Information Technology Reference
In-Depth Information
by intersecting the null subspace of N and the positive flux orthant, thus
obtaining a polyhedral cone and by imposing constraints on the magnitude of
individual metabolic fluxes (Fig. 8.12).
Fig. 8.12. In a three-dimensional flux space grey arrows correspond to the extreme pathways. They
determine a convex cone inside the positive orthant. Capacity constraints on each flux limits the
cone region to the polytope determined by the grey arrows and the light blue area. All possible flux
distributions lie within such polytope. When the cost function is a linear combination of fluxes,
then the optimal solution (black dot) usually lies in a corner of the polytope or along a whole edge.
The determination of a particular metabolic flux distribution can be obtained
by maximizing (or minimizing) a linear or quadratic objective function using a
linear programming approach. Examples of such objective functions are
minimization of nutrient uptake, maximal yield of a desired product, maximal
growth rate, flux minimization and so on.
8.3.4.
MOMA, PhPP and ROOM analyses
Schuster and colleagues stated that although the assumption of optimality for a
wild-type bacterium is justifiable, the same argument may not be valid for
genetically engineered knockouts or other bacterial strains that were not exposed
to long-term evolutionary pressure. So they address this point by introducing the
method of
minimization of metabolic adjustment
(MOMA), whereby they test the
hypothesis that knockout metabolic fluxes undergo a minimal redistribution with
respect to the flux configuration of the wild type. MOMA employs quadratic
programming to identify a point in flux space.
Phenotypic phase plane
(PhPP) analysis is a constraint-based method used to
obtain a global perspective of genotype-phenotype relationships in genome-scale
metabolic networks. In PhPP analysis, flux balance analysis and linear
programming are used to map all of the cellular growth conditions represented by
