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 (original) (raw)

References

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, 589–597
   Article Google Scholar
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, 2185–2202
   Article CAS PubMed Google Scholar
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, 151–173
   Article Google Scholar
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, 760–773
   Article CAS PubMed PubMed Central Google Scholar
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, 339–354
   Article CAS PubMed Google Scholar
6. Xiao, Y. and Zhu, X.-G. (2016) Chlorophyll fluorescecence and stable isotope signals in photosynthesis research. Plant Physiology Journal (in Chinese), 52, 1663–1670
   Google Scholar
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, 90–105
   Article CAS PubMed PubMed Central Google Scholar
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-x
   Google Scholar
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, 3747–3761
   Article CAS PubMed PubMed Central Google Scholar
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, 19686–19690
    Article CAS PubMed PubMed Central Google Scholar
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, 282–296
    Article Google Scholar
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, 1328–1341
    Article CAS Google Scholar
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, 676–705
    Article Google Scholar
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, 291–296
    Article CAS PubMed Google Scholar
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, 393–402
    Google Scholar
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, 155–165
    Article CAS Google Scholar
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, 1167–1175
    Article CAS PubMed Google Scholar
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, 1955–1967
    Article Google Scholar
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, 108–124
    Article CAS Google Scholar
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, 513–526
    Article CAS PubMed PubMed Central Google Scholar
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, 2231–2246
    Article CAS PubMed PubMed Central Google Scholar
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, 574–585
    Article CAS PubMed Google Scholar
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, 80–94
    Article CAS PubMed Google Scholar
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, 6021–6035
    Article CAS PubMed PubMed Central Google Scholar
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, 4075–4090
    Article CAS PubMed PubMed Central Google Scholar
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, 857–861
    Article CAS PubMed Google Scholar
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, 611–622
    Article CAS PubMed PubMed Central Google Scholar
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, 19587–19592
    Article CAS PubMed PubMed Central Google Scholar
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, 1711–1727
    Article CAS PubMed Google Scholar
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, 1116–1131
    Article CAS PubMed Google Scholar
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, 1564–1589
    Article CAS PubMed PubMed Central Google Scholar
32. Thornley, J. H. M. and Cannell, M. G. R. (2000) Modelling the components of plant respiration: representation and realism. Ann. Bot. (Lond.), 85, 55–67
    Article CAS Google Scholar
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, 1064–1081
    Article CAS PubMed Google Scholar
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, 602–621
    Article CAS PubMed Google Scholar
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, 737–747
    Article CAS PubMed PubMed Central Google Scholar
36. Wong, S.-C., Cowan, I. R. and Farquhar, G. D. (1979) Stomatal conductance correlates with photosynthetic capacity. Nature, 282, 424–426
    Article Google Scholar
37. Farquhar, G. D. and Sharkey, T. D. (1982) Stomatal conductance and photosynthesis. Annu. Rev. Plant Physiol., 33, 317–345
    Article CAS Google Scholar
38. Buckley, T. N., Mott, K. A. and Farquhar, G. D. (2003) A hydromechanical and biochemical model of stomatal conductance. Plant Cell Environ., 26, 1767–1785
    Article CAS Google Scholar
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 Netherlands
    Article Google Scholar
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, 1437–1443
    Article CAS PubMed PubMed Central Google Scholar
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, 2217–2234
    Article CAS PubMed Google Scholar
42. Tholen, D., Boom, C. and Zhu, X.-G. (2012) Opinion: prospects for improving photosynthesis by altering leaf anatomy. Plant Sci., 197, 92–101
    Article CAS PubMed Google Scholar
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, 2541–2550
    Article CAS PubMed Google Scholar
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, 50–61
    Article CAS PubMed Google Scholar
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, 886–897
    Article CAS PubMed Google Scholar
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, 217–230
    Article Google Scholar
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, 1131–1143
    Article Google Scholar
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, 1045–1049
    Article CAS PubMed PubMed Central Google Scholar
49. Domagalska, M. A. and Leyser, O. (2011) Signal integration in the control of shoot branching. Nat. Rev. Mol. Cell Biol., 12, 211–221
    Article CAS PubMed Google Scholar
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, 771–779
    Article CAS PubMed Google Scholar
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, 500–513
    Article CAS PubMed Google Scholar
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, 347–357
    Article CAS Google Scholar
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, 1212–1222
    Article PubMed PubMed Central Google Scholar
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. erx002
    Google Scholar
55. Yin, X. and Struik, P. C. (2010) Modelling the crop: from system dynamics to systems biology. J. Exp. Bot., 61, 2171–2183
    Article CAS PubMed Google Scholar
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, 664–675
    Article CAS PubMed Google Scholar
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, 166–176
    Article CAS PubMed Google Scholar
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, 535
    Article PubMed PubMed Central Google Scholar
59. Wenden, B. and Rameau, C. (2009) Systems biology for plant breeding: the example of flowering time in pea. C. R. Biol., 332, 998–1006
    Article PubMed Google Scholar
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, 1170–1179
    Article CAS PubMed Google Scholar
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, 9709–9714
    Article CAS PubMed PubMed Central Google Scholar
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–E4136
    Article CAS PubMed PubMed Central Google Scholar
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, 237–244
    Article CAS PubMed Google Scholar
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, 785–809
    Article CAS Google Scholar
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, 109–131
    Article CAS Google Scholar
66. Buckley, T. N. (2005) The control of stomata by water balance. New Phytol., 168, 275–292
    Article CAS PubMed Google Scholar
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, 139–151
    Article CAS Google Scholar
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, 235–265
    Article Google Scholar
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, 255–271
    Article Google Scholar
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, 361–371
    CAS PubMed Google Scholar
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, 51–55
    Article CAS PubMed Google Scholar
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 Press
    Google Scholar
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, 78–90
    Article CAS PubMed Google Scholar
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, 613–627
    Article CAS PubMed Google Scholar
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, 1871–1885
    Article PubMed Central Google Scholar
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, 579–589
    Article PubMed PubMed Central Google Scholar
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, 59–78
    Article PubMed Google Scholar
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, 114–133
    Article CAS PubMed Google Scholar
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, 273
    PubMed PubMed Central Google Scholar
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, 17845–17850
    Article CAS PubMed PubMed Central Google Scholar
81. Enquist, B. J. and Niklas, K. J. (2002) Global allocation rules for patterns of biomass partitioning in seed plants. Science, 295, 1517–1520
    Article CAS PubMed Google Scholar
82. Box, G. E. P. (1976) Science and statistics. J. Am. Stat. Assoc., 71, 791–799
    Article Google Scholar
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, 604–607
    Article CAS PubMed Google Scholar
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, 472–476
    Article PubMed PubMed Central Google Scholar
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, 253–276
    Article CAS Google Scholar
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, 1463–1489
    Article CAS PubMed Google Scholar
87. Wallace, J. G., Larsson, S. J. and Buckler, E. S. (2014) Entering the second century of maize quantitative genetics. Heredity (Edinb), 112, 30–38
    Article CAS Google Scholar
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, 796–815
    Article CAS Google Scholar
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, 1049–1057
    Article CAS PubMed Google Scholar
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, 786
    Article PubMed PubMed Central Google Scholar
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 Francisco
    Google Scholar

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