Quantitative Biology

, Volume 5, Issue 3, pp 260–271 | Cite as

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

  • Yi Xiao
  • Tiangen Chang
  • Qingfeng Song
  • Shuyue Wang
  • Danny Tholen
  • Yu Wang
  • Changpeng Xin
  • Guangyong Zheng
  • Honglong Zhao
  • Xin-Guang ZhuEmail author



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.


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.


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.


systems modeling quantitative predictive homeostasis multiscale crop in silico 



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).


  1. 1.
    Zhu, X.-G., Zhang, G. L., Tholen, D., Wang, Y., Xin, C. P. and Song, Q. F. (2011) The next generation models for crops and agroecosystems. Sci. China Inf. Sci., 54, 589–597CrossRefGoogle Scholar
  2. 2.
    Hammer, G. L., van Oosterom, E., McLean, G., Chapman, S. C., Broad, I., Harland, P. and Muchow, R. C. (2010) Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops. J. Exp. Bot., 61, 2185–2202CrossRefPubMedGoogle Scholar
  3. 3.
    Ruíz-Nogueira, B., Boote, K. J. and Sau, F. (2001) Calibration and use of CROPGRO-soybean model for improving soybean management under rainfed conditions. Agric. Syst., 68, 151–173CrossRefGoogle Scholar
  4. 4.
    Ma, W., Trusina, A., El-Samad, H., Lim, W. A. and Tang, C. (2009) Defining network topologies that can achieve biochemical adaptation. Cell, 138, 760–773CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Xin, C. P., Yang, J. and Zhu, X.-G. (2013) A model of chlorophyll a fluorescence induction kinetics with explicit description of structural constraints of individual photosystem IIunits. Photosynth. Res., 117, 339–354CrossRefPubMedGoogle Scholar
  6. 6.
    Xiao, Y. and Zhu, X.-G. (2016) Chlorophyll fluorescecence and stable isotope signals in photosynthesis research. Plant Physiology Journal (in Chinese), 52, 1663–1670Google Scholar
  7. 7.
    Tholen, D. and Zhu, X.-G. (2011) The mechanistic basis of internal conductance: a theoretical analysis of mesophyll cell photosynthesis and CO2 diffusion. Plant Physiol., 156, 90–105CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Wang, Y., Song, Q., Jaiswal, D., de Souza, A. P., Long, S. P. and Zhu, X.-G. (2017) Development of a three dimensional ray-tracing model of sugarcane canopy photosynthesis and its applications in assessing impacts of varied row spacing. Bioenerg Res., doi: 10.1007/s12155-017-9823-xGoogle Scholar
  9. 9.
    Zheng, B., Biddulph, B., Li, D., Kuchel, H. and Chapman, S. (2013) Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments. J. Exp. Bot., 64, 3747–3761CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Tubiello, F. N., Soussana, J.-F. and Howden, S. M. (2007) Crop and pasture response to climate change. Proc. Natl. Acad. Sci. USA, 104, 19686–19690CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Miguez, F. E., Zhu, X., Humphries, S., Bollero, G. A. and Long, S. P. (2009) A semimechanistic model predicting the growth and production of the bioenergy crop Miscanthus giganteus: description, parameterization and validation. GCB Bioenergy, 1, 282–296CrossRefGoogle Scholar
  12. 12.
    Li, T., Hasegawa, T., Yin, X., Zhu, Y., Boote, K., Adam, M., Bregaglio, S., Buis, S., Confalonieri, R., Fumoto, T., et al. (2015) Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. Glob. Change Biol., 21, 1328–1341CrossRefGoogle Scholar
  13. 13.
    Sellers, P. J., Randall, D. A., Collatz, G. J., Berry, J. A., Field, C. B., Dazlich, D. A., Zhang, C., Collelo, G. D. and Bounoua, L. (1996) A revised land surface parameterization (SiB2) for atmospheric GCMs. part I: model formulation. J. Clim., 9, 676–705CrossRefGoogle Scholar
  14. 14.
    Falkowski, P., Scholes, R. J., Boyle, E., Canadell, J., Canfield, D., Elser, J., Gruber, N., Hibbard, K., Högberg, P., Linder, S., et al. (2000) The global carbon cycle: a test of our knowledge of earth as a system. Science, 290, 291–296CrossRefPubMedGoogle Scholar
  15. 15.
