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Related Concept Videos

Transgenic Plants02:50

Transgenic Plants

Recombinant DNA technology called transgenesis is often used to add a foreign gene or remove a detrimental gene from an organism. Such genetically modified organisms are called transgenic organisms.
The first-ever transgenic plant was a tobacco plant developed in 1983 that showed resistance against the tobacco mosaic virus. Since then, many transgenic plants have been developed and commercialized for improving the agricultural, ornamental, and horticultural value of a crop plant. Transgenic...
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Updated: May 17, 2026

Peptide-derived Method to Transport Genes and Proteins Across Cellular and Organellar Barriers in Plants
08:48

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Published on: December 16, 2016

Toward predictable and programmable genetic circuits in plants.

Ci Kong1, Jianfeng Zhang1

  • 1Beijing Life Science Academy, Beijing, China; Key Laboratory of Biosynthesis and Biomanufacturing in Model Plants (Beijing Life Science Academy), Ministry of Industry and Information Technology, Beijing, China.

Biotechnology Advances
|May 15, 2026
PubMed
Summary
This summary is machine-generated.

Achieving predictable plant genetic circuits requires moving beyond empirical methods. A quantitative engineering framework, considering biological context and employing advanced modeling, is essential for scalable and reusable synthetic biology designs.

Keywords:
DBTL cyclePlant synthetic biologyPrecision genome engineeringPredictive engineeringSynthetic genetic circuits

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Area of Science:

  • Plant Synthetic Biology
  • Genetic Engineering
  • Systems Biology

Background:

  • Current plant genetic circuit design relies heavily on empirical optimization, hindering scalability and predictability.
  • Advances in genetic parts and circuit construction have not fully translated into predictable outcomes in plants.
  • Quantitative prediction remains a significant challenge in plant synthetic biology.

Purpose of the Study:

  • To review the challenges hindering quantitative prediction in plant genetic circuits.
  • To organize recent advances within a quantitative engineering framework for plant synthetic biology.
  • To highlight strategies for achieving predictable circuit behavior in plants.

Main Methods:

  • Examination of factors influencing circuit behavior, including genomic/epigenetic context, development, spatial organization, and environmental variability.
  • Discussion of strategies for quantitative sensing, information processing, and model-informed design.
  • Review of enabling technologies such as automation, high-throughput phenotyping, and AI-assisted modeling.

Main Results:

  • Predictable plant circuit design necessitates quantitative characterization, standardized measurement, and multiscale modeling.
  • Accounting for cellular, physiological, and environmental context is crucial for successful design.
  • Species-specific physiology and environmental conditions impact predictive performance in field applications.

Conclusions:

  • Predictability in plant synthetic biology is a systems-level engineering challenge.
  • Coordinated quantitative design across genetic, physiological, and environmental scales is required.
  • Integrating advanced modeling and automation will facilitate transferable design principles.