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Improving Bioenergy Crops through Dynamic Metabolic Modeling

The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 950 Atlantic Drive, Atlanta, GA 30332-2000, USA
Author to whom correspondence should be addressed.
Received: 4 September 2017 / Revised: 3 October 2017 / Accepted: 7 October 2017 / Published: 18 October 2017
(This article belongs to the Special Issue Biological Networks)
Enormous advances in genetics and metabolic engineering have made it possible, in principle, to create new plants and crops with improved yield through targeted molecular alterations. However, while the potential is beyond doubt, the actual implementation of envisioned new strains is often difficult, due to the diverse and complex nature of plants. Indeed, the intrinsic complexity of plants makes intuitive predictions difficult and often unreliable. The hope for overcoming this challenge is that methods of data mining and computational systems biology may become powerful enough that they could serve as beneficial tools for guiding future experimentation. In the first part of this article, we review the complexities of plants, as well as some of the mathematical and computational methods that have been used in the recent past to deepen our understanding of crops and their potential yield improvements. In the second part, we present a specific case study that indicates how robust models may be employed for crop improvements. This case study focuses on the biosynthesis of lignin in switchgrass (Panicum virgatum). Switchgrass is considered one of the most promising candidates for the second generation of bioenergy production, which does not use edible plant parts. Lignin is important in this context, because it impedes the use of cellulose in such inedible plant materials. The dynamic model offers a platform for investigating the pathway behavior in transgenic lines. In particular, it allows predictions of lignin content and composition in numerous genetic perturbation scenarios. View Full-Text
Keywords: biochemical systems theory; biofuel; lignin biosynthesis; optimization; plant metabolism; recalcitrance biochemical systems theory; biofuel; lignin biosynthesis; optimization; plant metabolism; recalcitrance
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MDPI and ACS Style

Faraji, M.; Voit, E.O. Improving Bioenergy Crops through Dynamic Metabolic Modeling. Processes 2017, 5, 61.

AMA Style

Faraji M, Voit EO. Improving Bioenergy Crops through Dynamic Metabolic Modeling. Processes. 2017; 5(4):61.

Chicago/Turabian Style

Faraji, Mojdeh, and Eberhard O. Voit. 2017. "Improving Bioenergy Crops through Dynamic Metabolic Modeling" Processes 5, no. 4: 61.

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