Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 11 de 11
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
IEEE Trans Cybern ; PP2020 Jul 22.
Artigo em Inglês | MEDLINE | ID: mdl-32697724

RESUMO

This article presents a data-driven method for computing reachable sets where active learning (AL) is used to reduce the computational burden. Set-based methods used to estimate reachable sets typically do not scale well with the state-space dimension, or rely heavily on the existence of a model. If such a model is not available, it is simple to generate state trajectory data by numerically simulating black-box oracles of systems (whose dynamics are unknown) from sampled initial conditions. Using these data samples, the estimation of reachable sets can be posed as a classification problem, wherein AL can intelligently select samples that are most informative and least similar to previously labeled samples. By exploiting submodularity, the actively learned samples can be selected efficiently, with bounded suboptimality. Our proposed framework is illustrated by estimating the domains of attractions of model predictive controllers (MPCs) and reinforcement learners. We also consider a scenario where there are two oracles that differ with respect to evaluation costs and labeling accuracy. We propose a framework to reduce the dependency of the expensive oracle in labeling samples using disagreement-based AL (DBAL). The potential of the DBAL algorithm is demonstrated on a solver selection problem for real-time MPC.

2.
Artigo em Inglês | MEDLINE | ID: mdl-32203039

RESUMO

We develop a method for obtaining safe initial policies for reinforcement learning via approximate dynamic programming (ADP) techniques for uncertain systems evolving with discrete-time dynamics. We employ the kernelized Lipschitz estimation to learn multiplier matrices that are used in semidefinite programming frameworks for computing admissible initial control policies with provably high probability. Such admissible controllers enable safe initialization and constraint enforcement while providing exponential stability of the equilibrium of the closed-loop system.

3.
Automatica (Oxf) ; 100: 336-348, 2019 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-31673164

RESUMO

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.

4.
J Process Control ; 76: 62-73, 2019 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-31178632

RESUMO

Current artificial pancreas systems (AP) operate via subcutaneous (SC) glucose sensing and SC insulin delivery. Due to slow diffusion and transport dynamics across the interstitial space, even the most sophisticated control algorithms in on-body AP systems cannot react fast enough to maintain tight glycemic control under the effect of exogenous glucose disturbances caused by ingesting meals or performing physical activity. Recent efforts made towards the development of an implantable AP have explored the utility of insulin infusion in the intraperitoneal (IP) space: a region within the abdominal cavity where the insulin-glucose kinetics are observed to be much more rapid than the SC space. In this paper, a series of canine experiments are used to determine the dynamic association between IP insulin boluses and plasma glucose levels. Data from these experiments are employed to construct a new mathematical model and to formulate a closed-loop control strategy to be deployed on an implantable AP. The potential of the proposed controller is demonstrated via in-silico experiments on an FDA-accepted benchmark cohort: the proposed design significantly outperforms a previous controller designed using artificial data (time in clinically acceptable glucose range: 97.3±1.5% vs. 90.1±5.6%). Furthermore, the robustness of the proposed closed-loop system to delays and noise in the measurement signal (for example, when glucose is sensed subcutaneously) and deleterious glycemic changes (such as sudden glucose decline due to physical activity) is investigated. The proposed model based on experimental canine data leads to the generation of more effective control algorithms and is a promising step towards fully automated and implantable artificial pancreas systems.

5.
IEEE/ACM Trans Comput Biol Bioinform ; 16(4): 1107-1116, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-28574365

RESUMO

High-throughput sequencing techniques have generated massive quantities of genotype data. Haplotype phasing has proven to be a useful and effective method for analyzing these data. However, the quality of phasing is undermined due to missing information. Imputation provides an effective means of improving the underlying genotype information. For model organisms, imputation can rely on an available reference genotype panel and a physical or genetic map. For non-model organisms, which often do not have a genotype panel, it is important to design an imputation technique that does not rely on reference data. Here, we present Accurate Data-Driven Imputation Technique (ADDIT), which is composed of two data-driven algorithms capable of handling data generated from model and non-model organisms. The non-model variant of ADDIT (referred to as ADDIT-NM) employs statistical inference methods to impute missing genotypes, whereas the model variant (referred to as ADDIT-M) leverages a supervised learning-based approach for imputation. We demonstrate that both variants of ADDIT are more accurate, faster, and require less memory than leading state-of-the-art imputation tools using model (human) and non-model (maize, apple, and grape) genotype data. Software Availability: The source code of ADDIT and test data sets are available at https://github.com/NDBL/ADDIT.


Assuntos
Biologia Computacional/métodos , Técnicas Genéticas , Genótipo , Algoritmos , Genômica/métodos , Técnicas de Genotipagem , Haplótipos , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Malus/genética , Modelos Estatísticos , Polimorfismo de Nucleotídeo Único , Reprodutibilidade dos Testes , Software , Vitis/genética , Zea mays/genética
6.
Sci Rep ; 8(1): 9936, 2018 07 02.
Artigo em Inglês | MEDLINE | ID: mdl-29967328

RESUMO

Second-generation DNA sequencing techniques generate short reads that can result in fragmented genome assemblies. Third-generation sequencing platforms mitigate this limitation by producing longer reads that span across complex and repetitive regions. However, the usefulness of such long reads is limited because of high sequencing error rates. To exploit the full potential of these longer reads, it is imperative to correct the underlying errors. We propose HECIL-Hybrid Error Correction with Iterative Learning-a hybrid error correction framework that determines a correction policy for erroneous long reads, based on optimal combinations of decision weights obtained from short read alignments. We demonstrate that HECIL outperforms state-of-the-art error correction algorithms for an overwhelming majority of evaluation metrics on diverse, real-world data sets including E. coli, S. cerevisiae, and the malaria vector mosquito A. funestus. Additionally, we provide an optional avenue of improving the performance of HECIL's core algorithm by introducing an iterative learning paradigm that enhances the correction policy at each iteration by incorporating knowledge gathered from previous iterations via data-driven confidence metrics assigned to prior corrections.


