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Robotic Sensing and Stimuli Provision for Guided Plant Growth
08:02

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Published on: July 1, 2019

Joining forces: feedback and integration in plant development.

Kirsten ten Tusscher1, Ben Scheres

  • 1Theoretical Biology and Bioinformatics Group, Department of Biology, Utrecht University, Padualaan 8, Utrecht, The Netherlands.

Current Opinion in Genetics & Development
|October 4, 2011
PubMed
Summary
This summary is machine-generated.

Plant development relies on feedback and information integration. Combining experiments with computational modeling reveals complex gene regulatory networks (GRNs) and interconnected processes, advancing our understanding of plant systems biology.

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Area of Science:

  • Plant developmental biology
  • Systems biology
  • Computational modeling

Background:

  • Biological systems require feedback and information integration for robust functioning.
  • Plant developmental biology increasingly uses computational modeling alongside experiments.
  • Understanding regulatory interactions is key to deciphering system behavior.

Purpose of the Study:

  • To highlight experimental and modeling studies on feedback loops and integration mechanisms in plant development.
  • To showcase advancements in understanding gene regulatory networks (GRNs).
  • To illustrate pervasive regulatory interactions across different biological processes and organizational levels.

Main Methods:

  • Review of experimental studies.
  • Analysis of computational modeling approaches.
  • Integration of findings from diverse plant development research.

Main Results:

  • Substantial expansion of knowledge on previously characterized gene regulatory networks (GRNs).
  • Demonstration of widespread regulatory interactions linking seemingly unrelated processes.
  • Identification of challenges in selecting appropriate levels of detail and organization for computational models.

Conclusions:

  • Feedback and information integration are crucial for plant development.
  • The combination of experimental and modeling approaches deepens the understanding of complex GRNs.
  • Future modeling efforts must carefully consider the scope and scale of biological processes to be investigated.