    Xue, Y., Chong, K., Han, B., Gui, J., Wang, T., Fu, X., He, Z., Chu, C., Tian, Z., Cheng, Z., Lin, S. (2015) New chapter of designer breeding in China: update on strategic program of molecular module-based designer breeding systems. Buttletin of Chinese Academy of Sciences, 30, 393–402Google Scholar
  16. 16.
    Zhu, X.-G., Portis, A. R. Jr and Long, S. P. (2004) Would transformation of C3 crop plants with foreign Rubisco increase productivity? A computational analysis extrapolating from kinetic properties to canopy photosynthesis. Plant Cell Environ., 27, 155–165CrossRefGoogle Scholar
  17. 17.
    Zhu, X.-G., Ort, D. R., Whitmarsh, J. and Long, S. P. (2004) The slow reversibility of photosystem II thermal energy dissipation on transfer from high to low light may cause large losses in carbon gain by crop canopies: a theoretical analysis. J. Exp. Bot., 55, 1167–1175CrossRefPubMedGoogle Scholar
  18. 18.
    Drewry, D. T., Kumar, P. and Long, S. P. (2014) Simultaneous improvement in productivity, water use, and albedo through crop structural modification. Glob. Change Biol., 20, 1955–1967CrossRefGoogle Scholar
  19. 19.
    Song, Q.-F., Zhang, G. and Zhu, X.-G. (2013) Optimal crop canopy architecture to maximise canopy photosynthetic CO2 uptake under elevated CO2–a theoretical study using a mechanistic model of canopy photosynthesis. Funct. Plant Biol., 40, 108–124CrossRefGoogle Scholar
  20. 20.
    Zhu, X.-G., de Sturler, E. and Long, S. P. (2007) Optimizing the distribution of resources between enzymes of carbon metabolism can dramatically increase photosynthetic rate: a numerical simulation using an evolutionary algorithm. Plant Physiol., 145, 513–526CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Wang, Y., Long, S. P. and Zhu, X. G. (2014) Elements required for an efficient NADP-malic enzyme type C4 photosynthesis. Plant Physiol., 164, 2231–2246CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Xin, C. P., Tholen, D., Devloo, V. and Zhu, X. G. (2015) The benefits of photorespiratory bypasses: how can they work? Plant Physiol., 167, 574–585CrossRefPubMedGoogle Scholar
  23. 23.
    Wang, S., Tholen, D. and Zhu, X. G. (2017) C4 photosynthesis in C3 rice: a theoretical analysis of biochemical and anatomical factors. Plant Cell Environ., 40, 80–94CrossRefPubMedGoogle Scholar
  24. 24.
    Xiao, Y., Tholen, D. and Zhu, X.-G. (2016) The influence of leaf anatomy on the internal light environment and photosynthetic electron transport rate: exploration with a new leaf ray tracing model. J. Exp. Bot., 67, 6021–6035CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Simkin, A. J., McAusland, L., Headland, L. R., Lawson, T. and Raines, C. A. (2015) Multigene manipulation of photosynthetic carbon assimilation increases CO2 fixation and biomass yield in tobacco. J. Exp. Bot., 66, 4075–4090CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Kromdijk, J., Głowacka, K., Leonelli, L., Gabilly, S. T., Iwai, M., Niyogi, K. K. and Long, S. P. (2016) Improving photosynthesis and crop productivity by accelerating recovery from photoprotection. Science, 354, 857–861CrossRefPubMedGoogle Scholar
  27. 27.
    Nunes-Nesi, A., Carrari, F., Lytovchenko, A., Smith, A. M., Loureiro, M. E., Ratcliffe, R. G., Sweetlove, L. J. and Fernie, A. R. (2005) Enhanced photosynthetic performance and growth as a consequence of decreasing mitochondrial malate dehydrogenase activity in transgenic tomato plants. Plant Physiol., 137, 611–622CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Sweetlove, L. J., Lytovchenko, A., Morgan, M., Nunes-Nesi, A., Taylor, N. L., Baxter, C. J., Eickmeier, I. and Fernie, A. R. (2006) Mitochondrial uncoupling protein is required for efficient photosynthesis. Proc. Natl. Acad. Sci. USA, 103, 19587–19592CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Zhu, X.-G., Wang, Y., Ort, D. R. and Long, S. P. (2013) e-Photosynthesis: a comprehensive dynamic mechanistic model of C3 photosynthesis: from light capture to sucrose synthesis. Plant Cell Environ., 36, 1711–1727CrossRefPubMedGoogle Scholar
  30. 30.
