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.

Figure 1.

Steady State Modeling Approaches. (A) A model reaction consisting of three metabolites (M), three exchanges (b), and three internal reactions (v). (B) The reaction network represented in a stoichiometric matrix. (C) The network rewritten in matrix form based on the equations. (D) In a metabolic steady state, the product of the stoichiometric matrix (S) and the flux vector (v) returns a null vector (i.e., S.v = 0). Mass balance equations for each metabolite have been represented here. (E) Constraints (shown in gray) and solution space. With no constraints, the flux distribution of a biological network reconstruction may lie at any point in a solution space (i). FBA (ii) solves the equation S.v = 0 by calculating intracellular fluxes from the measurement of a limited number of input and output fluxes. The solution (black dot) requires the definition of an objective function. By applying EMA and EPA (iii), the irreversible fluxes are constrained to be non-negative (v ≥ 0), then the resulting space of flux distributions is a convex polyhedral cone, which represents the flux space of the metabolic system, containing all allowable flux distributions. MFA (iv) provides information concerning the contribution of a measured reaction (m) to the operational state of overall unmeasured fluxes and computes a metabolic flux vector specific for a particular growth condition (black dot).

Figure 2.

The Matrix and Figures Representing the Set of Three Elementary Modes and the Set of Two Extreme Pathways of the Network Depicted in Figure 1A. With respect to the equation S.v = 0, S denotes the matrix formed by regarding each elementary mode (EM) (A) or each extreme pathway (EP) (B) as a column, and v is the vector containing their respective activity. Notice that the two EPs are also EMs, but the EM3 can be expressed as a combination of the others. This occurs because EPs are a subset of EMs, but no EP can be reconstructed as a linear combination of other EPs. Each EM represents a stoichiometrically and thermodynamically feasible route to the conversion of substrates into products, which cannot be decomposed into simpler routes. EMA reveals the adaptability and robustness of the metabolic network and EPA represents the margins of the derived steady state flux cone.