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What is Gene Expression?

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Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

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Processing of Primary Brain Tumor Tissue for Stem Cell Assays and Flow Sorting

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Published on: September 25, 2012

Inferring single-cell gene expression mechanisms using stochastic simulation.

Bernie J Daigle¹, Mohammad Soltani¹, Linda R Petzold¹

¹Institute for Collaborative Biotechnologies, University of California, Santa Barbara, CA 93106, Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716 and Department of Computer Science, University of California, Santa Barbara, CA 93106, USA.

Bioinformatics (Oxford, England)

|January 10, 2015

Summary

This summary is machine-generated.

Gene expression noise arises from promoter switching. We developed a new method, bursty MCEM(2), to model complex promoter states and analyze gene expression bursting with Weibull distributions.

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Area of Science:

Computational Biology
Molecular Biology
Genetics

Background:

Stochastic promoter switching between active (ON) and inactive (OFF) states is a key source of noise in gene expression.
The assumption of memoryless (exponentially distributed) ON/OFF times may oversimplify complex transcriptional regulation.
Non-exponential promoter ON/OFF times suggest intricate regulatory architectures requiring advanced computational tools.

Purpose of the Study:

To develop efficient computational techniques for characterizing complex promoter architectures.
To model non-exponential promoter ON/OFF times using Weibull distributions.
To infer promoter state configurations from single-cell gene expression data.

Main Methods:

Developed a novel model reduction for promoters with multiple ON/OFF states, approximating behavior with Weibull-distributed ON/OFF times.
Created bursty Monte Carlo expectation-maximization with modified cross-entropy method (bursty MCEM(2)) for parameter estimation and model selection.
Applied bursty MCEM(2) to analyze single-cell gene expression data, inferring promoter state dynamics.

Main Results:

The novel model reduction effectively approximates complex promoter switching with Weibull distributions.
Bursty MCEM(2) efficiently infers the number and configuration of promoter states from gene expression data.
Analysis of the mouse glutaminase promoter revealed nearly deterministic OFF times, suggesting a multi-step inactivation mechanism with at least 10 states.

Conclusions:

The developed model reduction and bursty MCEM(2) offer powerful tools for characterizing transcriptional bursting.
These methods enable a deeper understanding of gene expression noise and regulatory mechanisms.
The findings highlight the complexity of promoter dynamics and provide a framework for future investigations.