The Dynamics of Hybrid Metabolic-Genetic Oscillators

The synthetic construction of intracellular circuits is frequently hindered by a poor knowledge of appropriate kinetics and precise rate parameters. Here, we use generalized modeling (GM) to study the dynamical behavior of topological models of a family of hybrid metabolic-genetic circuits known as "metabolators." Under mild assumptions on the kinetics, we use GM to analytically prove that all explicit kinetic models which are topologically analogous to one such circuit, the "core metabolator," cannot undergo Hopf bifurcations. Then, we examine more detailed models of the metabolator. Inspired by the experimental observation of a Hopf bifurcation in a synthetically constructed circuit related to the core metabolator, we apply GM to identify the critical components of the synthetically constructed metabolator which must be reintroduced in order to recover the Hopf bifurcation. Next, we study the dynamics of a re-wired version of the core metabolator, dubbed the "reverse" metabolator, and show that it exhibits a substantially richer set of dynamical behaviors, including both local and global oscillations. Prompted by the observation of relaxation oscillations in the reverse metabolator, we study the role that a separation of genetic and metabolic time scales may play in its dynamics, and find that widely separated time scales promote stability in the circuit. Our results illustrate a generic pipeline for vetting the potential success of a potential circuit design, simply by studying the dynamics of the corresponding generalized model

Feedback-based admission control for hard real-time task allocation under dynamic workload on many-core systems

In hard real-time systems, a computationally expensive schedulability analysis has to be performed for every task. Fulfilling this requirement is particularly tough when system workload and service capacity are not available a priori and thus the analysis has to be conducted at runtime. This paper presents an approach for applying controltheory-based admission control to predict the task schedulability so that the exact schedulability analysis is performed only to the tasks with positive prediction results. In case of a careful fine-tuning of parameters, the proposed approach can be successfully applied even to many-core embedded systems with hard real-time constraints and other time-critical systems. The provided experimental results demonstrate that, on average, only 62% of the schedulability tests have to be performed in comparison with the traditional, open-loop approach. The proposed approach is particularly beneficial for heavier workloads, where the number of executed tasks is almost unchanged in comparison with the traditional open-loop approach. By our approach, only 32% of exact schedulability tests have to be conducted. Moreover, for the analysed industrial workloads with dependent jobs, the proposed technique admitted and executed 11% more tasks while not violating any timing constraints

A Single-Loop DC Motor Control System Design with a Desired Aperiodic Degree of Stability

The application of the original analytical approach for Pi-controller synthesis of a stable second-order plant is considered. This approach allows finding controller parameters without any intensive computing by using the direct expressions. The plant model is obtained on the basis of identification, which is based on the automated real-interpolation method. The results of natural experiments are given

Resilient Parameter-Invariant Control With Application to Vehicle Cruise Control

This work addresses the general problem of resilient control of unknown stochastic linear time-invariant (LTI) systems in the presence of sensor attacks. Motivated by a vehicle cruise control application, this work considers a first order system with multiple measurements, of which a bounded subset may be corrupted. A frequency-domain-designed resilient parameter-invariant controller is introduced that simultaneously minimizes the effect of corrupted sensors, while maintaining a desired closed-loop performance, invariant to unknown model parameters. Simulated results illustrate that the resilient parameter-invariant controller is capable of stabilizing unknown state disturbances and can perform state trajectory tracking

Series-Parallel and Parallel Identification Schemes for a Class of Continuous Nonlinear Systems

Fig. 4(a) shows the parameter estimates under the existence of the measmable disturbance (di = 5, a\ = 0) with the parameter estimates under the ideal condition (di = a\ = 0) overlaid. Since the inserted DDR's remove the disturbance from the inputoutput relation, the disturbance does not slow down the identification speed. Fig. 4(6) shows the parameter estimates under the existence of the unmeasurable disturbance (di = 0, d 2 = 1) with the parameter estimates under the ideal condition (di = di = 0) overlaid. There exists no difference between the two cases as far as the identification speed is concerned. In the simulation, the step disturbances, di and d 2 , were injected to the plant at k = 0. Thus, strictly speaking, at k = 0, di(k) and d 2 (fc) did not satisfy equation V Conclusions Adverse effects of deterministic disturbances in linear identification have been pointed out, and a method to remove such effects has been presented. This method works for measurable and unmeasurable disturbances which can be regarded as the outputs of free systems with known dynamics. The unmeasurable disturbance must always be removed to achieve successful identification. When the disturbance is measurable, however, it does not have to be removed if it can provide a positive contribution to identification. A constant disturbance was shown to slow down the identification speed. The best results will be obtained if one selects a DDR which removes only undesirable disturbances. In this technical brief, discrete series-parallel and parallel identification schemes for single-input, single-output systems were considered. The same principle, however, can be extended to other situations including the continuous time case and multi-input, and multi-output case. References 1 Astrom, K. J., and Eykhoff, P., "System Identification -A Survey," Automatica, Vol. 7, 1971, pp. 123-16

