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The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is also...
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Related Experiment Video

Updated: May 23, 2026

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
09:56

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Published on: April 30, 2019

Computational approaches to developmental patterning.

Luis G Morelli1, Koichiro Uriu, Saúl Ares

  • 1Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.

Science (New York, N.Y.)
|April 14, 2012
PubMed
Summary
This summary is machine-generated.

Computational methods and precise embryo measurements are advancing our understanding of embryonic development. This integration reveals insights into classic and new patterning strategies for developmental biology.

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Last Updated: May 23, 2026

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Published on: October 17, 2011

Area of Science:

  • Developmental Biology
  • Computational Biology
  • Embryogenesis

Background:

  • Understanding embryonic development is crucial for developmental biology.
  • Classic theories of embryonic patterning include signaling gradients, activator-inhibitor systems, and coupled oscillators.
  • Emerging paradigms like tissue deformation are also key to understanding embryo formation.

Purpose of the Study:

  • To discuss recent studies integrating precise embryonic measurements with computational modeling.
  • To investigate classic and emerging embryonic patterning strategies.
  • To highlight the role of computational approaches in developmental biology.

Main Methods:

  • Coupling precise measurements in embryos with computational modeling.
  • Analyzing signaling gradients, activator-inhibitor systems, and coupled oscillators.
  • Investigating novel paradigms such as tissue deformation.

Main Results:

  • Computational approaches offer new insights into embryonic patterning.
  • Integration of experimental data and computational models enhances understanding.
  • Identified key mechanisms in signaling gradients, oscillations, and tissue dynamics.

Conclusions:

  • Parallel progress in theory and experiment is essential for deciphering developmental patterning.
  • Computational methods are becoming indispensable tools in embryogenesis research.
  • A combined approach of precise measurement and modeling will drive future discoveries in developmental biology.