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Related Concept Videos

What is Gene Expression?01:42

What is Gene Expression?

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Overview
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.
Genetic Information Flows from DNA to RNA to Protein
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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What is Gene Expression?01:36

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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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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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Chromatin Position Affects Gene Expression02:35

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Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
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mRNA Stability and Gene Expression02:51

mRNA Stability and Gene Expression

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The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
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Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells
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Predicting growth rate from gene expression.

Thomas P Wytock1, Adilson E Motter2,3,4

  • 1Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208.

Proceedings of the National Academy of Sciences of the United States of America
|December 23, 2018
PubMed
Summary
This summary is machine-generated.

Researchers developed a machine learning model to predict microbial growth rates from gene expression data. This model accurately forecasts growth rate changes, aiding in optimizing antibiotic efficacy and bioreactor productivity.

Keywords:
biological networksdata sciencemachine learningmetabolic networkssystems biology

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Area of Science:

  • Microbiology
  • Systems Biology
  • Bioinformatics

Background:

  • Microbial growth rate is a crucial phenotypic trait influencing population dynamics and survival.
  • Understanding the link between genetic changes and growth rate is vital for applications like antibiotic development and bioprocessing.
  • Directly mapping gene expression to growth rate consequences is a significant challenge in microbial research.

Purpose of the Study:

  • To develop a predictive model linking transcriptional profiles to microbial growth rates.
  • To identify key genes and pathways that significantly influence growth rate.
  • To provide a framework for rationally designing experiments to optimize microbial growth.

Main Methods:

  • Collected and integrated published gene-expression data from Escherichia coli and Saccharomyces cerevisiae with corresponding growth rate measurements.
  • Employed a machine-learning approach, specifically k-nearest-neighbors regression, to build the predictive model.
  • Utilized feature selection and model sparsification techniques to reduce dimensionality while maintaining predictive power.

Main Results:

  • The model successfully predicted 81% of the variance in growth rate for E. coli, reducing over 4,000 features to just 9.
  • For S. cerevisiae, the model explained 89% of the growth rate variance, with a reduction from over 5,500 dimensions to 18.
  • Demonstrated a strong correlation between transcriptional profiles and actual growth rates in both model organisms.

Conclusions:

  • A robust machine learning model can accurately predict microbial growth rates from gene expression data.
  • This approach significantly reduces the complexity of analyzing high-dimensional genomic data for growth rate prediction.
  • The developed model serves as a powerful tool for selecting optimal experimental strategies to enhance microbial growth and related applications.