Implementation of dynamic Bayesian decision making by intracellular kinetics.
Decision making in a noisy and dynamically changing environment is a fundamental task for a cell. To choose appropriate decisions over time, a cell must be equipped with intracellular kinetics that can conduct dynamic and efficient decision making. By using the theory of sequential inference, I demonstrate that dynamic Bayesian decision making can be implemented by an intracellular kinetics with a dual positive feedback structure. I also show that the combination of linear instantaneous and nonlinear stationary sensitivities to the input dominantly contributes to decision making efficiency, and that the state-dependent sensitivity change further suppresses noisy response. The statistical principles underlying these two factors are further clarified to be a log-likelihood-dependent quantification of the input information and uncertainty-dependent sensitivity control.
Kobayashi
Tetsuya J
TJ
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba Meguro-ku, Tokyo 153-8505, Japan. tetsuya@mail.crmind.net
D001499:Bayes Theorem; D025461:Feedback, Physiological; D042541:Intracellular Space / Q000378:metabolism*; D007700:Kinetics; D008954:Models, Biological