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Related Concept Videos

Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
Diversity in Cell Signaling Responses01:22

Diversity in Cell Signaling Responses

The physiological function of a cell and cellular communication are outcomes of a range of extrinsic signals, intracellular signaling pathways, and cellular responses. No two cell types express the same repertoire of signaling components. Receptors are highly selective for their cognate ligands, but once activated, they can alter multiple cellular processes such as DNA transcription, protein synthesis, and metabolic activity. 
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Constitutive and Regulated Gene Expression01:27

Constitutive and Regulated Gene Expression

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Chromatin Position Affects Gene Expression

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Moment-based inference predicts bimodality in transient gene expression.

Christoph Zechner1, Jakob Ruess, Peter Krenn

  • 1Automatic Control Laboratory, ETH Zurich, CH-8092 Zurich, Switzerland.

Proceedings of the National Academy of Sciences of the United States of America
|May 9, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for analyzing molecular noise in cellular processes using low-order moments from distribution measurements. This approach enables accurate parameter estimation in complex stochastic systems, even with cell-to-cell variability.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Biophysics

Background:

  • Molecular noise in cellular processes contains valuable information about system dynamics and parameters.
  • Stochastic models are essential for accessing this information but are computationally challenging to analyze.
  • Current parameter estimation methods for stochastic systems are limited to small, simple models, especially when using distribution measurements like flow cytometry.

Purpose of the Study:

  • To develop a novel method for parameter estimation in stochastic systems using low-order moments of distribution measurements.
  • To enable analysis of more complex, realistic biological systems beyond current limitations.
  • To incorporate cell-to-cell variability into the analysis, removing the need for a homogeneous population assumption.

Main Methods:

  • Utilized low-order moments (mean and variance) of measured distributions to retain essential information for parameter estimation.
  • Developed a method applicable to systems of realistic size, overcoming limitations of traditional stochastic model analysis.
  • Incorporated cell-to-cell variability into the analytical framework, addressing heterogeneity in biological populations.

Main Results:

  • Demonstrated the method's efficacy using synthetic data from a gene expression model.
  • Successfully calibrated a stochastic model using time-lapsed flow cytometry data of yeast's osmo-stress response.
  • Showed that mean and variance measurements are sufficient for parameter determination, even for non-ideal distributions like bimodal ones.

Conclusions:

  • The proposed method effectively estimates parameters in stochastic biological systems using accessible distribution measurements.
  • This approach significantly expands the applicability of stochastic modeling to larger, more complex biological processes.
  • The ability to account for cell-to-cell variability enhances the biological relevance and accuracy of model parameterization.