    Owen, N. A. and Griffiths, H. (2013) A system dynamics model integrating physiology and biochemical regulation predicts extent of crassulacean acid metabolism (CAM) phases. New Phytol., 200, 1116–1131CrossRefPubMedGoogle Scholar
  31. 31.
    Cortassa, S., Aon, M. A., O’Rourke, B., Jacques, R., Tseng, H. J., Marbán, E. and Winslow, R. L. (2006) A computational model integrating electrophysiology, contraction, and mitochondrial bioenergetics in the ventricular myocyte. Biophys. J., 91, 1564–1589CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Thornley, J. H. M. and Cannell, M. G. R. (2000) Modelling the components of plant respiration: representation and realism. Ann. Bot. (Lond.), 85, 55–67CrossRefGoogle Scholar
  33. 33.
    Lawson, T., Simkin, A. J., Kelly, G. and Granot, D. (2014) Mesophyll photosynthesis and guard cell metabolism impacts on stomatal behaviour. New Phytol., 203, 1064–1081CrossRefPubMedGoogle Scholar
  34. 34.
    Flexas, J., Ribas-Carbó, M., Diaz-Espejo, A., Galmés, J. and Medrano, H. (2008) Mesophyll conductance to CO2: current knowledge and future prospects. Plant Cell Environ., 31, 602–621CrossRefPubMedGoogle Scholar
  35. 35.
    Baroli, I., Price, G. D., Badger, M. R. and von Caemmerer, S. (2008) The contribution of photosynthesis to the red light response of stomatal conductance. Plant Physiol., 146, 737–747CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Wong, S.-C., Cowan, I. R. and Farquhar, G. D. (1979) Stomatal conductance correlates with photosynthetic capacity. Nature, 282, 424–426CrossRefGoogle Scholar
  37. 37.
    Farquhar, G. D. and Sharkey, T. D. (1982) Stomatal conductance and photosynthesis. Annu. Rev. Plant Physiol., 33, 317–345CrossRefGoogle Scholar
  38. 38.
    Buckley, T. N., Mott, K. A. and Farquhar, G. D. (2003) A hydromechanical and biochemical model of stomatal conductance. Plant Cell Environ., 26, 1767–1785CrossRefGoogle Scholar
  39. 39.
    Ball, J. T., Woodrow, I. E. and Berry, J. A. (1987) A Model Predicting Stomatal Conductance and Its Contribution to The Control of Photosynthesis Under Different Environmental Conditions. In Progress in Photosynthesis Research. Biggens, J. ed., Vol, IV,pp. 221–224. Berlin: Springer NetherlandsCrossRefGoogle Scholar
  40. 40.
    Loreto, F., Harley, P. C., Di Marco, G. and Sharkey, T. D. (1992) Estimation of mesophyll conductance to CO2 flux by three different methods. Plant Physiol., 98, 1437–1443CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Pons, T. L., Flexas, J., von Caemmerer, S., Evans, J. R., Genty, B., Ribas-Carbo, M. and Brugnoli, E. (2009) Estimating mesophyll conductance to CO2: methodology, potential errors, and recommendations. J. Exp. Bot., 60, 2217–2234CrossRefPubMedGoogle Scholar
  42. 42.
    Tholen, D., Boom, C. and Zhu, X.-G. (2012) Opinion: prospects for improving photosynthesis by altering leaf anatomy. Plant Sci., 197, 92–101CrossRefPubMedGoogle Scholar
  43. 43.
    Xiong, D., Liu, X., Liu, L., Douthe, C., Li, Y., Peng, S. and Huang, J. (2015) Rapid responses of mesophyll conductance to changes of CO2 concentration, temperature and irradiance are affected by N supplements in rice. Plant Cell Environ., 38, 2541–2550CrossRefPubMedGoogle Scholar
  44. 44.
    Ho, Q. T., Berghuijs, H. N., Watté, R., Verboven, P., Herremans, E., Yin, X., Retta, M. A., Aernouts, B., Saeys, W., Helfen, L., et al. (2016) Three-dimensional microscale modelling of CO2 transport and light propagation in tomato leaves enlightens photosynthesis. Plant Cell Environ., 39, 50–61CrossRefPubMedGoogle Scholar
  45. 45.
