From qualitative to quantitative: the state of the art and challenges for plant synthetic biology

Author:

Tian Chenfei12,Li Jianhua1,Wang Yong1

Affiliation:

1. CAS‐Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences Institute of Plant Physiology and Ecology, Chinese Academy of Sciences Shanghai 200032 China

2. University of Chinese Academy of Sciences Beijing 100039 China

Abstract

The flourishing plant science promotes the exploding number of data and the expansion of toolkits. Plant synthetic biology is still in its early stages and requires more quantitative and predictable study. Despite the challenges, some pioneering examples have been successfully demonstrated in model plants.BackgroundsAs an increasing number of synthetic switches and circuits have been created for plant systems and of synthetic products produced in plant chassis, plant synthetic biology is taking a strong foothold in agriculture and medicine. The ever‐exploding data has also promoted the expansion of toolkits in this field. Genetic parts libraries and quantitative characterization approaches have been developed. However, plant synthetic biology is still in its infancy. The considerations for selecting biological parts to design and construct genetic circuits with predictable functions remain desired.ResultsIn this article, we review the current biotechnological progresses in field of plant synthetic biology. Assembly standardization and quantitative approaches of genetic parts and genetic circuits are discussed. We also highlight the main challenges in the iterative cycles of design‐build‐test‐learn for introducing novel traits into plants.ConclusionPlant synthetic biology promises to provide important solutions to many issues in agricultural production, human health care, and environmental sustainability. However, tremendous challenges exist in this field. For example, the quantitative characterization of genetic parts is limited; the orthogonality and the transfer functions of circuits are unpredictable; and also, the mathematical modeling‐assisted circuits design still needs to improve predictability and reliability. These challenges are expected to be resolved in the near future as interests in this field are intensifying.

Publisher

Wiley

Subject

Applied Mathematics,Computer Science Applications,Biochemistry, Genetics and Molecular Biology (miscellaneous),Modeling and Simulation

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