need to introduce fluctuating rate constants. Instead, these extrinsic fluctuations are explained by cell cycle fluctuations and differences in cell age, which are ubiquitously observed in growing populations. We give explicit formulas to quantify mean molecule numbers, intrinsic and extrinsic noise statistics as measured in two-colour experiments. We find that these statistics may differ significantly depending on the experimental setup used to observe the cells. We illustrate this fact using (i) averages over an isolated cell lineage tracked over many generations as observed in the mother machine, (ii) snapshots of a growing population with known cell ages as recorded in time-lapse microscopy, and (iii) snapshots of unknown cell ages as measured from static images. Our integrated approach applies to arbitrary biochemical networks and generation time distributions. By employing models of stochastic gene expression and feedback regulation, we elucidate that isolated lineages, as compared to snapshot data, can significantly overestimate the mean number of molecules, overestimate extrinsic noise but underestimate intrinsic noise and have qualitatively different sensitivities to cell cycle fluctuations.

scholarly journals Inferring intrinsic and extrinsic noise from a dual fluorescent reporter

2016 ◽  
Author(s):  
Erik van Nimwegen
Keyword(s):  
Gene Expression ◽  
Bayesian Analysis ◽  
Ad Hoc ◽  
Intrinsic Noise ◽  
Extrinsic Noise ◽  
Fluorescent Reporter ◽  
Gene Expression Noise ◽  
Total Gene Expression ◽  
Using Data ◽  
Intrinsic And Extrinsic Noise

AbstractDual fluorescent reporter constructs, which measure gene expression from two identical promoters within the same cell, allow total gene expression noise to be decomposed into an extrinsic component, roughly associated with cell-to-cell fluctuations in cellular component concentrations, and intrinsic noise, roughly associated with inherent stochasticity of the biochemical reactions involved in gene expression [1]. A recent paper by Fu and Pachter presented frequentist statistical estimators for intrinsic and extrinsic noise using data from dual reporters [2]. For comparison, I here present results of a Bayesian analysis of this problem. I show that the orthodox estimators suffer from pathologies such as predicting negative values for a manifestly non-negative quantity, i.e. variance, and show that the Bayesian estimators do not suffer from such pathologies. In addition, I show that the Bayesian analysis automatically identifies that optimal estimates of intrinsic and extrinsic noise depend on a subtle combination of two statistics of the data, allowing for accuracies that are up to twice the accuracy of the orthodox estimators in some parameter regimes.I hope up this little worked out example contrasting orthodox statistical analysis based on ad hoc estimators with estimators resulting from a Bayesian analysis, will be educational for others in the field. I distribute a Mathematica Notebook with this paper that allows users to easily reproduce all results and figures of the paper.

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 Integrated time-lapse and single-cell transcription studies highlight the variable and dynamic nature of human hematopoietic cell fate commitment

2017 ◽  
Author(s):  
Alice Moussy ◽  
Jérémie Cosette ◽  
Romuald Parmentier ◽  
Cindy da Silva ◽  
Guillaume Corre ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Cell Fate ◽  
Morphological Changes ◽  
Gene Expression Pattern ◽  
Time Lapse ◽  
Dynamic Nature ◽  
Transcriptional Changes ◽  
Fate Decision

AbstractIndividual cells take lineage commitment decisions in a way that is not necessarily uniform. We address this issue by characterizing transcriptional changes in cord blood derived CD34+ cells at the single-cell level and integrating data with cell division history and morphological changes determined by time-lapse microscopy. We show, that major transcriptional changes leading to a multilineage-primed gene expression state occur very rapidly during the first cell cycle. One of the two stable lineage-primed patterns emerges gradually in each cell with variable timing. Some cells reach a stable morphology and molecular phenotype by the end of the first cell cycle and transmit it clonally. Others fluctuate between the two phenotypes over several cell cycles. Our analysis highlights the dynamic nature and variable timing of cell fate commitment in hematopoietic cells, links the gene expression pattern to cell morphology and identifies a new category of cells with fluctuating phenotypic characteristics, demonstrating the complexity of the fate decision process, away from a simple binary switch between two options as it is usually envisioned.

scholarly journals Intrinsic and extrinsic noise are distinguishable in a synthesis – export – degradation model of mRNA production

2020 ◽  
Author(s):  
Gennady Gorin ◽  
Lior Pachter
Keyword(s):  
Gene Expression ◽  
Copy Number ◽  
Downstream Processing ◽  
Mrna Splicing ◽  
Noise Sources ◽  
Extrinsic Noise ◽  
Degradation Model ◽  
Degenerate Cases ◽  
Intrinsic And Extrinsic Noise

AbstractIntrinsic and extrinsic noise sources in gene expression, originating respectively from transcriptional stochasticity and from differences between cells, complicate the determination of transcriptional models. In particularly degenerate cases, the two noise sources are altogether impossible to distinguish. However, the incorporation of downstream processing, such as the mRNA splicing and export implicated in gene expression buffering, recovers the ability to identify the relevant source of noise. We report analytical copy-number distributions, discuss the noise sources’ qualitative effects on lower moments, and provide simulation routines for both models.