A method for the reconstruction of unknown non-monotonic growth functions in the chemostat

We propose an adaptive control law that allows one to identify unstable steady states of the open-loop system in the single-species chemostat model without the knowledge of the growth function. We then show how one can use this control law to trace out (reconstruct) the whole graph of the growth function. The process of tracing out the graph can be performed either continuously or step-wise. We present and compare both approaches. Even in the case of two species in competition, which is not directly accessible with our approach due to lack of controllability, feedback control improves identifiability of the non-dominant growth rate.Comment: expansion of ideas from proceedings paper (17 pages, 8 figures), proceedings paper is version v

Active Learning in Persistent Surveillance UAV Missions

The performance of many complex UAV decision-making problems can be extremely sensitive to small errors in the model parameters. One way of mitigating this sensitivity is by designing algorithms that more effectively learn the model throughout the course of a mission. This paper addresses this important problem by considering model uncertainty in a multi-agent Markov Decision Process (MDP) and using an active learning approach to quickly learn transition model parameters. We build on previous research that allowed UAVs to passively update model parameter estimates by incorporating new state transition observations. In this work, however, the UAVs choose to actively reduce the uncertainty in their model parameters by taking exploratory and informative actions. These actions result in a faster adaptation and, by explicitly accounting for UAV fuel dynamics, also mitigates the risk of the exploration. This paper compares the nominal, passive learning approach against two methods for incorporating active learning into the MDP framework: (1) All state transitions are rewarded equally, and (2) State transition rewards are weighted according to the expected resulting reduction in the variance of the model parameter. In both cases, agent behaviors emerge that enable faster convergence of the uncertain model parameters to their true values

Identification of single-input–single-output quantum linear systems

The purpose of this paper is to investigate system identification for single-input–single-output general (active or passive) quantum linear systems. For a given input we address the following questions: (1) Which parameters can be identified by measuring the output? (2) How can we construct a system realization from sufficient input-output data? We show that for time-dependent inputs, the systems which cannot be distinguished are related by symplectic transformations acting on the space of system modes. This complements a previous result of Guţă and Yamamoto [IEEE Trans. Autom. Control 61, 921 (2016)] for passive linear systems. In the regime of stationary quantum noise input, the output is completely determined by the power spectrum. We define the notion of global minimality for a given power spectrum, and characterize globally minimal systems as those with a fully mixed stationary state. We show that in the case of systems with a cascade realization, the power spectrum completely fixes the transfer function, so the system can be identified up to a symplectic transformation. We give a method for constructing a globally minimal subsystem direct from the power spectrum. Restricting to passive systems the analysis simplifies so that identifiability may be completely understood from the eigenvalues of a particular system matrix

ggstThe role of tendon microcirculation in Achilles and patellar tendinopathy

Tendinopathy is of distinct interest as it describes a painful tendon disease with local tenderness, swelling and pain associated with sonographic features such as hypoechogenic texture and diameter enlargement. Recent research elucidated microcirculatory changes in tendinopathy using laser Doppler flowmetry and spectrophotometry such as at the Achilles tendon, the patellar tendon as well as at the elbow and the wrist level. Tendon capillary blood flow is increased at the point of pain. Tendon oxygen saturation as well as tendon postcapillary venous filling pressures, determined non-invasively using combined Laser Doppler flowmetry and spectrophotometry, can quantify, in real-time, how tendon microcirculation changes over with pathology or in response to a given therapy. Tendon oxygen saturation can be increased by repetitive, intermittent short-term ice applications in Achilles tendons; this corresponds to 'ischemic preconditioning', a method used to train tissue to sustain ischemic damage. On the other hand, decreasing tendon oxygenation may reflect local acidosis and deteriorating tendon metabolism. Painful eccentric training, a common therapy for Achilles, patellar, supraspinatus and wrist tendinopathy decreases abnormal capillary tendon flow without compromising local tendon oxygenation. Combining an Achilles pneumatic wrap with eccentric training changes tendon microcirculation in a different way than does eccentric training alone; both approaches reduce pain in Achilles tendinopathy. The microcirculatory effects of measures such as extracorporeal shock wave therapy as well as topical nitroglycerine application are to be studied in tendinopathy as well as the critical question of dosage and maintenance. Interestingly it seems that injection therapy using color Doppler for targeting the area of neovascularisation yields to good clinical results with polidocanol sclerosing therapy, but also with a combination of epinephrine and lidocaine