  To develop and investigate detailed mathematical models of cellular metabolic
processes is one of the primary challenges in systems biology. However, despite
considerable advance in the topological analysis of metabolic networks,
explicit kinetic modeling based on differential equations is still often
severely hampered by inadequate knowledge of the enzyme-kinetic rate laws and
their associated parameter values. Here we propose a method that aims to give a
detailed and quantitative account of the dynamical capabilities of metabolic
systems, without requiring any explicit information about the particular
functional form of the rate equations. Our approach is based on constructing a
local linear model at each point in parameter space, such that each element of
the model is either directly experimentally accessible, or amenable to a
straightforward biochemical interpretation. This ensemble of local linear
models, encompassing all possible explicit kinetic models, then allows for a
systematic statistical exploration of the comprehensive parameter space. The
method is applied to two paradigmatic examples: The glycolytic pathway of yeast
and a realistic-scale representation of the photosynthetic Calvin cycle.
