1 |
XIAO Y, CHANG T G, SONG Q F, 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[J]. Quantitative Biology, 2017, 5(3): 260-271.
|
2 |
张先恩,中国合成生物学发展回顾与展望[J]. 中国科学:生命科学, 2019, 49(12): 1543-1572.
|
|
ZHANG X-E. Synthetic biology in China: review and prospects[J]. Scientia Sinica Vitae, 2019, 49(12): 1543-1572.
|
3 |
ZHU X G, LYNCH J, LEBAUER D S, et al. Plants in silico: why, why now and what? An integrative platform for plant systems biology research[J]. Plant Cell and Environment, 2015, 39: 1049-1057.
|
4 |
KROMDIJK J, GŁOWACKA K, LEONELLI L, et al. Improving photosynthesis and crop productivity by accelerating recovery from photoprotection[J]. Science, 2016, 354(6314): 857.
|
5 |
SOUTH P F, CAVANAGH A P, LIU H W, et al. Synthetic glycolate metabolism pathways stimulate crop growth and productivity in the field[J]. Science, 2019, 363(6422): eaat9077.
|
6 |
YIN K, GAO C, QIU J L. Progress and prospects in plant genome editing[J]. Nature Plants, 2017, 3(8): 17107.
|
7 |
SHAO Y, LU N, WU Z, et al. Creating a functional single-chromosome yeast[J]. Nature, 2018, 560: 331-335.
|
8 |
ARUTE F, ARYA K, BABBUSH R E A. Quantum supremacy using a programmable superconducting processor[J]. Nature, 2019, 574: 505-510.
|
9 |
HUANG X, HAN B. Natural variations and genome-wide association studies in crop plants[J]. Annual Review of Plant Biology, 2014, 65: 531-551.
|
10 |
MILLS M C, RAHAL C. A scientometric review of genome-wide association studies[J]. Communications Biology, 2019, 2(1):9.
|
11 |
ZHU X G, GOVINDJEE, BAKER N R, et al. Chlorophyll a fluorescence induction kinetics in leaves predicted from a model describing each discrete step of excitation energy and electron transfer associated with photosystem II[J]. Planta, 2005, 223(1): 114-133.
|
12 |
LAZAR D. Chlorophyll a fluorescence induction[J]. Biochimica et Biophysica Acta, 1999, 1412(1): 1-28.
|
13 |
LAZAR D. Chlorophyll a fluorescence rise induced by high light illumination of dark-adapted plant tissue studied by means of a model of photosystem II and considering photosystem II heterogeneity[J]. Journal of Theoretical Biology, 2003, 220: 469-503.
|
14 |
ZHU X G, WANG Y, ORT D, et al. e-Photosynthesis: a comprehensive dynamic mechanistic model of C3 photosynthesis: from light capture to sucrose synthesis[J]. Plant Cell & Environment, 2013, 36(9): 1711-1727.
|
15 |
HAMDANI S, QU M, XIN C P, et al. Variations between the photosynthetic properties of elite and landrace Chinese rice cultivars revealed by simultaneous measurements of 820nm transmission signal and chlorophyll a fluorescence induction[J]. Journal of Plant Physiology, 2015, 177: 128-138.
|
16 |
PETTERSSON G, RYDE-PETTERSSON U. A mathematical model of the Calvin photosynthesis cycle[J]. European Journal of Biochemistry, 1988, 175: 661-672.
|
17 |
BORISUK M T, TYSON J J. Bifurcation analysis of a model of mitotic control in frog eggs[J]. Journal of Theoretical Biology, 1998, 195(1): 69-85.
|
18 |
TYSON J J, CHEN K C, NOVAK B. Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell[J]. Current Opinion In Cell Biology, 2003, 15(2): 221-231.
|
19 |
TYSON J J, CSIKASZ-NAGY A, NOVAK B. The dynamics of cell cycle regulation[J]. Bioessays, 2002, 24: 1095-1109.
