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ePlant for quantitative and predictive plant science research in the big data era—Lay the foundation for the future model guided crop breeding, engineering and agronomy

Quantitative Biology volume 5, pages 260–271 (2017)Cite this article

Abstract

Background

The increase in global population, climate change and stagnancy in crop yield on unit land area basis in recent decades urgently call for a new approach to support contemporary crop improvements. ePlant is a mathematical model of plant growth and development with a high level of mechanistic details to meet this challenge.

Results

ePlant integrates modules developed for processes occurring at drastically different temporal (10‒8‒106 seconds) and spatial (10‒10‒10 meters) scales, incorporating diverse physical, biophysical and biochemical processes including gene regulation, metabolic reaction, substrate transport and diffusion, energy absorption, transfer and conversion, organ morphogenesis, plant environment interaction, etc. Individual modules are developed using a divide-and-conquer approach; modules at different temporal and spatial scales are integrated through transfer variables.We further propose a supervised learning procedure based on information geometry to combine model and data for both knowledge discovery and model extension or advances. We finally discuss the recent formation of a global consortium, which includes experts in plant biology, computer science, statistics, agronomy, phenomics, etc. aiming to expedite the development and application of ePlant or its equivalents by promoting a new model development paradigm where models are developed as a community effort instead of driven mainly by individual labs’ effort.

Conclusions

ePlant, as a major research tool to support quantitative and predictive plant science research, will play a crucial role in the future model guided crop engineering, breeding and agronomy.

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Acknowledgments

The work in XGZ’s lab is supported by CAS strategic leading project on designer breeding by molecular module (No. XDA08020301), the National High Technology Development Plan of the Ministry of Science and Technology of China (2014AA101601), the National Natural Science Foundation of China (No. C020401), the National Key Basic Research Program of China (No. 2015CB150104), Bill and Melinda Gates Foundation (No. OPP1060461), CAS-CSIRO Cooperative Research Program (No. GJHZ1501).

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Authors and Affiliations

  1. Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200032, China

    Yi Xiao, Qingfeng Song, Honglong Zhao & Xin-Guang Zhu

  2. Plant Systems Biology Research Group, Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai, 200031, China

    Tiangen Chang, Shuyue Wang, Danny Tholen, Yu Wang, Changpeng Xin, Guangyong Zheng & Xin-Guang Zhu

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  10. Xin-Guang Zhu
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Correspondence to Xin-Guang Zhu.

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Xiao, Y., Chang, T., Song, Q. et al. ePlant for quantitative and predictive plant science research in the big data era—Lay the foundation for the future model guided crop breeding, engineering and agronomy. Quant Biol 5, 260–271 (2017). https://doi.org/10.1007/s40484-017-0110-9

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  • DOI: https://doi.org/10.1007/s40484-017-0110-9

Keywords

  • systems modeling
  • quantitative
  • predictive
  • homeostasis
  • multiscale
  • crop in silico