Abstract
Genome-scale metabolic (GSM) models of plants have flourished over the last decade with advancements in both their scope and their applications. While the first plant models were mainly developed to represent a comprehensive set of all metabolic reactions that occur within a plant , organ- and tissue-specific models have been developed and recently these models have been linked to create whole-plant metabolic models. GSM models provide a promising path to predict the effect of genetic and environmental perturbations on metabolism. These models capture the interplay between carbon and nitrogen metabolism , which is important in designing genetic manipulations that improve nitrogen use efficiency (NUE). There is also potential to apply and adapt the diverse set of algorithms developed for microbial GSM models to plants. These algorithms have yielded numerous success stories in predicting the metabolic effects of genetic manipulations by identifying strategies for over-producing chemicals, and driving discovery. Furthering the development of plant GSM models and associated algorithmic tools is expected to have a large impact on predicting manipulations to improve plant traits such as NUE.
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Simons-Senftle, M.N., Sarkar, D., Maranas, C.D. (2018). Modeling Plant Metabolism: Advancements and Future Capabilities. In: Shrawat, A., Zayed, A., Lightfoot, D. (eds) Engineering Nitrogen Utilization in Crop Plants. Springer, Cham. https://doi.org/10.1007/978-3-319-92958-3_4
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