    Price, N. D., Reed, J. L. and Palsson, B. O. (2004) Genome-scale models of microbial cells: evaluating the consequences of constraints. Nat. Rev. Microbiol., 2, 886–897CrossRefPubMedGoogle Scholar
  46. 46.
    Guo, Y., Ma, Y., Zhan, Z., Li, B., Dingkuhn, M., Luquet, D. and De Reffye, P. (2006) Parameter optimization and field validation of the functional-structural model GREENLAB for maize. Ann. Bot. (Lond.), 97, 217–230CrossRefGoogle Scholar
  47. 47.
    Watanabe, T., Hanan, J. S., Room, P. M., Hasegawa, T., Nakagawa, H. and Takahashi, W. (2005) Rice morphogenesis and plant architecture: measurement, specification and the reconstruction of structural development by 3D architectural modelling. Ann. Bot. (Lond.), 95, 1131–1143CrossRefGoogle Scholar
  48. 48.
    Song, Y. H., Smith, R. W., To, B. J., Millar, A. J. and Imaizumi, T. (2012) FKF1 conveys timing information for CONSTANS stabilization in photoperiodic flowering. Science, 336, 1045–1049CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Domagalska, M. A. and Leyser, O. (2011) Signal integration in the control of shoot branching. Nat. Rev. Mol. Cell Biol., 12, 211–221CrossRefPubMedGoogle Scholar
  50. 50.
    Minchin, P. E. H. and Lacointe, A. (2005) New understanding on phloem physiology and possible consequences for modelling longdistance carbon transport. New Phytol., 166, 771–779CrossRefPubMedGoogle Scholar
  51. 51.
    Rasse, D. P. and Tocquin, P. (2006) Leaf carbohydrate controls over Arabidopsis growth and response to elevated CO2: an experimentally based model. New Phytol., 172, 500–513CrossRefPubMedGoogle Scholar
  52. 52.
    Lynch, J. P. (2013) Steep, cheap and deep: an ideotype to optimize water and N acquisition by maize root systems. Ann. Bot. (Lond.), 112, 347–357CrossRefGoogle Scholar
  53. 53.
    Dyson, R. J., Vizcay-Barrena, G., Band, L. R., Fernandes, A. N., French, A. P., Fozard, J. A., Hodgman, T. C., Kenobi, K., Pridmore, T. P., Stout, M., et al. (2014) Mechanical modelling quantifies the functional importance of outer tissue layers during root elongation and bending. New Phytol., 202, 1212–1222CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Chang, T. G. and Zhu, X. G. (2017) Source-sink interaction: a century old concept under the light of modern molecular systems biology. J. Exp. Bot. erx002Google Scholar
  55. 55.
    Yin, X. and Struik, P. C. (2010) Modelling the crop: from system dynamics to systems biology. J. Exp. Bot., 61, 2171–2183CrossRefPubMedGoogle Scholar
  56. 56.
    Li, Y., Pearl, S. A. and Jackson, S. A. (2015) Gene networks in plant biology: approaches in reconstruction and analysis. Trends Plant Sci., 20, 664–675CrossRefPubMedGoogle Scholar
  57. 57.
    Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D. and Friedman, N. (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet., 34, 166–176CrossRefPubMedGoogle Scholar
  58. 58.
    Zheng, G., Xu, Y., Zhang, X., Liu, Z. P., Wang, Z., Chen, L. and Zhu, X. G. (2016) CMIP: a software package capable of reconstructing genome-wide regulatory networks using gene expression data. BMC Bioinformatics, 17, 535CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Wenden, B. and Rameau, C. (2009) Systems biology for plant breeding: the example of flowering time in pea. C. R. Biol., 332, 998–1006CrossRefPubMedGoogle Scholar
  60. 60.
    Salazar, J. D., Saithong, T., Brown, P. E., Foreman, J., Locke, J. C., Halliday, K. J., Carré, I. A., Rand, D. A. and Millar, A. J. (2009) Prediction of photoperiodic regulators from quantitative gene circuit models. Cell, 139, 1170–1179CrossRefPubMedGoogle Scholar
  61. 61.
