  We present an information-theoretic formalism to study signal transduction in
four architectural variants of a model two-step cascade with increasing input
population. Our results categorize these four types into two classes depending
upon the effect played out by activation and repression on mutual information,
net synergy, and signal-to-noise ratio. Within the Gaussian framework and using
the linear noise approximation, we derive the analytic expressions for these
metrics to establish their underlying relationships in terms of the biochemical
parameters. We also verify our approximations through stochastic simulations.
