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BACKGROUND AND PURPOSE: The ability to measure specific molecules at multiple sites within the body simultaneously, and with a time resolution of seconds, could greatly advance our understanding of drug transport and elimination. EXPERIMENTAL APPROACH: As a proof-of-principle demonstration, here we describe the use of electrochemical aptamer-based (EAB) sensors to measure transport of the antibiotic vancomycin from the plasma (measured in the jugular vein) to the cerebrospinal fluid (measured in the lateral ventricle) of live rats with temporal resolution of a few seconds. KEY RESULTS: In our first efforts, we made measurements solely in the ventricle. Doing so we find that, although the collection of hundreds of concentration values over a single drug lifetime enables high-precision estimates of the parameters describing intracranial transport, due to a mathematical equivalence, the data produce two divergent descriptions of the drug's plasma pharmacokinetics that fit the in-brain observations equally well. The simultaneous collection of intravenous measurements, however, resolves this ambiguity, enabling high-precision (typically of ±5 to ±20% at 95% confidence levels) estimates of the key pharmacokinetic parameters describing transport from the blood to the cerebrospinal fluid in individual animals. CONCLUSIONS AND IMPLICATIONS: The availability of simultaneous, high-density 'in-vein' (plasma) and 'in-brain' (cerebrospinal fluid) measurements provides unique opportunities to explore the assumptions almost universally employed in earlier compartmental models of drug transport, allowing the quantitative assessment of, for example, the pharmacokinetic effects of physiological processes such as the bulk transport of the drug out of the CNS via the dural venous sinuses.
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Knowledge of drug concentrations in the brains of behaving subjects remains constrained on a number of dimensions, including poor temporal resolution and lack of real-time data. Here, however, we demonstrate the ability of electrochemical aptamer-based sensors to support seconds-resolved, real-time measurements of drug concentrations in the brains of freely moving rats. Specifically, using such sensors, we achieve <4 µM limits of detection and 10-s resolution in the measurement of procaine in the brains of freely moving rats, permitting the determination of the pharmacokinetics and concentration-behavior relations of the drug with high precision for individual subjects. In parallel, we have used closed-loop feedback-controlled drug delivery to hold intracranial procaine levels constant (±10%) for >1.5 hours. These results demonstrate the utility of such sensors in (i) the determination of the site-specific, seconds-resolved neuropharmacokinetics, (ii) enabling the study of individual subject neuropharmacokinetics and concentration-response relations, and (iii) performing high-precision control over intracranial drug levels.
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Encéfalo , Procaína , Ratas , Animales , RetroalimentaciónRESUMEN
We introduce a performance-guaranteed limbic system-inspired control (LISIC) strategy for nonlinear multi-agent systems (MASs) with uncertain high-order dynamics and external perturbations, where each agent in the MAS incorporates a LISIC structure to support the consensus controller. This novel approach, which we call double integrator LISIC (DILISIC), is designed to imitate double integrator dynamics after closing the agent-specific control loop, allowing the control designer to apply consensus techniques specifically formulated for double integrator agents. The objective of each DILISIC structure is then to identify and compensate model differences between the theoretical assumptions considered when tuning the consensus protocol and the actual conditions encountered in the real-time system to be controlled. A Lyapunov analysis is provided to demonstrate the stability of the closed-loop MAS enhanced with the DILISIC. Additionally, the stabilization of a complex system via DILISIC is addressed in a synthetic scenario: the consensus control of a team of flexible single-link arms. The dynamics of these agents are of fourth order, contain uncertainties, and are subject to external perturbations. The numerical results validate the applicability of the proposed method.
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We address the prediction of the number of new cases and deaths for the coronavirus disease 2019 (COVID-19) over a future horizon from historical data (forecasting). We use a model-based approach based on a stochastic Susceptible-Infections-Removed (SIR) model with time-varying parameters, which captures the evolution of the disease dynamics in response to changes in social behavior, non-pharmaceutical interventions, and testing rates. We show that, in the presence of asymptomatic cases, such model includes internal parameters and states that cannot be uniquely identified solely on the basis of measurements of new cases and deaths, but this does not preclude the construction of reliable forecasts for future values of these measurements. Such forecasts and associated confidence intervals can be computed using an iterative algorithm based on nonlinear optimization solvers, without the need for Monte Carlo sampling. Our results have been validated on an extensive COVID-19 dataset covering the period from March through December 2020 on 144 regions around the globe.
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By, in effect, rendering pharmacokinetics an experimentally adjustable parameter, the ability to perform feedback-controlled dosing informed by high-frequency in vivo drug measurements would prove a powerful tool for both pharmacological research and clinical practice. Efforts to this end, however, have historically been thwarted by an inability to measure in vivo drug levels in real time and with sufficient convenience and temporal resolution. In response, we describe a closed-loop, feedback-controlled delivery system that uses drug level measurements provided by an in vivo electrochemical aptamer-based (E-AB) sensor to adjust dosing rates every 7 s. The resulting system supports the maintenance of either constant or predefined time-varying plasma drug concentration profiles in live rats over many hours. For researchers, the resultant high-precision control over drug plasma concentrations provides an unprecedented opportunity to (1) map the relationships between pharmacokinetics and clinical outcomes, (2) eliminate inter- and intrasubject metabolic variation as a confounding experimental variable, (3) accurately simulate human pharmacokinetics in animal models, and (4) measure minute-to-minute changes in a drug's pharmacokinetic behavior in response to changing health status, diet, drug-drug interactions, or other intrinsic and external factors. In the clinic, feedback-controlled drug delivery would improve our ability to accurately maintain therapeutic drug levels in the face of large, often unpredictable intra- and interpatient metabolic variation. This, in turn, would improve the efficacy and safety of therapeutic intervention, particularly for the most gravely ill patients, for whom metabolic variability is highest and the margin for therapeutic error is smallest.
