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Related Concept Videos

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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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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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Updated: Dec 18, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Predicting Evolutionary Constraints by Identifying Conflicting Demands in Regulatory Networks.

Manjunatha Kogenaru1, Philippe Nghe2, Frank J Poelwijk3

  • 1AMOLF, Science Park 104, Amsterdam 1098 XG, the Netherlands; Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.

Cell Systems
|June 20, 2020
PubMed
Summary

Gene regulation networks evolve to adapt to new environments. Partial order analysis predicts evolutionary constraints by ranking network outputs, guiding adaptation strategies and revealing how networks change structure or fine-tune components to improve fitness.

Keywords:
Pareto frontconstraint predictionexperimental evolutiongenetic networkpartial orderregulatory conflictvariable environments

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Area of Science:

  • Systems Biology
  • Evolutionary Biology
  • Synthetic Biology

Background:

  • Organisms adapt to environmental niches using gene regulation networks.
  • The evolutionary constraints on gene regulation network evolution are not well understood.
  • Partial order analysis offers a novel approach to identify these constraints.

Purpose of the Study:

  • To investigate the evolutionary constraints on gene regulation networks.
  • To test the predictive power of partial order analysis in experimental evolution.
  • To understand how engineered networks adapt to different environments.

Main Methods:

  • Developed and applied partial order analysis to predict evolutionary constraints.
  • Experimentally evolved an engineered signal-integrating network in multiple environments.
  • Analyzed population fitness expansion and changes in network components and structure.

Main Results:

  • Populations expanded fitness along the Pareto-optimal front by fine-tuning binding affinities.
  • Populations also expanded beyond the Pareto-optimal front via alterations in network structure.
  • Partial order predictions were validated without needing network architecture or genetic details.

Conclusions:

  • Partial order analysis effectively identifies evolutionary constraints on gene regulation.
  • Evolutionary adaptation involves both fine-tuning existing network components and structural changes.
  • Understanding current regulatory phenotypes can predict future evolutionary trajectories.