Plant Metabolic Modeling: Achieving New Insight into Metabolism and Metabolic Engineering

Abstract

Models are used to represent aspects of the real world for specific purposes, and mathematical models have opened up new approaches in studying the behavior and complexity of biological systems. However, modeling is often time-consuming and requires significant computational resources for data development, data analysis, and simulation. Computational modeling has been successfully applied as an aid for metabolic engineering in microorganisms. But such model-based approaches have only recently been extended to plant metabolic engineering, mainly due to greater pathway complexity in plants and their highly compartmentalized cellular structure. Recent progress in plant systems biology and bioinformatics has begun to disentangle this complexity and facilitate the creation of efficient plant metabolic models. This review highlights several aspects of plant metabolic modeling in the context of understanding, predicting and modifying complex plant metabolism. We discuss opportunities for engineering photosynthetic carbon metabolism, sucrose synthesis, and the tricarboxylic acid cycle in leaves and oil synthesis in seeds and the application of metabolic modeling to the study of plant acclimation to the environment. The aim of the review is to offer a current perspective for plant biologists without requiring specialized knowledge of bioinformatics or systems biology.

Inference and prediction of metabolic network fluxes

“…The studies have mainly focused on the central metabolic network of carbon and nitrogen metabolism as relates to the synthesis of the main biomass components of growing tissues. Recent reviews have provided a comprehensive overview of the findings from such studies (Baghalian et al, 2014;Kruger and Ratcliffe, 2015). Here, we will focus on specific examples that have provided the most significant new insights into metabolic behavior and function.…”

Metabolic architecture of the cereal grain and its relevance to maximize carbon use efficiency

“…Diurnal variation in transcript abundance in barley (Hordeum vulgare) grains has been found (Mangelsen et al, 2010), but it is unclear whether transcriptomic changes are mirrored by (proportional) changes to the metabolic activity. Where experimentation is difficult, it may be possible to exploit in silico metabolic modeling, an approach that has been validated in the form of flux balance analysis (FBA; Sweetlove and Ratcliffe, 2011;Baghalian et al, 2014). FBA represents an established technique to generate quantitative models of optimal steady-state flux distributions at large-scale metabolic networks.…”

Improving Bioenergy Crops through Dynamic Metabolic Modeling

“…Also, most of these models have an exclusive focus on steady-state operation, whereas dynamic modeling of bioenergy pathways is still in its infancy. Baghalian et al [37], and Morgan and Rhodes [19], provide excellent reviews on modeling plant metabolism that describe prominent mathematical approaches in the field.…”

“…However, the restriction to steady-state operation must be considered with some caution in plant and crop modeling, as plants seldom truly operate at the same steady state throughout the day [19,20,37,92]. In particular, the dynamics of the light-dark cycle is an important reminder of the non-steady-state operation of plants [36].…”

“…For instance, these models implicitly rely on the homogeneity of the medium in which the reactions occur, which doesn't hold true in the in vivo environment of a cell. For larger systems, the parameterization of mechanistic kinetic models becomes laborious, expensive, and time consuming [37], and the resulting measurements are often obtained in vitro, and may not be representative of enzyme kinetics in vivo [27,103]. An additional problem with mechanistic models is that it may become impossible to infer their structure if multiple regulatory mechanisms are involved in a reaction [104].…”