摘要
当前,各类组学技术、基因组编辑技术及超性能计算能力的飞速发展正使得植物科学从描述性、定性研究向精细定量研究乃至理性设计的转变。在这个过程中,数字植物的研究应运而生。数字植物通过对植物生长发育过程多尺度、多生理生化现象的系统定量模拟,以实现植物整个生命周期的“数字化”。数字植物将为定量植物科学研究、植物设计及改造提供理论工具。数字植物的发展将支持新代谢通路、基因调控网络的设计,乃至植物理想基因系统的设计,从而为以提升作物产量和优化作物品质为目标的植物合成生物学提供设计工具。本文在分析当前构建数字植物遇到的瓶颈的基础上,提出发展数字植物所需要的关键措施,即:构建植物生长发育基本模型;获得同化物在各器官间分配的代谢数据;创建模块模型耦联方法;建立数字植物研究公共平台;发展表型数据与机理模型相结合的方法;以水稻为模式植物,开展数字植物指导的分子设计育种;建立支持数字植物的人才培养及储备策略。利用数字植物定量模拟和设计植物是未来植物合成生物学发展的趋势,数字植物指导下的作物栽培和育种也是精准农业和智慧农业的必然要求。
Plant systems and synthetic biology has gained more and more attention in recent years as a result of the rapid advances in genomics,proteomics,metabolomics,epigenomics and genome editing technologies.Plant systems and synthetic biology for quantitative studies of plant systems differs from traditional plant science that mainly focuses on descriptive and qualitative studies of plant systems.In addition,plant systems and synthetic biology also emphasizes the design and creation of new biochemical pathways,regulatory circuits,signaling pathways,and even biological structures that are not exist in native plants hosts for desired properties.A number of mega projects in plant synthetic biology have been initiated in recent years,all with a common goal of boosting crop yield and developing technologies for Green Revolution of agriculture.With these projects,it was evident that to maximize the benefit of plant synthetic biology,we ought to develop a robust system model for plant growth and development:ePlant,which includes the simulation not only for molecular,biochemical,physiological,and physical processes in different organs of a plant,but also for interaction between plant and environment as highlighted by the system of soil,root,plant and air.The ePlant model needs to be developed using a divide-and-conquer approach followed by the modular construction,and once developed,it can be used as a major tool for basic research in plant science,studies for interaction among genes,environment and management practices,identification of new options to engineer crops for desired properties,and finally design of ideotypes desired for crop breeding.To expedite the development of ePlant model,we propose a number of priorities and strategies,which include developing basic models for plant growth and development,collecting systems data related to partitioning of similarities among different organs,creating new methods to integrate modules at different temporal and spatial scales,building up public online platforms to support the development of ePlant,developing algorithms to support effective integration of ePlant models with phenomics data,and finally forming policies to nurture the development of communities on plant systems modeling.We emphasize a strong demand for an online platform or portal to support the development of ePlant models and their applications.This platform needs to include not only the basic models,data supporting model development and application and high-performance super-computing resources to enable the models'widely applications by the plant science research community.We also advocate for using rice as the model species to construct such an ePlant,considering the large quantities of studies on rice genomics,genetics,physiology,breeding and agronomics,which will expedite the development,validation and application of ePlant models.After ePlant model developed for rice(eRice),we can use it to guide dissection of mechanism underlying high yield formation in current rice lines,to design ideotypes for super-high-yield rice breeding,and to format optimal agronomic practices.
作者
朱新广
常天根
宋青峰
常硕其
王重荣
张国庆
郭亚
周少川
ZHU Xinguang;CHANG Tiangen;SONG Qingfeng;CHANG Shuoqi;WANG Chongrong;ZHANG Guoqing;GUO Ya;ZHOU Shaochuan(State Key Laboratory of Plant Molecular Genetics,CAS Center for Excellence in Molecular Plant Sciences,Institute of Plant Physiology and Ecology,Shanghai Institute for Biological Sciences,Chinese Academy of Sciences,Shanghai 200032,China;State Key Laboratory of Hybrid Rice,Hunan Hybrid Rice Research Center,Changsha 410125,Hunan,China;Guangdong Provincial Key Laboratory of New Technology for Rice Breeding,Rice Research Institute of Guangdong Academy of Agricultural Sciences,Guangzhou 510640,Guangdong,China;Shanghai Institute of Nutrition and Health,CAS-MPG Partner Institute for Computer,Shanghai Institute for Biological Sciences,Key Laboratory of Computational Biology,Chinese Academy of Sciences,Shanghai 200031,China;Jiangnan University,Wuxi 214122,Jiangsu,China)
出处
《合成生物学》
2020年第3期285-297,共13页
Synthetic Biology Journal
基金
中国科学院先导专项(XDB27020105、XDA08020301)
国家自然科学基金(30970213、31870214)
国家重点基础研究发展计划(2015CB150104)
国家高技术研究发展计划(2014AA101601)
国家重点研发计划(2019YFA0904600)
广东省应用型科技专项(2015B 020231001)。