scholarly journals Tracking Normal and Chronic Myeloid Leukemia Progenitor Cell Cycle Kinetics in a Defined Niche Using Live Time Lapse Confocal Imaging with Fucci2BL a Novel Lentiviral Bicistronic Reporter

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 1863-1863
Author(s):  
Gabriel Pineda ◽  
Kathleen M Lennon ◽  
Nathaniel P Delos-Santos ◽  
Florence Lambert-Fliszar ◽  
Gennarina L Riso ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Stem Cells ◽  
Myeloid Leukemia ◽  
Research Funding ◽  
Chronic Phase ◽  
Time Lapse ◽  
Therapeutic Resistance ◽  
Cell Cycle Kinetics ◽  
Confocal Fluorescence

Abstract Malignant reprogramming of progenitors into self-renewing cancer stem cells (CSCs) that have a predisposition for dormancy in protective niches has been implicated in therapeutic resistance of chronic myeloid leukemia (CML) and other CSC-driven malignancies. An unmet medical need for developing therapies that target niche dependent dormant human CSCs provides a compelling rationale for identifying key differences in gene expression at different cell cycle phases between normal and malignant progenitors in a CSC-supportive stromal co-culture system. Currently, few methods exist for quantifying cell cycle kinetics in live human leukemia stem cells (LSC). To date, efficient cell cycle transit time analysis in single live human leukemic progenitors derived from primary patient samples has been hampered by 1) decreased cell viability following transfection or transduction, 2) limited sample size, 3) dormancy of primitive progenitor populations thereby necessitating lentiviral rather than retroviral transduction and 4) increased apoptosis in the absence of a supportive microenvironment. To alleviate these challenges and improve transduction efficiency, we generated Fucci2BL, a lentiviral bicistronic reporter vector. Fucci2BL expresses mVenus-hGem(1/110) fused to mCherry-hCdt1(30/120) by the T2A peptide using an EF1 promoter that generates optimal levels of gene expression in progenitors. Initially, the reporter fidelity was characterized in 293A cells using flow cytometry and time-lapse confocal fluorescence microscopy. Time-lapse confocal fluorescence microscopy revealed normal cell morphology and distinct nuclear staining of either green or red fluorescence depending on the cell cycle stage. Once the fidelity of the Fucci2BL reporter was characterized, differences in gene expression levels between normal and malignant progenitors were analyzed. Whole transcriptome RNA-seq analysis revealed both cell cycle and DNA replication pathways were enriched in chronic phase CP (CML) compared to normal progenitors. Cell cycle kinetics between normal and chronic phase (CML) progenitors co-cultured in a niche were also analyzed using the Fucci2BL reporter. Normal progenitor cells on average transited the cell cycle within 26 hours while CP progenitor cells demonstrated a prolongation of transit through G1. In summary, the Fucci2BL system enables single transduction and single cell cycle tracking as well as gene expression changes in live primary progenitors in response to a niche. This robust lentiviral reporter can reproducibly distinguish cell cycle phases thereby providing an opportunity to quantitatively study the contribution of cell cycle kinetics to single cancer stem cell therapeutic resistance and to relapse. Disclosures Jamieson: J&J: Research Funding; GSK: Research Funding.

scholarly journals Limits of noise for autoregulated gene expression

2017 ◽  
Author(s):  
Peter Czuppon ◽  
Peter Pfaffelhuber
Keyword(s):  
Gene Expression ◽  
Negative Feedback ◽  
Positive Feedback ◽  
Fano Factor ◽  
Intrinsic Noise ◽  
Stochastic Averaging ◽  
Jump Processes ◽  
Markov Jump ◽  
Extrinsic Noise ◽  
Cellular Processes

AbstractGene expression is influenced by extrinsic noise (involving a fluctuating environment of cellular processes) and intrinsic noise (referring to fluctuations within a cell under constant environment). We study the standard model of gene expression including an (in-)active gene, mRNA and protein. Gene expression is regulated in the sense that the protein feeds back and either represses (negative feedback) or enhances (positive feedback) its production at the stage of transcription. While it is well-known that negative (positive) feedback reduces (increases) intrinsic noise, we give a precise result on the resulting fluctuations in protein numbers. The technique we use is an extension of the Langevin approximation and is an application of a central limit theorem under stochastic averaging for Markov jump processes (Kang, Kurtz and Popovic, 2014). We find that (under our scaling and in equilibrium), negative feedback leads to a reduction in the Fano factor of at most 2, while the noise under positive feedback is potentially unbounded. The fit with simulations is very good and improves on known approximations.

