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Evolutionary Relationships through Genome Comparisons

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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Quantifying Abdominal Pigmentation in Drosophila melanogaster
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Published on: June 1, 2017

Predicting phenotypic diversity and the underlying quantitative molecular transitions.

Claudiu A Giurumescu1, Paul W Sternberg, Anand R Asthagiri

  • 1Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, United States of America.

Plos Computational Biology
|April 11, 2009
PubMed
Summary

Quantitative changes in developmental signaling networks can generate hundreds of multicellular phenotypes, revealing insights into evolution and development. This computational approach predicts novel phenotypes for experimental validation.

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Area of Science:

  • Developmental biology
  • Computational biology
  • Systems biology

Background:

  • Signaling networks orchestrate multicellular pattern formation during development.
  • The impact of quantitative fluctuations in these networks on multicellular phenotypes is not well understood.

Purpose of the Study:

  • To develop a computational framework for predicting and analyzing phenotypic diversity in developmental signaling networks.
  • To apply this framework to vulval development in *C. elegans* and explore evolutionary implications.

Main Methods:

  • Developed a computational approach to predict and analyze phenotypic diversity accessible to developmental signaling networks.
  • Applied the framework to *C. elegans* vulval development, assessing phenotype robustness to parameter perturbations.
  • Modeled the landscape of multicellular phenotypes in network parameter space.

Main Results:

  • Identified approximately 500 distinct multicellular phenotypes accessible through quantitative changes in the *C. elegans* vulval development network.
  • Discovered novel phenotypes and demonstrated that established mutant phenotypes can arise from non-canonical network perturbations.
  • Showed that the model can generate phenotypes observed in *C. elegans* and other *Caenorhabditis* species, suggesting quantitative network changes drive interspecies phenotypic differences.

Conclusions:

  • Quantitative variations in regulatory networks, without altering core architecture, are sufficient to generate significant phenotypic diversity.
  • The study provides a systematic method to trace quantitative regulatory changes during species evolution.
  • The computational framework aids in identifying promising phenotypes for experimental validation and understanding evolutionary diversification.