When it comes to meeting the projected 70% increase in food demand in 2050, Dr. Xinguang Zhu considers photosynthetic efficiency to be an unexplored opportunity to deliver significant yield increases. Backing this are two recent studies reporting 15% and 40% increased crop biomass achieved by manipulating photosynthetic efficiency.
The targets for both of those studies were guided by computer modeling. Crop systems models, by simultaneously considering many interacting components in a complex system, can be used to define the optimal combinations of parameters to gain increased yields through optimization analysis or identify the most effective target to engineer for increased yield for a particular cultivar through sensitivity analysis.
According to their perspective paper recently published in in silico Plants, Zhu and coauthors propose that the development of high yielding crops can be expedited using phenomics, genomics and systems models in combination because of recent advances in all three areas.
High-throughput phenotyping (HTP) produces data of unprecedented quantity and quality for model parameterization and application. HTP delivers data required by a complete systems model that includes (a) morphological traits that are difficult to track visually and non-destructively, such as root morphology, (b) changes in functional and structural dynamics over time, such as during a whole growth season, and (c) plant-environment interactions.
In the post-genomics era, molecular marker based selection, genomic selection and genomic editing has become progressively faster and more efficient. This information will enhance crop systems models abilities to predict phenotype from genotype. Genomic information can be incorporated into systems models by (a) constructing mapping functions between molecular markers and macroscopic model parameters, or (b) constructing a genetic regulatory network to directly predict phenotype from genotype.
To develop models that can more effectively guide crop breeding, the authors call for effective integration of HTP, genomics and systems modeling, extending ongoing efforts of developing a universal interface that can enable the development of a comprehensive, multi-scale and mechanistic model describing biogenesis, function, growth and senescence of each organ.
Zhu, X.-G., Long, S. P., & Ort, D. R. (2010). Improving Photosynthetic Efficiency for Greater Yield. Annual Review of Plant Biology, 61(1), 235–261. https://doi.org/10.1146/annurev-arplant-042809-112206
Kromdijk, J., Głowacka, K., Leonelli, L., Gabilly, S. T., Iwai, M., Niyogi, K. K., & Long, S. P. (2016). Improving photosynthesis and crop productivity by accelerating recovery from photoprotection. Science, 354(6314), 857–861. https://doi.org/10.1126/science.aai8878
South, P. F., Cavanagh, A. P., Liu, H. W., & Ort, D. R. (2019). Synthetic glycolate metabolism pathways stimulate crop growth and productivity in the field. Science, 363(6422), eaat9077. https://doi.org/10.1126/science.aat9077
Chang, T.-G., Chang, S., Song, Q.-F., Perveen, S., & Zhu, X.-G. (2019). Systems models, phenomics and genomics: three pillars for developing high-yielding photosynthetically efficient crops. In Silico Plants, 1(1). https://doi.org/10.1093/insilicoplants/diy003
Zhu, X.-G., Lynch, J. P., LeBauer, D. S., Millar, A. J., Stitt, M., & Long, S. P. (2015). Plants in silico
: why, why now and what?-an integrative platform for plant systems biology research. Plant, Cell & Environment, 39(5), 1049–1057. https://doi.org/10.1111/pce.12673
Marshall-Colon, A., Long, S. P., Allen, D. K., Allen, G., Beard, D. A., Benes, B., … Zhu, X.-G. (2017). Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform. Frontiers in Plant Science, 8. https://doi.org/10.3389/fpls.2017.00786
Xiao, Y., Chang, T., Song, Q., Wang, S., Tholen, D., Wang, Y., … Zhu, X.-G. (2017). 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, 5(3), 260–271. https://doi.org/10.1007/s40484-017-0110-9