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Regulation of Expression at Multiple Steps

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...

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Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast.

Alexandra M Poos¹, André Maicher², Anna K Dieckmann³

¹Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, D-07747 Jena, Erlanger Allee 101, Germany Network Modeling, Leibniz Institute for Natural Product Research and Infection Biology-Hans Knöll Institute (HKI) Jena, Beutenbergstrasse 11a, 07745 Jena, Germany Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, 69120 Heidelberg, Germany.

Nucleic Acids Research

|February 25, 2016

Summary

This summary is machine-generated.

This study identifies novel regulators of telomerase gene expression in yeast, crucial for cancer cell immortalization. Machine learning identified key transcription factors like Sum1, Hst1, and Srb2 impacting telomere length maintenance.

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Area of Science:

Molecular Biology
Cancer Biology
Systems Biology

Background:

Telomere length maintenance is crucial for cancer cell immortalization.
Transcriptional regulation of telomerase genes is vital for controlling telomere length.
Dysregulation of telomere maintenance is a hallmark of cancer.

Purpose of the Study:

To identify novel regulatory interactions controlling telomerase gene expression.
To explain discrepancies in telomerase transcript levels in yeast mutants.
To develop a machine learning approach for analyzing gene regulation.

Main Methods:

Integrated Mixed Integer Linear Programming (MILP) models with a comparative machine learning approach.
Analyzed telomerase transcript levels in yeast mutants with deleted regulators.
Utilized gene regulator binding information and expression profiles.

Main Results:

Uncovered novel regulators of telomerase expression, including those affecting histone modifications.
Identified transcription factors Sum1, Hst1, and Srb2 as important for EST1 transcription regulation.
Experimentally validated the role of Sum1 in regulating telomerase transcription.

Conclusions:

The study provides new insights into the transcriptional control of telomere length maintenance.
A user-friendly R package was developed for applying this machine learning method to similar biological problems.
This approach facilitates the analysis of gene regulation across different conditions and phenotypes.