  Biochemical networks can respond to temporal characteristics of time-varying
signals. To understand how reliably biochemical networks can transmit
information we must consider how an input signal as a function of time--the
input trajectory--can be mapped onto an output trajectory. Here we estimate the
mutual information between in- and output trajectories using a Gaussian model.
We study how reliably the chemotaxis network of E. coli can transmit
information on the ligand concentration to the flagellar motor, and find the
input power spectrum that maximizes the information transmission rate.
