Mutual information in time-varying biochemical systems.
Cells must continuously sense and respond to time-varying environmental stimuli. These signals are transmitted and processed by biochemical signaling networks. However, the biochemical reactions making up these networks are intrinsically noisy, which limits the reliability of intracellular signaling. Here we use information theory to characterize the reliability of transmission of time-varying signals through elementary biochemical reactions in the presence of noise. We calculate the mutual information for both instantaneous measurements and trajectories of biochemical systems for a Gaussian model. Our results indicate that the same network can have radically different characteristics for the transmission of instantaneous signals and trajectories. For trajectories, the ability of a network to respond to changes in the input signal is determined by the timing of reaction events, and is independent of the correlation time of the output of the network. We also study how reliably signals on different time scales can be transmitted by considering the frequency-dependent coherence and gain-to-noise ratio. We find that a detector that does not consume the ligand molecule upon detection can more reliably transmit slowly varying signals, while an absorbing detector can more reliably transmit rapidly varying signals. Furthermore, we find that while one reaction may more reliably transmit information than another when considered in isolation, when placed within a signaling cascade the relative performance of the two reactions can be reversed. This means that optimizing signal transmission at a single level of a signaling cascade can reduce signaling performance for the cascade as a whole.
Tostevin
Filipe
F
FOM Institute AMOLF, Science Park 104, 1098XG Amsterdam, The Netherlands. f.tostevin@amolf.nl
ten Wolde
Pieter Rein
PR
D000465:Algorithms; D000818:Animals; D001671:Biochemistry / Q000379:methods; D001703:Biophysics / Q000379:methods*; D002468:Cell Physiological Phenomena; D008954:Models, Biological; D015233:Models, Statistical; D016011:Normal Distribution; D011336:Probability; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry; D051381:Rats; D015398:Signal Transduction; D013997:Time Factors
D011506:Proteins