Comparing the Effects of Intrinsic and Extrinsic Noise on the Vicsek Model in Three Dimensions

2017 ◽  
Author(s):  
Masoud Jahromi Shirazi ◽  
Nicole Abaid
Keyword(s):  
Collective Behavior ◽  
Intrinsic Noise ◽  
Three Dimensions ◽  
Extrinsic Noise ◽  
Vicsek Model ◽  
Fish Schools ◽  
Natural Systems ◽  
Order And Disorder ◽  
Noise Models ◽  
Intrinsic And Extrinsic Noise

A group of simple individuals may show ordered, complex behavior through local interactions. This phenomenon is called collective behavior, which has been observed in a vast variety of natural systems such as fish schools or bird flocks. The Vicsek model is a well-established mathematical model to study collective behavior through interaction of individuals with their neighbors in the presence of noise. How noise is modeled can impact the collective behavior of the group. Extrinsic noise captures uncertainty imposed on individuals, such as noise in measurements, while intrinsic noise models uncertainty inherent to individuals, akin to free will. In this paper, the effects of intrinsic and extrinsic noise on characteristics of the transition between order and disorder in the Vicsek model in three dimensions are studied through numerical simulation.

scholarly journals An Integrated Framework to Model Cellular Phenotype as a Component of Biochemical Networks

2011 ◽  
Vol 2011 ◽  
pp. 1-14
Author(s):  
Michael Gormley ◽  
Viswanadha U. Akella ◽  
Judy N. Quong ◽  
Andrew A. Quong
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Gene Networks ◽  
Computational Models ◽  
Simulation Analysis ◽  
Network Models ◽  
Biochemical Networks ◽  
Phenotypic Data ◽  
Model Cell ◽  
Systematic Identification

Identification of regulatory molecules in signaling pathways is critical for understanding cellular behavior. Given the complexity of the transcriptional gene network, the relationship between molecular expression and phenotype is difficult to determine using reductionist experimental methods. Computational models provide the means to characterize regulatory mechanisms and predict phenotype in the context of gene networks. Integrating gene expression data with phenotypic data in transcriptional network models enables systematic identification of critical molecules in a biological network. We developed an approach based on fuzzy logic to model cell budding in Saccharomyces cerevisiae using time series expression microarray data of the cell cycle. Cell budding is a phenotype of viable cells undergoing division. Predicted interactions between gene expression and phenotype reflected known biological relationships. Dynamic simulation analysis reproduced the behavior of the yeast cell cycle and accurately identified genes and interactions which are essential for cell viability.

scholarly journals Allele-specific single-cell RNA sequencing reveals different architectures of intrinsic and extrinsic gene expression noises

2019 ◽  
Author(s):  
Mengyi Sun ◽  
Jianzhi Zhang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Intrinsic Noise ◽  
Mouse Fibroblast ◽  
Extrinsic Noise ◽  
Gene Expression Noise ◽  
Cell State ◽  
Single Cell Rna Sequencing ◽  
Allele Specific

ABSTRACTGene expression noise refers to the variation of the expression level of a gene among isogenic cells in the same environment, and has two sources: extrinsic noise arising from the disparity of the cell state and intrinsic noise arising from the stochastic process of gene expression in the same cell state. Due to the low throughput of the existing method for measuring the two noise components, the architectures of intrinsic and extrinsic expression noises remain elusive. Using allele-specific single-cell RNA sequencing, we here estimate the two noise components of 3975 genes in mouse fibroblast cells. Our analyses verify predicted influences of several factors such as the TATA-box and microRNA targeting on intrinsic and extrinsic noises and reveal gene function-associated noise trends implicating the action of natural selection. These findings unravel differential regulations, optimizations, and biological consequences of intrinsic and extrinsic noises and can aid the construction of desired synthetic circuits.

scholarly journals PEPSDI: Scalable and flexible inference framework for stochastic dynamic single-cell models

2021 ◽  
Author(s):  
Sebastian Persson ◽  
Niek Welkenhuysen ◽  
Sviatlana Shashkova ◽  
Samuel Wiqvist ◽  
Patrick Reith ◽  
...  
Keyword(s):  
Single Cell ◽  
Intrinsic Noise ◽  
Time Lapse ◽  
Nutrient Sensing ◽  
Model Parameters ◽  
Stochastic Dynamic ◽  
Cell Models ◽  
Extrinsic Noise ◽  
Yeast Saccharomyces Cerevisiae ◽  
Cell To Cell Variability

Mathematical modelling is an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic and extrinsic noise. Here we present PEPSDI, a scalable and flexible framework for Bayesian inference in state-space mixed-effects stochastic dynamic single-cell models. Unlike previous frameworks, PEPSDI imposes a few modelling assumptions when inferring unknown model parameters from time-lapse data. Specifically, it can infer model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. This allowed us to identify hexokinase activity as a source of extrinsic noise, and to deduce that sugar availability dictates cell-to-cell variability in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway.

Sign in / Sign up

Export Citation Format

Share Document