Flux modeling for monolignol biosynthesis

Jack P Wang; Megan L Matthews; Punith P Naik; Cranos M Williams; Joel J Ducoste; Ronald R Sederoff; Vincent L Chiang

doi:10.1016/j.copbio.2018.12.003

Flux modeling for monolignol biosynthesis

Curr Opin Biotechnol. 2019 Apr:56:187-192. doi: 10.1016/j.copbio.2018.12.003. Epub 2018 Dec 18.

Authors

Jack P Wang¹, Megan L Matthews², Punith P Naik³, Cranos M Williams², Joel J Ducoste⁴, Ronald R Sederoff⁵, Vincent L Chiang⁶

Affiliations

¹ State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin 150040, China; Forest Biotechnology Group, Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, United States.
² Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695, United States.
³ Freddie Mac, McLean, VA 22102, United States; Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, United States.
⁴ Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, United States.
⁵ Forest Biotechnology Group, Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, United States.
⁶ State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin 150040, China; Forest Biotechnology Group, Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, United States. Electronic address: vchiang@ncsu.edu.

PMID: 30557780
DOI: 10.1016/j.copbio.2018.12.003

Abstract

The pathway of monolignol biosynthesis involves many components interacting in a metabolic grid to regulate the supply and ratios of monolignols for lignification. The complexity of the pathway challenges any intuitive prediction of the output without mathematical modeling. Several models have been presented to quantify the metabolic flux for monolignol biosynthesis and the regulation of lignin content, composition, and structure in plant cell walls. Constraint-based models using data from transgenic plants were formulated to describe steady-state flux distribution in the pathway. Kinetic-based models using enzyme reaction and inhibition constants were developed to predict flux dynamics for monolignol biosynthesis in wood-forming cells. This review summarizes the recent progress in flux modeling and its application to lignin engineering for improved plant development and utilization.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Review

MeSH terms

Biosynthetic Pathways*
Kinetics
Lignin / biosynthesis*
Metabolic Engineering
Metabolic Flux Analysis*
Models, Biological

Substances

Lignin