A synthetic integral feedback controller for robust tunable regulation in bacteria

AbstractWe report on the first engineered integral feedback control system in a living cell. The controller is based on the recently published antithetic integral feedback motif [1] which has been analytically shown to have favorable regulation properties. It is implemented along with test circuitry in Escherichia coli using seven genes and three small-molecule inducers. The closed-loop system is highly tunable, allowing a regulated protein of interest to be driven to a desired level and maintained there with precision. Realized using a sigma/anti-sigma protein pair, the integral controller ensures that regulation is maintained in the face of perturbations that lead to the regulated protein’s degradation, thus serving as a proof-of-concept prototype of integral feedback implementation in living cells. When suitably optimized, this integral controller may be utilized as a general-purpose robust regulator for genetic circuits with unknown or partially-known topologies and parameters.

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Pseudo-random binary sequence closed-loop system identification error with integration control

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1243/09596518jsce794 ◽

2009 ◽

Vol 223 (6) ◽

pp. 877-884 ◽

Cited By ~ 5

Author(s):

Z Ren ◽

G G Zhu

Keyword(s):

System Identification ◽

Closed Loop ◽

Binary Sequence ◽

Open Loop ◽

Loop System ◽

Loop Model ◽

Closed Loop System ◽

Identification Error ◽

Closed Loop Identification ◽

Integral Controller

This paper studies the closed-loop system identification (ID) error when a dynamic integral controller is used. Pseudo-random binary sequence (PRBS) q-Markov covariance equivalent realization (Cover) is used to identify the closed-loop model, and the open-loop model is obtained based upon the identified closed-loop model. Accurate open-loop models were obtained using PRBS q-Markov Cover system ID directly. For closed-loop system ID, accurate open-loop identified models were obtained with a proportional controller, but when a dynamic controller was used, low-frequency system ID error was found. This study suggests that extra caution is required when a dynamic integral controller is used for closed-loop system identification. The closed-loop identification framework also has significant effects on closed-loop identification error. Both first- and second-order examples are provided in this paper.

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Ultrasensitive molecular controllers for quasi-integral feedback

10.1101/413914 ◽

2018 ◽

Cited By ~ 3

Author(s):

Christian Cuba Samaniego ◽

Elisa Franco

Keyword(s):

Computational Models ◽

Closed Loop ◽

Reaction Network ◽

Robust Tracking ◽

Closed Loop System ◽

System Equilibrium ◽

Molecular Reaction ◽

Unknown Disturbances ◽

Integral Controller ◽

The Impact

AbstractFeedback control has enabled the success of automated technologies by mitigating the effects of variability, unknown disturbances, and noise. Similarly, feedback loops in biology reduce the impact of noise and help shape kinetic responses, but it is still unclear how to rationally design molecular controllers that approach the performance of controllers in traditional engineering applications, in particular the performance of integral controllers. Here, we describe a strategy to build molecular quasi-integral controllers by following two design principles: (1) a highly ultrasensitive response, which guarantees a small steady-state error, and (2) a tunable ultrasensitivity threshold, which determines the system equilibrium point (reference). We describe a molecular reaction network, which we name Brink motif, that satisfies these requirements by combining sequestration and an activation/deactivation cycle. We show that if ultrasensitivity conditions are satisfied, this motif operates as a quasi-integral controller and promotes homeostatic behavior of the closed-loop system (robust tracking of the input reference while rejecting disturbances). We propose potential biological implementations of Brink controllers and we illustrate different example applications with computational models.

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Tracking and optimizing human performance using deep reinforcement learning in closed-loop behavioral- and neuro- feedback: a proof of concept

10.1101/225995 ◽

2017 ◽

Author(s):

Martin Dinov ◽

Robert Leech

Keyword(s):

Reinforcement Learning ◽

Sustained Attention ◽

Human Performance ◽

Closed Loop ◽

Reaction Times ◽

Simulated Data ◽

General Purpose ◽

Proof Of Concept ◽

Response Task ◽

Continuous Actions

AbstractReinforcement learning (RL) is a general-purpose powerful machine learning framework within which we can model various deterministic, non-deterministic and complex environments. We applied RL to the problem of tracking and improving human sustained attention during a simple sustained attention to response task (SART) in a proof of concept study with two subjects, using state-of-the-art deep neural network-based RL in the form of Deep Q Networks (DQNs). While others have used RL in EEG settings previously, none have applied it in a neurofeedback (NFB) setting, which seems a natural problem within Brain Computer Interfaces (BCIs) to tackle using end-to-end RL in the form of DQNs, due to both the problem’s non-stationarity and the ability of RL to learn in a continuous setting. Furthermore, while many have explored phasic alerting previously, learning optimal alerting in a personalized way in real time is a less explored field, which we believe RL to be an most suitable solution for. First, we used empirically-derived simulated data of EEG and reaction times and subsequent parameter/algorithmic exploration within this simulated model to pick parameters for the DQN that are more likely to be optimal for the experimental setup and to explore the behavior of DQNs in this task setting. We then applied the method on two subjects and show that we get different but plausible results for both subjects, suggesting something about the behavior of DQNs in this setting. For this experimental part, we used parameters suggested to us by the simulation results. This RL-based behavioral- and neuro-feedback BCI method we have developed here is input feature agnostic and allows for complex continuous actions to be learned in other more complex closed-loop behavioral or neuro-feedback approaches.