|
20 |
TYSON J J, NOVAK B, ODELL G M, et al. Chemical kinetic theory: understanding cell cycle regulation[J]. Trends in Biochemical Sciences, 1996, 21(3): 89-96.
|
21 |
ZWOLAK J W, TYSON J J, WATSON L T. Finding all steady state solutions of chemical kinetic models[J]. Nonlinear Analysis: Real World Applications, 2004, 5: 801-814.
|
22 |
ZWOLAK J W, TYSON J J, WATSON L T. Parameter estimation for a mathematical model of the cell cycle in frog eggs[J]. Journal of Computational Biology, 2005, 12(1): 48-63.
|
23 |
FRIEDLINGSTEIN P, MULLER J F, BRASSEUR G P. Sensitivity of the terrestrial biosphere to climatic changes: impact on the carbon cycle[J]. Environmental Pollution, 1994, 83(1/2): 143-147.
|
24 |
ESSER G, LAUTENSCHLAGER M. Estimating the change of carbon in the terrestrial biosphere from 18 000 BP to present using a carbon cycle model[J]. Environmental Pollution, 1994, 83(1/2): 45-53.
|
25 |
SELLERS P J, MINTZ Y, SUD Y C, et al. A simple biosphere model (SiB) for use within general circulation models[J]. Journal of the Atmospheric Sciences, 1986, 43(6): 505-531.
|
26 |
SONG Q F, ZHANG G, ZHU X G. Optimal crop canopy architecture to maximise canopy photosynthetic CO2 uptake under elevated CO2 - a theoretical study using a mechanistic model of canopy photosynthesis[J]. Functional Plant Biology, 2013, 40(2): 108-124.
|
27 |
WANG Y, SONG Q F, JAISWAL D, et al. Development of a three dimensional ray-tracing model of sugarcane canopy photosynthesis and its applications in assessing impacts of varied row spacing[J]. Bioenergy Research, 2017, 10:626-634.
|
28 |
MONTEITH J L. Climate and the efficiency of crop production in Britain.[J]. Philosophical Transactions of the Royal Society B: Biological Sciences, 1977, 281: 277-294.
|
29 |
ZHU X G, LONG S P, ORT D R. Improving photosynthetic efficiency for greater yield[J]. Annual Review of Plant Biology, 2010, 61: 235-261.
|
30 |
LONG S P, ZHU X G, NAIDU S L, et al. Can improvement in photosynthesis increase crop yields?[J]. Plant Cell and Environment, 2006, 29(3): 315-330.
|
31 |
WANG Y, LONG S P, ZHU X G. Elements required for an efficient NADP-malic enzyme type C4 photosynthesis[J]. Plant Physiology, 2014, 164: 2231-2246.
|
32 |
THOLEN D, ETHIER G, GENTY B, et al. Variable mesophyll conductance revisited: theoretical background and experimental implications[J]. Plant Cell & Environment, 2012, 35: 2087-103.
|
33 |
THOLEN D, ZHU X G. The mechanistic basis of internal conductance: a theoretical analysis of mesophyll cell photosynthesis and CO2 diffusion[J]. Plant Physiology, 2011, 156: 90-105.
|
34 |
XIN C P, THOLEN D, DEVLOO V, et al. The benefits of photorespiratory bypasses: how can they work?[J]. Plant Physiology, 2015, 167(2): 574-585.
|
35 |
SOUTH P F, CAVANAGH A P, LIU H W, et al. Synthetic glycolate metabolism pathways stimulate crop growth and productivity in the field[J]. Science, 2019, 363(6422): eaat9077.
|
36 |
ZHU X G, SONG Q F, ORT D R. Elements of a dynamic systems model of canopy photosynthesis[J]. Current Opinion in Plant Biology, 2012, 15: 237-244.
|
37 |
SONG Q F, CHU C, PARRY M, et al. Genetics based dynamic systems model of canopy photosynthesis: the key to improved light and resource use efficiencies for crops[J]. Food and Energy Security, 2016, 5: 18-25.
