PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

The demand for starch-rich crops remains high due to their wide applications, and one of them is rice (Oryza sativa). However, large-scale rice production faces challenges such as unstable productivity, climate changes and excessive use of agrochemicals. Plant cell culture technology is proposed to increase rice yield and produce a drought-resistance variety of rice to sustain its demand. However, the amount of starch in rice cultures is expected to be smaller compared to the planted ones. The main aim of this study is to apply Flux Balance Analysis (FBA) to optimize starch production in rice cultures. This study reconstructed the stoichiometric metabolic model for rice culture based on the published articles. It consists of 160 reactions and 148 metabolites representing rice’s main carbon metabolism towards starch production. The model was then formulated in GAMS v31.1.0, and the objective function was set to the maximization of biomass and starch. The selected constraints (sugar uptake rates and cell growth rates) from previous studies were utilized. The simulated starch production rate values were achieved at the highest glucose uptake rates with the value of 0.0544 mol/g CDW.h. The internal metabolic flux distributions demonstrated that the incoming carbon fixes were directed towards the glycolysis pathway, TCA cycle, PPP cycle, and starch biosynthesis reactions. The study results serve as a starting point to further understanding the starch production mechanism in plants known to be complex.

• Amthor, J. S. (2000). The McCree–de Wit–Penning de Vries–Thornley respiration paradigms: 30 years later. Annals of Botany, 86(1), 1-20.

• Carnicer, M., Baumann, K., Töplitz, I., Sánchez-Ferrando, F., Mattanovich, D., Ferrer, P., & Albiol, J. (2009). Macromolecular and elemental composition analysis and extracellular metabolite balances of Pichia pastoris growing at different oxygen levels. Microbial Cell Factories, 8, 1-14. https://doi.org/10.1186/1475-2859-8-65

• Hanegraaf, P. P. F., & Muller, E. B. (2001). The dynamics of the macromolecular composition of biomass. Journal of Theoretical Biology, 212(2), 237-251. https://doi.org/10.1006/jtbi.2001.2369

• Lakshmanan, M., Zhang, Z., Mohanty, B., Kwon, J. Y., Choi, H. Y., Nam, H. J., Kim, D. Il, & Lee, D. Y. (2013). Elucidating rice cell metabolism under flooding and drought stresses using flux-based modeling and analysis. Plant Physiology, 162(4), 2140-2150. https://doi.org/10.1104/pp.113.220178

• Lee, S. T., & Huang, W. L. (2013). Cytokinin, auxin, and abscisic acid affects sucrose metabolism conduce to de novo shoot organogenesis in rice (Oryza sativa L.) callus. Botanical Studies, 54(1), 1-11. https://doi.org/10.1186/1999-3110-54-5

• Lularevic, M., Racher, A. J., Jaques, C., & Kiparissides, A. (2019). Improving the accuracy of flux balance analysis through the implementation of carbon availability constraints for intracellular reactions. Biotechnology and Bioengineering, 116(9), 2339-2352. https://doi.org/10.1002/bit.27025

• Marco, C., Pérez, G., León, A. E., & Rosell, C. M. (2008). Effect of transglutaminase on protein electrophoretic pattern of rice, soybean, and rice‐soybean blends. Cereal Chemistry, 85(1), 59-64. https://doi.org/10.1094/CCHEM-85-1-0059

• Masni, Z., & Wasli, M. E. (2019). Yield performance and nutrient uptake of red rice variety (MRM 16) at different NPK fertilizer rates. International Journal of Agronomy, 2019, Article 5134358. https://doi.org/10.1155/2019/5134358

• Orth, J. D., Thiele, I., & Palsson, B. O. (2010). What is flux balance analysis? Nature Biotechnology, 28(3), 245-248. https://doi.org/10.1038/nbt.1614

• Pfister, B., & Zeeman, S. C. (2016). Formation of starch in plant cells. Cellular and Molecular Life Sciences, 73(14), 2781-2807. https://doi.org/10.1007/s00018-016-2250-x

• Poolman, M. G., Miguet, L., Sweetlove, L. J., & Fell, D. A. (2009). A genome-scale metabolic model of Arabidopsis and some of its properties. Plant Physiology, 151(3), 1570-1581. https://doi.org/10.1104/pp.109.141267

• Puad, N. (2011). Modelling the Metabolism of Plant Cell Culture (Arabidopsis) (PhD thesis). The University of Manchester, UK.

• Raman, K., & Chandra, N. (2009). Flux balance analysis of biological systems: Applications and challenges. Briefings in Bioinformatics, 10(4), 435-449. https://doi.org/10.1093/bib/bbp011

• Shaw, R., & Kundu, S. (2015). Flux balance analysis of genome-scale metabolic model of rice (Oryza sativa): Aiming to increase biomass. Journal of Biosciences, 40(4), 819-828. https://doi.org/10.1007/s12038-015-9563-z

• Shu, X., Jia, L., Gao, J., Song, Y., Zhao, H., Nakamura, Y., & Wu, D. (2007). The influences of chain length of amylopectin on resistant starch in rice (Oryza sativa L.). Starch/Staerke, 59(10), 504-509. https://doi.org/10.1002/star.200700640

• Toivonen, L., Laakso, S., & Rosenqvist, H. (1992). The effect of temperature on hairy root cultures of Catharanthus roseus: Growth, indole alkaloid accumulation and membrane lipid composition. Plant Cell Reports, 11(8), 395-399. https://doi: 10.1007/BF00234368

• Tony, L. P. A., & McFarland, L. M. (2021). Essential nutrients for plants - How do nutrients affect plant growth? Texas A&M Agrilife Extension. https://agrilifeextension.tamu.edu/library/gardening/essential-nutrients-for-plants/

• Wang, M. Y., Siddiqi, M. Y., Ruth, T. J., & Glass, A. D. (1993). Ammonium uptake by rice roots (II. Kinetics of 13NH4+ influx across the plasmalemma). Plant Physiology, 103(4), 1259-1267. https://doi.org/10.1104/pp.103.4.1259

• Zeeman, S. C., Kossmann, J., & Smith, A. M. (2010). Starch: Its metabolism, evolution, and biotechnological modification in plants. Annual Review of Plant Biology, 61, 209-234. https://doi.org/10.1146/annurev-arplant-042809-112301