Optimal signal processing in small stochastic biochemical networks.
We quantify the influence of the topology of a transcriptional regulatory network on its ability to process environmental signals. By posing the problem in terms of information theory, we do this without specifying the function performed by the network. Specifically, we study the maximum mutual information between the input (chemical) signal and the output (genetic) response attainable by the network in the context of an analytic model of particle number fluctuations. We perform this analysis for all biochemical circuits, including various feedback loops, that can be built out of 3 chemical species, each under the control of one regulator. We find that a generic network, constrained to low molecule numbers and reasonable response times, can transduce more information than a simple binary switch and, in fact, manages to achieve close to the optimal information transmission fidelity. These high-information solutions are robust to tenfold changes in most of the networks' biochemical parameters; moreover they are easier to achieve in networks containing cycles with an odd number of negative regulators (overall negative feedback) due to their decreased molecular noise (a result which we derive analytically). Finally, we demonstrate that a single circuit can support multiple high-information solutions. These findings suggest a potential resolution of the "cross-talk" phenomenon as well as the previously unexplained observation that transcription factors that undergo proteolysis are more likely to be auto-repressive.
Ziv
Etay
E
College of Physicians and Surgeons, Columbia University, New York, New York, United States of America. ez87@columbia.edu
Nemenman
Ilya
I
Wiggins
Chris H
CH
D001671:Biochemistry / Q000379:methods; D003198:Computer Simulation; D019277:Entropy; D025461:Feedback, Physiological; D053263:Gene Regulatory Networks; D007700:Kinetics; D008954:Models, Biological; D008956:Models, Chemical; D008957:Models, Genetic; D015233:Models, Statistical; D016012:Poisson Distribution; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D014158:Transcription, Genetic