|
38 |
CASSMAN K G. Breaking the yield barrier [M]. Philippines: International Rice Research Institute, 1994.
|
39 |
DONALD C M. The breeding of crop ideotypes[J]. Euphytica, 1968, 17: 385-403.
|
40 |
袁隆平. 杂交水稻超高产育种 [J]. 杂交水稻, 1997, 12(6): 1-6.
|
|
YUAN L P. Hybrid rice breeding for super high yield[J]. Hybrid Rice, 1997, 12(6): 1-6.
|
41 |
CHEN W F, XU Z J, ZHANG W Z, et al. Creation of new plant type and breeding rice for super high yield[J]. Acta Agronomica Sinica, 2001, 27(5): 665-672.
|
42 |
CHANG T G, ZHAO H, WANG N, et al. A three-dimensional canopy photosynthesis model in rice with a complete description of the canopy architecture, leaf physiology, and mechanical properties[J]. Journal of Experimental Botany, 2019, 70(9): 2479-2490.
|
43 |
CHANG T G, ZHU X G. Source sink interaction: a century old concept under the light of modern molecular systems biology[J]. Journal of Experimental Botany, 2017, 68: 4417-4431.
|
44 |
GUO T, YU H, QIU J, et al. Advances in rice genetics and breeding by molecular design in China[J]. SCIENCE CHINA Life Sciences, 2019, 49: 1185-1212.
|
45 |
赵雷, 周少川, 王重荣, 等. 绿色广适性优质稻品种的系谱分析及育种应用研究[J]. 生命科学, 2018, 30(10): 1100-1107.
|
|
ZHAO L, ZHOU S C, WANG C R, et al. Pedigree analysis and breeding and application of green widely adaptive good quality rice varieties [J]. Chinese Bulletin of Life Sciences, 2018, 30(10): 1100-1107.
|
46 |
SONG Q F, SRINIASAN V, LONG S P, et al. Decomposition analysis on soybean productivity increase under elevated CO2 using 3D canopy model reveals synergestic effects of CO2 and light in photosynthesis[J]. Annals of Botany, 2019. DOI:10.1111193/aob/mcz163.
DOI
|
47 |
SHI Z, CHANG T G, CHEN G, et al. Dissection of mechanisms for high yield in two elite rice cultivars[J]. Field Crop Research, 2019, 241. .
|
48 |
ZHANG R, LIU J, CHAI Z, et al. Generation of herbicide tolerance traits and a new selectable marker in wheat using base editing[J]. Nature Plants, 2019, 5(1): 480-485.
|
49 |
LI C, ZHANG R, MENG X, et al. Targeted, random mutagenesis of plant genes with dual cytosine and adenine base editors[J]. Nature Biotechnology, 2020, 38(7): 875-882.
|
50 |
KUANG Y, LI S, REN B, et al. Base-editing-mediated artificial evolution of OsALS1 in planta to develop novel herbicide-tolerant rice germplasms[J]. Molecular Plant, 2020, 13(4): 565-572.
|
51 |
CHEN S, LIN Z, ZHOU D, et al. Genome-wide study of an elite rice pedigree reveals a complex history of genetic architecture for breeding improvement[J]. Scientific Reports, 2017, 45685.
|
52 |
ZHOU D, CHEN W, LIN Z, et al. Pedigree-based analysis of derivation of genome segments of an elite rice reveals key regions during its breeding[J]. Plant Biotechnology Journal, 2016, 14(2): 638-648.
|
53 |
ZHU X G, ZHANG G, THOLEN D, et al. The next generation models for crops and agro-ecosystems[J]. Science China Information Sciences, 2011, 54(3): 589-597.
|
54 |
CHANG T G, CHANG S Q, SONG Q F, et al. Systems models, phenomics and genomics: three pillars for developing high-yielding photosynthetically efficient crops [J]. In Silico Plants, 2019, 1(1). .
|