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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Driving developmental and evolutionary change: A systems biology view.

Jonathan Bard1

  • 1Department of Physiology, Anatomy & Genetics, University of Oxford, UK. j.bard@ed.ac.uk

Progress in Biophysics and Molecular Biology
|October 23, 2012
PubMed
Summary

Embryonic development relies on core processes linking genotype to phenotype. Modeling these processes as mathematical graphs aids understanding of developmental changes and evolutionary modifications.

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Area of Science:

  • Developmental Biology
  • Evolutionary Genetics
  • Systems Biology

Background:

  • Embryonic development involves core processes like morphogenesis, growth, patterning, and differentiation.
  • These processes are outputs of complex molecular networks, linking genotype to phenotype.
  • Understanding these networks is crucial for explaining biological change.

Purpose of the Study:

  • To explore the implications of viewing embryonic development through the lens of core processes.
  • To investigate how mathematical graph models can represent developmental changes.
  • To clarify evolutionary genetics concepts by relating them to molecular networks.

Main Methods:

  • Modeling developmental changes using mathematical graphs ( triplets).
  • Analyzing the effects of mutations on process dynamics and congenital abnormalities.
  • Connecting protein networks to genetic elements in evolutionary genetics.

Main Results:

  • Developmental changes can be modeled as mathematical graphs, simplifying complex phenomena.
  • Mutations altering process dynamics can lead to inherited, advantageous evolutionary modifications.
  • Protein networks represent genes, and traits reflect complex network outputs, resolving evolutionary synthesis challenges.

Conclusions:

  • Viewing development via core processes and network outputs offers a parsimonious link between genotype and phenotype.
  • Mathematical graph models can elucidate multi-level complexity in development and guide experiments.
  • This network-centric perspective clarifies evolutionary genetics and the basis of inherited traits.