There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
Genome-scale constraint-based models of several organisms have now been constructed
and are being used for model driven research. A key issue that may arise in the use
of such models is the existence of alternate optimal solutions wherein the same maximal
objective (e.g., growth rate) can be achieved through different flux distributions.
Herein, we investigate the effects that alternate optimal solutions may have on the
predicted range of flux values calculated using currently practiced linear (LP) and
quadratic programming (QP) methods. An efficient LP-based strategy is described to
calculate the range of flux variability that can be present in order to achieve optimal
as well as suboptimal objective states. Sample results are provided for growth predictions
of E. coli using glucose, acetate, and lactate as carbon substrates. These results
demonstrate the extent of flux variability to be highly dependent on environmental
conditions and network composition. In addition we examined the impact of alternate
optima for growth under gene knockout conditions as calculated using QP-based methods.
It was observed that calculations using QP-based methods can show significant variation
in growth rate if the flux variability among alternate optima is high. The underlying
biological significance and general source of such flux variability is further investigated
through the identification of redundancies in the network (equivalent reaction sets)
that lead to alternate solutions. Collectively, these results illustrate the variability
inherent in metabolic flux distributions and the possible implications of this heterogeneity
for constraint-based modeling approaches. These methods also provide an efficient
and robust method to calculate the range of flux distributions that can be derived
from quantitative fermentation data.