    Bassel, G. W., Lan, H., Glaab, E., Gibbs, D. J., Gerjets, T., Krasnogor, N., Bonner, A. J., Holdsworth, M. J. and Provart, N. J. (2011) Genome-wide network model capturing seed germination reveals coordinated regulation of plant cellular phase transitions. Proc. Natl. Acad. Sci. USA, 108, 9709–9714CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Chew, Y. H., Wenden, B., Flis, A., Mengin, V., Taylor, J., Davey, C. L., Tindal, C., Thomas, H., Ougham, H. J., de Reffye, P., et al. (2014) Multiscale digital Arabidopsis predicts individual organ and whole-organism growth. Proc. Natl. Acad. Sci. USA, 111, E4127–E4136CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Zhu, X.-G., Song, Q. and Ort, D. R. (2012) Elements of a dynamic systems model of canopy photosynthesis. Curr. Opin. Plant Biol., 15, 237–244CrossRefPubMedGoogle Scholar
  64. 64.
    Parton, W. J., Scurlock, J. M. O., Ojima, D. S., Gilmanov, T. G., Scholes, R. J., Schimel, D. S., Kirchner, T., Menaut, J.-C., Seastedt, T., Garcia Moya, E., et al. (1993) Observations and modelling of biomass and soil organic matter dynamics for the grassland biome wordwide. Global Biogeochem. Cycles, 7, 785–809CrossRefGoogle Scholar
  65. 65.
    Parton, W. J., Stewart, J.W. B. and Cole, C. V. (1988) Dynamics of C, N, P and S in grassland soils: a model. Biogeochemistry, 5, 109–131CrossRefGoogle Scholar
  66. 66.
    Buckley, T. N. (2005) The control of stomata by water balance. New Phytol., 168, 275–292CrossRefPubMedGoogle Scholar
  67. 67.
    Lynch, J. P., Nielsen, K. L., Davis, R. D. and Jablokow, A. G. (1997) SimRoot: modeling and visualization of root systems. Plant Soil, 188, 139–151CrossRefGoogle Scholar
  68. 68.
    Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., Wilkens, P. W., Singh, U., Gijsman, A. J. and Ritchie, J. T. (2003) The DSSAT cropping system model. Eur. J. Agron., 18, 235–265CrossRefGoogle Scholar
  69. 69.
    McCown, R. L., Hammer, G. L., Hargreaves, J. N. G., Holzworth, D. P. and Freebairn, D. M. (1996) APSIM: a novel software system for model development, model testing and simulation in agricultural systems research. Agric. Syst., 50, 255–271CrossRefGoogle Scholar
  70. 70.
    Humphries, S. W. and Long, S. P. (1995) WIMOVAC: a software package for modelling the dynamics of plant leaf and canopy photosynthesis. Comput. Appl. Biosci., 11, 361–371PubMedGoogle Scholar
  71. 71.
    Song, Q., Chen, D., Long, S. P. and Zhu, X. G. (2017) A userfriendly means to scale from the biochemistry of photosynthesis to whole crop canopies and production in time and space— development of Java WIMOVAC. Plant Cell Environ., 40, 51–55CrossRefPubMedGoogle Scholar
  72. 72.
    Norman, J. M. (1980) Interfacing leaf and canopy light interception models. In Predicting Photosynthesis for Ecosystem Models. Hesketh, J. D. & Jones, J. W. eds. Vol. 2, pp. 49–67. Boca Raton: CRC PressGoogle Scholar
  73. 73.
    Farquhar, G. D., von Caemmerer, S. and Berry, J. A. (1980) A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta, 149, 78–90CrossRefPubMedGoogle Scholar
  74. 74.
    Pokhilko, A., Flis, A., Sulpice, R., Stitt, M. and Ebenhöh, O. (2014) Adjustment of carbon fluxes to light conditions regulates the daily turnover of starch in plants: a computational model. Mol. Biosyst., 10, 613–627CrossRefPubMedGoogle Scholar
  75. 75.
    de Oliveira Dal’Molin, C. G., Quek, L.-E., Palfreyman, R. W., Brumbley, S. M. and Nielsen, L. K. (2010) C4GEM, a genomescale metabolic model to study C4 plant metabolism. Plant Physiol., 154, 1871–1885CrossRefPubMedCentralGoogle Scholar
  76. 76.
    de Oliveira Dal’Molin, C. G., Quek, L. E., Palfreyman, R. W., Brumbley, S. M. and Nielsen, L. K. (2010) AraGEM, a genomescale reconstruction of the primary metabolic network in Arabidopsis. Plant Physiol., 152, 579–589CrossRefPubMedPubMedCentralGoogle Scholar
  77. 77.
    Warren, J. M., Hanson, P. J., Iversen, C. M., Kumar, J., Walker, A. P. and Wullschleger, S. D. (2015) Root structural and functional dynamics in terrestrial biosphere models — evaluation and recommendations. New Phytol., 205, 59–78CrossRefPubMedGoogle Scholar
  78. 78.
