Improving Bioenergy Crops through Dynamic Metabolic Modeling

Abstract

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.

A dynamic model of lignin biosynthesis in Brachypodium distachyon

“…In fact, such a model offers future opportunities for predicting responses to numerous types of alterations and, in particular, optimal genomic changes with respect to lignin content and composition (cf. [ 15 ]).…”

“…The model can now be used for other predictions, e.g., regarding responses to single or double gene knockdowns, as described in [ 11 , 15 ] for switchgrass. To validate the prediction accuracy of our earlier switchgrass model, we used a transgenic line whose data had not been used to construct the model, adjusted the model parameters for the measured enzyme profile in the so-far not-used perturbation experiment and compared simulated and observed lignin compositions.…”

“…In particular, if stable solutions exist, the simulations and mathematical analyses will reveal them. Furthermore, the dynamic model permits optimization methods that predict particularly useful gene modifications, as we have shown in the context of lignin synthesis elsewhere [ 15 ].…”

Modeling cross-regulatory influences on monolignol transcripts and proteins under single and combinatorial gene knockdowns in Populus trichocarpa

“…Predicting what transgenic modifications will lead to desired lignin and wood phenotypes is of current interest in the bioenergy and biomaterial industries among others [29,30]. Computational models of the monolignol pathway have become an important tool in the past decade to understanding how changes to the monolignol enzymes result in changes to the pathway outputs [18,[31][32][33][34] and lignin and wood phenotypes [4]. We add to this body of work by developing a model that incorporates observed influences at both the transcript and protein levels to estimate how the enzymes in the monolignol biosynthetic pathway are influenced by one or more monolignol gene knockdowns.…”

Special Issue on “Biological Networks”

“…The review paper by Faraji and Voit [8] focuses on the metabolic modeling of crop science with a specific focus on bioenergy crops. In comparison to microbes and animal cells, mathematical modeling of plant metabolisms is still in its infancy, but is expected to become a standard tool in the future.…”