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Asymptotic behaviour of the spectrum of a direct feedback control system

The Journal of the Australian Mathematical Society Series B Applied Mathematics ◽

10.1017/s033427000000758x ◽

1995 ◽

Vol 37 (1) ◽

pp. 86-98 ◽

Cited By ~ 3

Author(s):

Bao Zhu Guo

Keyword(s):

Control System ◽

Feedback Control ◽

Asymptotic Behaviour ◽

Closed Loop ◽

Analytic Extension ◽

Feedback Control System ◽

Closed Loop System ◽

System Operator ◽

System Energy ◽

Direct Feedback

AbstractThis paper establishes an estimate for the asymptotic behaviour of the spectrum of a direct strain feedback (DSF) control system. The results show that the system operator corresponding to the closed loop system cannot have an analytic extension and that the decay rate for the system energy is not proportional to the feedback constant.

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Passive coordination of nonlinear bilateral teleoperated manipulators

Robotica ◽

10.1017/s026357470500247x ◽

2006 ◽

Vol 24 (4) ◽

pp. 463-476 ◽

Cited By ~ 2

Author(s):

M. McIntyre ◽

W. Dixon ◽

D. Dawson ◽

E. Tatlicioglu

Keyword(s):

Closed Loop ◽

Dynamic Models ◽

Practical Importance ◽

Parametric Uncertainty ◽

Asymptotic Result ◽

Closed Loop System ◽

Incomplete System ◽

Significant Research ◽

Integral Feedback ◽

And Control

Significant research has been aimed at the development and control of teleoperator systems due to both the practical importance and the challenging theoretical nature of the problem. Two controllers are developed in this paper for a nonlinear teleoperator system that target coordination of the master and slave manipulators and passivity of the overall system. The first controller is proven to yield a semi-global asymptotic result in the presence of parametric uncertainty in the master and slave manipulator dynamic models. The second controller yields a global asymptotic result despite unmeasurable user and environmental input forces. To develop each controller, a transformation encodes the coordination and passivity objectives in the closed loop system. The coordinated system is forced to track a dynamic system to assist in meeting all control objectives. Finally, continuous nonlinear integral feedback terms are used to accommodate for incomplete system knowledge for both controllers. Lyapunov-based techniques are used to prove that all control objectives are met and that all signals are bounded.

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Parameter Sensitivity of Closed Loop System Response

Measurement and Control ◽

10.1177/002029406800100406 ◽

1968 ◽

Vol 1 (4) ◽

pp. T69-T71 ◽

Cited By ~ 1

Author(s):

H. A. Barker ◽

D. J. Murray-Smith

Keyword(s):

Control System ◽

Feedback Control ◽

Closed Loop ◽

Environmental Changes ◽

Design Procedure ◽

Loop System ◽

System Response ◽

Complex Frequency ◽

Feedback Control System ◽

Closed Loop System

The transient response of a linear feedback control system is characterised by the s plane pole positions of the closed-loop system transfer function, particularly by those of the dominant poles. During a design procedure these pole positions are changed by varying the parameters which are under the control of the designer until the transient performance specification of the system is satisfied. These pole positions can also change as a result of variations in system parameters not under the control of the designer, for example, due to component tolerances or environmental changes. A necessary part of the design procedure is therefore the determination of the sensitivities of the pole positions to system parameter variations. Insofar as the design procedure seeks to predict closed-loop system behaviour from open-loop system information it is desirable that these sensitivities are determined from the same information in order that sensitivity considerations may be introduced at an early stage. This may be accomplished by an extension of the complex frequency response method for feedback control system design.

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Therapeutic cell engineering: designing programmable synthetic genetic circuits in mammalian cells

Protein & Cell ◽

10.1007/s13238-021-00876-1 ◽

2021 ◽

Author(s):

Maysam Mansouri ◽

Martin Fussenegger

Keyword(s):

Mammalian Cells ◽

Closed Loop ◽

Control Cell ◽

Open Loop ◽

Proof Of Concept ◽

Genetic Circuits ◽

Cell Behaviors ◽

Digital Devices ◽

Closed Loop Systems ◽

Therapeutic Cell

AbstractCell therapy approaches that employ engineered mammalian cells for on-demand production of therapeutic agents in the patient’s body are moving beyond proof-of-concept in translational medicine. The therapeutic cells can be customized to sense user-defined signals, process them, and respond in a programmable and predictable way. In this paper, we introduce the available tools and strategies employed to design therapeutic cells. Then, various approaches to control cell behaviors, including open-loop and closed-loop systems, are discussed. We also highlight therapeutic applications of engineered cells for early diagnosis and treatment of various diseases in the clinic and in experimental disease models. Finally, we consider emerging technologies such as digital devices and their potential for incorporation into future cell-based therapies.

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