    Zhu, X.-G., Govindjee Baker, N. R., de Sturler, E., Ort, D. O. and Long, S. P. (2005) 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. Planta, 223, 114–133CrossRefPubMedGoogle Scholar
  79. 79.
    Yu, X., Zheng, G., Shan, L., Meng, G., Vingron, M., Liu, Q. and Zhu, X. G. (2014) Reconstruction of gene regulatory network related to photosynthesis in Arabidopsis thaliana. Front. Plant Sci., 5, 273PubMedPubMedCentralGoogle Scholar
  80. 80.
    Chandrasekaran, S. and Price, N. D. (2010) Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc. Natl. Acad. Sci. USA, 107, 17845–17850CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    Enquist, B. J. and Niklas, K. J. (2002) Global allocation rules for patterns of biomass partitioning in seed plants. Science, 295, 1517–1520CrossRefPubMedGoogle Scholar
  82. 82.
    Box, G. E. P. (1976) Science and statistics. J. Am. Stat. Assoc., 71, 791–799CrossRefGoogle Scholar
  83. 83.
    Machta, B. B., Chachra, R., Transtrum, M. K. and Sethna, J. P. (2013) Parameter space compression underlies emergent theories and predictive models. Science, 342, 604–607CrossRefPubMedGoogle Scholar
  84. 84.
    Zhou, M., Wang, W., Karapetyan, S., Mwimba, M., Marqués, J., Buchler, N. E. and Dong, X. (2015) Redox rhythm reinforces the circadian clock to gate immune response. Nature, 523, 472–476CrossRefPubMedPubMedCentralGoogle Scholar
  85. 85.
    Zuo, J. and Li, J. (2014) Molecular dissection of complex agronomic traits of rice: a team effort by Chinese scientists in recent years. Natl. Sci. Rev. 1, 253–276CrossRefGoogle Scholar
  86. 86.
    Valluru, R., Reynolds, M. P. and Salse, J. (2014) Genetic and molecular bases of yield-associated traits: a translational biology approach between rice and wheat. Theor. Appl. Genet., 127, 1463–1489CrossRefPubMedGoogle Scholar
  87. 87.
    Wallace, J. G., Larsson, S. J. and Buckler, E. S. (2014) Entering the second century of maize quantitative genetics. Heredity (Edinb), 112, 30–38CrossRefGoogle Scholar
  88. 88.
    Kaul, S., Koo, H. L., Jenkins, J., Rizzo, M., Rooney, T., Tallon, L. J., Feldblyum, T., Nierman, W., Benito, M., Lin, X. (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature, 408, 796–815CrossRefGoogle Scholar
  89. 89.
    Zhu, X. G., Lynch, J. P., LeBauer, D. S., Millar, A. J., Stitt, M. and Long, S. P. (2016) Plants in silico: why, why now and what—an integrative platform for plant systems biology research. Plant Cell Environ., 39, 1049–1057CrossRefPubMedGoogle Scholar
  90. 90.
    Marshall-Colon, A., Long, S. P., Allen, D. K., Allen, G., Beard, D. A., Benes, B., von Caemmerer, S., Christensen, A. J., Cox, D. J., Hart, J. C. et al. (2017) Crops in silico: a prospectus from the plants in silico symposium and workshop. Front. Plant Sci. 8, 786CrossRefPubMedPubMedCentralGoogle Scholar
  91. 91.
    Yabusaki, S., Fang, Y., Chen, X., Scheibe, T. D. (2016) Single Plant Root Systems Modeling Under Soil Moisture Variation. In 2016 American Geophysical Union, San FranciscoGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Yi Xiao
    • 1
  • Tiangen Chang
    • 2
  • Qingfeng Song
    • 1
  • Shuyue Wang
    • 2
  • Danny Tholen
    • 2
  • Yu Wang
    • 2
  • Changpeng Xin
    • 2
  • Guangyong Zheng
    • 2
  • Honglong Zhao
    • 1
  • Xin-Guang Zhu
    • 1
    • 2
    Email author
  1. 1.Shanghai Institute of Plant Physiology and EcologyChinese Academy of SciencesShanghaiChina
  2. 2.Plant Systems Biology Research Group, Partner Institute for Computational BiologyChinese Academy of SciencesShanghaiChina

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