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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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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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Computational challenges in modeling gene regulatory events.

Abhijeet Pataskar1, Vijay K Tiwari1

  • 1a Institute of Molecular Biology (IMB) , Mainz , Germany.

Transcription
|July 9, 2016
PubMed
Summary
This summary is machine-generated.

Computational biology integrates omics data to model gene regulation. This article highlights current computational challenges in molecular biology research.

Keywords:
epigenetic mechanismsgene regulationsystems biologytranscriptiontranscription factors

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Area of Science:

  • Molecular biology
  • Computational biology
  • Genomics
  • Epigenetics

Background:

  • Cellular transcriptional programs are complex, influenced by genetic and epigenetic factors.
  • Understanding these programs requires integrating diverse 'omics' data.
  • Gene regulatory events are key to cellular function and development.

Purpose of the Study:

  • To provide an exemplified account of current computational challenges in molecular biology.
  • To emphasize the need for computational biology to advance alongside experimental techniques.
  • To bridge the gap between 'omics' data and mechanistic understanding of gene regulation.

Main Methods:

  • Review and exemplification of computational approaches.
  • Integration of multi-omics datasets.
  • Modeling of gene regulatory networks.

Main Results:

  • Identification of key computational bottlenecks in analyzing complex biological data.
  • Demonstration of challenges in translating experimental data into predictive models.
  • Highlighting the need for advanced algorithms and scalable computational infrastructure.

Conclusions:

  • Computational biology plays a crucial role in deciphering cellular transcriptional programs.
  • Addressing current computational challenges is essential for future advancements in molecular biology.
  • Closer integration of experimental and computational methodologies will accelerate discovery.