  Mathematical models of cellular physiological mechanisms often involve random
walks on graphs representing transitions within networks of functional states.
Schmandt and Gal\'{a}n recently introduced a novel stochastic shielding
approximation as a fast, accurate method for generating approximate sample
paths from a finite state Markov process in which only a subset of states are
observable. For example, in ion channel models, such as the Hodgkin-Huxley or
other conductance based neural models, a nerve cell has a population of ion
channels whose states comprise the nodes of a graph, only some of which allow a
transmembrane current to pass. The stochastic shielding approximation consists
of neglecting fluctuations in the dynamics associated with edges in the graph
not directly affecting the observable states. We consider the problem of
finding the optimal complexity reducing mapping from a stochastic process on a
graph to an approximate process on a smaller sample space, as determined by the
choice of a particular linear measurement functional on the graph. The
partitioning of ion channel states into conducting versus nonconducting states
provides a case in point. In addition to establishing that Schmandt and
Gal\'{a}n's approximation is in fact optimal in a specific sense, we use recent
results from random matrix theory to provide heuristic error estimates for the
accuracy of the stochastic shielding approximation for an ensemble of random
graphs. Moreover, we provide a novel quantitative measure of the contribution
of individual transitions within the reaction graph to the accuracy of the
approximate process.
