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Turing's model for biological pattern formation and the robustness problem.

Philip K Maini1, Thomas E Woolley, Ruth E Baker

  • 1Centre for Mathematical Biology, Mathematical Institute, University of Oxford, 24-29 St Giles', Oxford OX1 3PN, UK ; Oxford Centre for Integrative Systems Biology, Department of Biochemistry, University of Oxford, South Parks Road OX1 3QU, UK.

Interface Focus
|August 7, 2013
PubMed
Summary
This summary is machine-generated.

This study explores Turing

Keywords:
Turingbiological pattern formationrobustness problem

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Area of Science:

  • Developmental Biology
  • Theoretical Biology
  • Systems Biology

Background:

  • Understanding pattern formation from homogeneous beginnings is a core challenge in developmental biology.
  • Biological systems exhibit remarkable robustness against noise, a mechanism that remains poorly understood.
  • Generating reproducible biological patterns requires addressing perturbations in various parameters.

Purpose of the Study:

  • To review the fundamental properties of Turing's reaction-diffusion theory.
  • To evaluate the successes and limitations of applying Turing's theory to biological systems.
  • To discuss recent advancements in modeling biological pattern formation and robustness.

Main Methods:

  • Review of established Turing reaction-diffusion models.
  • Analysis of model applicability to biological heterogeneity.
  • Discussion of theoretical frameworks for biological robustness.

Main Results:

  • Turing's theory provides a foundational framework for understanding pattern formation.
  • The application of Turing's theory to biological systems faces challenges in explaining robustness and reproducibility.
  • Emerging developments offer new perspectives on modeling complex biological patterns.

Conclusions:

  • Turing's theory is a valuable but incomplete model for biological pattern formation.
  • Further theoretical development is needed to fully explain biological robustness and heterogeneity.
  • Continued research integrating theoretical models with experimental data is crucial.