Tools for Analyzing Network Structure and Performance
In general, individual metabolic fluxes are the net result of the coordinated
activity of the whole network and so rational manipulation of these fluxes
requires tools that can analyze the network as a system rather than focusing on
individual steps. The available modeling approaches can be classified on the basis
of their underlying assumptions (Wiechert, 2002), and the resulting hierarchy
matches the usefulness of the models for metabolic engineering.
The simplest models are the structural network models that are based on the
metabolites and reaction steps that make up the network (Wiechert, 2002). Models
of this kind are useful for exploring the architecture of the network, but they are of
rather limited use in a physiological context because they lack quantitative information
about the metabolites and reaction steps. This deficiency is remedied in
stoichiometric models by assuming constant fluxes and intracellular pool sizes.
Stoichiometric models provide the basis for determining intracellular fluxes
(Bonarius
et al., 1997), as well as permitting the identification of fundamental
network properties such as elementary flux modes and extreme pathways
(Klamt and Stelling, 2003). Stoichiometric modeling can also be applied at the
level of the individual carbon atoms in metabolites, and this leads to a more general
method of determining intracellular fluxes based on the steady-state analysis of the
redistribution of
13Clabels (Kruger
et al., 2003; Wiechert, 2001; Wiechert
et al., 2001).
Models that provide an explanation of the empirically derived flux distribution
can be obtained by incorporating a kinetic description of each reaction step into a stoichiometric model (Wiechert, 2002). These mechanistic (kinetic) models
require detailed information about the
in vivo kinetic properties of the enzymes in
the network, and this is a major obstacle in developing useful models. However,
kinetic modeling is now well developed in yeasts (Teusink
et al., 2000) and red
blood cells (Mulquiney and Kuchel, 2003). Accurate mechanistic models are
expected to have predictive value in the context of metabolic engineering, and
they can also be used to investigate the distribution of control within the conceptual
framework of metabolic control analysis (Fell, 1997). Mechanistic models can be used to analyze both steady-state and transient fluxes and in the longer term it
may also be possible to allowfor fluctuations in enzyme level by incorporating the
regulatory networks for gene expression (Wiechert, 2002).
It is clear from this survey that the analysis of the properties of metabolic
networks can be approached using a variety of model-based strategies. Some of
these approaches aim to make deductions about the performance of the network
from an analysis of the constraints imposed by its structure and stoichiometry
alone, whereas others are heavily dependent on direct measurements of metabolic
fluxes and the kinetic properties of the enzymes that define the network. The aim
here is to describe four of these methods in more detail and to comment on their
utility as predictive tools for plant metabolic engineering.
