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Published February 11, 2022 | public
Journal Article

Using channel theory to model biochemical networks with feedback

Abstract

Biochemical signaling networks constantly detect and transmit cell signals to adapt to environmental stimuli. In certain contexts, these responses depend not only on instantaneous signal values but also on their histories. The reliability of information transmission down such noisy network "channels" depends crucially on how well the detection process maps input to output trajectories. Typical analysis describes this mapping using a multivariate Gaussian channel that treats detection of each signal in parallel and with inherent channel noise correlated from one time instant to the next. In relying on "memory" of previously detected signals, however, these now-standard models do not explicitly incorporate feedback from output at previous and more distant timepoints into the assessment of subsequent input processing. I address this feature by proposing a parallel Gaussian channel model with cross-channel feedback where a given parallel channel input may be altered by feedback regulation from outputs at multiple previous timepoints and with variable strengths of correlation. This alternative model is physically distinct from others in that its interpretation of memory implies that a system remembers not only signal detection at previous timepoints, but also specific information about that signal, which has consequences for the subsequent reliability of signal transmission. I present this interpretation using various detection motifs to highlight the effect of feedback on key network reaction properties such as the rate of information transmission.

Additional Information

© 2021 Biophysical Society. Published by Elsevier Inc. Available online 11 February 2022, Version of Record 11 February 2022.

Additional details

Created:
August 22, 2023
Modified:
October 24, 2023