Pharmaceutical-based entrainment of circadian phase via nonlinear model predictive control
Autor: | Elizabeth B. Klerman, Ankush Chakrabarty, Francis J. Doyle, John H. Abel |
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Rok vydání: | 2019 |
Předmět: |
0209 industrial biotechnology
Computer science Lag Systems biology 020208 electrical & electronic engineering 02 engineering and technology Optimal control Article Nonlinear system Model predictive control 020901 industrial engineering & automation Maximum principle Control and Systems Engineering Control theory 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Parametric statistics Phase response curve |
Zdroj: | Automatica. 100:336-348 |
ISSN: | 0005-1098 |
DOI: | 10.1016/j.automatica.2018.11.012 |
Popis: | The widespread adoption of closed-loop control in systems biology has resulted from improvements in sensors, computing, actuation, and the discovery of alternative sites of targeted drug delivery. Most control algorithms for circadian phase resetting exploit light inputs. However, recently identified small-molecule pharmaceuticals offer advantages in terms of invasiveness and potency of actuation. Herein, we develop a systematic method to control the phase of biological oscillations motivated by the recently identified small molecule circadian pharmaceutical KL001. The model-based control architecture exploits an infinitesimal parametric phase response curve (ipPRC) that is used to predict the effect of control inputs on future phase trajectories of the oscillator. The continuous time optimal control policy is first derived for phase resetting, based on the ipPRC and Pontryagin’s maximum principle. Owing to practical challenges in implementing a continuous time optimal control policy, we investigate the effect of implementing the continuous time policy in a sampled time format. Specifically, we provide bounds on the errors incurred by the physiologically tractable sampled time control law. We use these results to select directions of resetting (i.e. phase advance or delay), sampling intervals, and prediction horizons for a nonlinear model predictive control (MPC) algorithm for phase resetting. The potential of this ipPRC-informed pharmaceutical nonlinear MPC is then demonstrated in silico using real-world scenarios of jet lag or rotating shift work. |
Databáze: | OpenAIRE |
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