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Related Concept Videos

Cooperative Binding of Transcription Regulators02:13

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Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Transcriptional Network Growing Models Using Motif-Based Preferential Attachment.

Ahmed F Abdelzaher1, Ahmad F Al-Musawi2, Preetam Ghosh1

  • 1Biological Networks Laboratory, Department of Computer Science, Virginia Commonwealth University , Richmond, VA , USA.

Frontiers in Bioengineering and Biotechnology
|November 4, 2015
PubMed
Summary
This summary is machine-generated.

New models construct gene-regulatory networks (GRNs) using transcriptional motifs as building blocks, improving upon node-by-node methods. This approach better reflects motif distributions in organisms like E. coli.

Keywords:
attachment kerneldegree distributionmotifpower-lawtranscriptional network

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Area of Science:

  • Systems biology
  • Bioinformatics
  • Network science

Background:

  • Gene-regulatory networks (GRNs) are crucial for understanding biological processes, disease dynamics, and drug development.
  • GRNs exhibit scale-free properties and network motifs, which are small subgraphs occurring frequently.
  • Network motifs are considered fundamental building blocks contributing to GRN robustness and stability.

Purpose of the Study:

  • To develop novel network-construction models for gene-regulatory networks.
  • To investigate the relationship between architectural properties and network motifs in GRNs.
  • To improve the fidelity of simulated GRNs to observed motif distributions.

Main Methods:

  • Developed new network-construction models using transcriptional motifs as the fundamental growth unit.
  • Generated randomized GRNs based on specified transcriptional motifs (e.g., feed-forward loops).
  • Validated models by comparing motif and degree distributions against existing models and data from Escherichia coli.

Main Results:

  • The new models produce GRNs with a controlled lower bound on specific transcriptional motifs.
  • Resultant motif and degree distributions showed improved fidelity compared to existing node-by-node construction methods.
  • The models successfully replicated motif distributions observed in the model organism E. coli.

Conclusions:

  • The developed network-construction models offer a more accurate representation of GRN architecture.
  • These models serve as improved testbeds for studying the link between transcriptional motif topology and network dynamics.
  • Findings advance systems biology by providing better tools to understand GRN organization and function.