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This paper proposes a control algorithm based on adaptive dynamic programming to solve the infinite-horizon optimal control problem for known deterministic nonlinear systems with saturating actuators and nonquadratic cost functionals. The algorithm is based on an actor/critic framework, where a critic neural network (NN) is used to learn the optimal cost, and an actor NN is used to learn the optimal control policy. The adaptive control nature of the algorithm requires a persistence of excitation condition to be a priori validated, but this can be relaxed using previously stored data concurrently with current data in the update of the critic NN. A robustifying control term is added to the controller to eliminate the effect of residual errors, leading to the asymptotically stability of the closed-loop system. Simulation results show the effectiveness of the proposed approach for a controlled Van der Pol oscillator and also for a power system plant.
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Many biochemical reaction networks are inherently multiscale in time and in the counts of participating molecular species. A standard technique to treat different time scales in the stochastic kinetics framework is averaging or quasi-steady-state analysis: it is assumed that the fast dynamics reaches its equilibrium (stationary) distribution on a time scale where the slowly varying molecular counts are unlikely to have changed. We derive analytic equilibrium distributions for various simple biochemical systems, such as enzymatic reactions and gene regulation models. These can be directly inserted into simulations of the slow time-scale dynamics. They also provide insight into the stimulus-response of these systems. An important model for which we derive the analytic equilibrium distribution is the binding of dimer transcription factors (TFs) that first have to form from monomers. This gene regulation mechanism is compared to the cases of the binding of simple monomer TFs to one gene or to multiple copies of a gene, and to the cases of the cooperative binding of two or multiple TFs to a gene. The results apply equally to ligands binding to enzyme molecules.
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Regulación de la Expresión Génica/genética , Modelos Genéticos , Modelos Estadísticos , Factores de Transcripción/química , Factores de Transcripción/genética , Transcripción Genética/genética , Activación Transcripcional/genética , Animales , Simulación por Computador , Transferencia de Energía/genética , Humanos , Modelos Químicos , Transducción de Señal/genética , Procesos EstocásticosRESUMEN
Organisms respond to changes in their environment over a wide range of biological and temporal scales. Such phenotypic plasticity can involve developmental, behavioral, physiological, and genetic shifts. The adaptive value of a plastic response is known to depend on the nature of the information that is available to the organism as well as the direct and indirect costs of the plastic response. We modeled the dynamic process of simple gene regulatory networks as they responded to temporal fluctuations in environmental conditions. We simulated the evolution of networks to determine when genes that function solely as transcription factors, with no direct function of their own, are beneficial to the function of the network. When there is perfect information about the environment and there is no timing information to be extracted then there is no advantage to adding pure transcription factor genes to the network. In contrast, when there is either timing information that can be extracted or only indirect information about the current state of the environment then additional transcription factor genes improve the evolved network fitness.
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Evolución Molecular , Redes Reguladoras de Genes , Modelos Genéticos , Animales , Ecosistema , Factores de Tiempo , Factores de Transcripción/genética , Factores de Transcripción/metabolismoRESUMEN
Many protein and mRNA species occur at low molecular counts within cells, and hence are subject to large stochastic fluctuations in copy numbers over time. Development of computationally tractable frameworks for modelling stochastic fluctuations in population counts is essential to understand how noise at the cellular level affects biological function and phenotype. We show that stochastic hybrid systems (SHSs) provide a convenient framework for modelling the time evolution of population counts of different chemical species involved in a set of biochemical reactions. We illustrate recently developed techniques that allow fast computations of the statistical moments of the population count, without having to run computationally expensive Monte Carlo simulations of the biochemical reactions. Finally, we review different examples from the literature that illustrate the benefits of using SHSs for modelling biochemical processes.
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Fenómenos Bioquímicos/fisiología , Modelos Biológicos , Dinámicas no Lineales , Procesos Estocásticos , Escherichia coli/metabolismo , Redes Reguladoras de Genes/fisiología , CinéticaRESUMEN
Autoregulatory feedback loops, where the protein expressed from a gene inhibits or activates its own expression are common gene network motifs within cells. In these networks, stochastic fluctuations in protein levels are attributed to two factors: intrinsic noise (i.e., the randomness associated with mRNA/protein expression and degradation) and extrinsic noise (i.e., the noise caused by fluctuations in cellular components such as enzyme levels and gene-copy numbers). We present results that predict the level of both intrinsic and extrinsic noise in protein numbers as a function of quantities that can be experimentally determined and/or manipulated, such as the response time of the protein and the level of feedback strength. In particular, we show that for a fixed average number of protein molecules, decreasing response times leads to attenuation of both protein intrinsic and extrinsic noise, with the extrinsic noise being more sensitive to changes in the response time. We further show that for autoregulatory networks with negative feedback, the protein noise levels can be minimal at an optimal level of feedback strength. For such cases, we provide an analytical expression for the highest level of noise suppression and the amount of feedback that achieves this minimal noise. These theoretical results are shown to be consistent and explain recent experimental observations. Finally, we illustrate how measuring changes in the protein noise levels as the feedback strength is manipulated can be used to determine the level of extrinsic noise in these gene networks.