Assuntos
Sequenciamento de Nucleotídeos em Larga Escala/métodos , Aprendizado de Máquina , Análise de Sequência de DNA/métodos , Escherichia coli/genética , Mosquitos Vetores/genética , Sequências Repetitivas de Ácido Nucleico , Saccharomyces cerevisiae/genética
7.
IEEE Trans Biomed Eng ; 65(3): 575-586, 2018 03.
Artigo em Inglês | MEDLINE | ID: mdl-28541890

RESUMO

OBJECTIVE: The development of artificial pancreas (AP) technology for deployment in low-energy, embedded devices is contingent upon selecting an efficient control algorithm for regulating glucose in people with type 1 diabetes mellitus. In this paper, we aim to lower the energy consumption of the AP by reducing controller updates, that is, the number of times the decision-making algorithm is invoked to compute an appropriate insulin dose. METHODS: Physiological insights into glucose management are leveraged to design an event-triggered model predictive controller (MPC) that operates efficiently, without compromising patient safety. The proposed event-triggered MPC is deployed on a wearable platform. Its robustness to latent hypoglycemia, model mismatch, and meal misinformation is tested, with and without meal announcement, on the full version of the US-FDA accepted UVA/Padova metabolic simulator. RESULTS: The event-based controller remains on for 18 h of 41 h in closed loop with unannounced meals, while maintaining glucose in 70-180 mg/dL for 25 h, compared to 27 h for a standard MPC controller. With meal announcement, the time in 70-180 mg/dL is almost identical, with the controller operating a mere 25.88% of the time in comparison with a standard MPC. CONCLUSION: A novel control architecture for AP systems enables safe glycemic regulation with reduced processor computations. SIGNIFICANCE: Our proposed framework integrated seamlessly with a wide variety of popular MPC variants reported in AP research, customizes tradeoff between glycemic regulation and efficacy according to prior design specifications, and eliminates judicious prior selection of controller sampling times.


Assuntos
Diabetes Mellitus Tipo 1/tratamento farmacológico , Modelos Estatísticos , Pâncreas Artificial , Algoritmos , Automonitorização da Glicemia , Humanos , Hipoglicemia/tratamento farmacológico , Hipoglicemia/prevenção & controle , Hipoglicemiantes/administração & dosagem , Hipoglicemiantes/uso terapêutico
8.
Diabetes Obes Metab ; 19(12): 1698-1705, 2017 12.
Artigo em Inglês | MEDLINE | ID: mdl-28474383

RESUMO

AIMS: To compare intraperitoneal (IP) to subcutaneous (SC) insulin delivery in an artificial pancreas (AP). RESEARCH DESIGN AND METHODS: Ten adults with type 1 diabetes participated in a non-randomized, non-blinded sequential AP study using the same SC glucose sensing and Zone Model Predictive Control (ZMPC) algorithm adjusted for insulin clearance. On first admission, subjects underwent closed-loop control with SC delivery of a fast-acting insulin analogue for 24 hours. Following implantation of a DiaPort IP insulin delivery system, the identical 24-hour trial was performed with IP regular insulin delivery. The clinical protocol included 3 unannounced meals with 70, 40 and 70 g carbohydrate, respectively. Primary endpoint was time spent with blood glucose (BG) in the range of 80 to 140 mg/dL (4.4-7.7 mmol/L). RESULTS: Percent of time spent within the 80 to 140 mg/dL range was significantly higher for IP delivery than for SC delivery: 39.8 ± 7.6 vs 25.6 ± 13.1 ( P = .03). Mean BG (mg/dL) and percent of time spent within the broader 70 to 180 mg/dL range were also significantly better for IP insulin: 151.0 ± 11.0 vs 190.0 ± 31.0 ( P = .004) and 65.7 ± 9.2 vs 43.9 ± 14.7 ( P = .001), respectively. Superiority of glucose control with IP insulin came from the reduced time spent in hyperglycaemia (>180 mg/dL: 32.4 ± 8.9 vs 53.5 ± 17.4, P = .014; >250 mg/dL: 5.9 ± 5.6 vs 23.0 ± 11.3, P = .0004). Higher daily doses of insulin (IU) were delivered with the IP route (43.7 ± 0.1 vs 32.3 ± 0.1, P < .001) with no increased percent time spent <70 mg/dL (IP: 2.5 ± 2.9 vs SC: 4.1 ± 5.3, P = .42). CONCLUSIONS: Glycaemic regulation with fully-automated AP delivering IP insulin was superior to that with SC insulin delivery. This pilot study provides proof-of-concept for an AP system combining a ZMPC algorithm with IP insulin delivery.


Assuntos
Diabetes Mellitus Tipo 1/terapia , Hiperglicemia/prevenção & controle , Hipoglicemia/prevenção & controle , Hipoglicemiantes/administração & dosagem , Sistemas de Infusão de Insulina , Insulina Lispro/administração & dosagem , Pâncreas Artificial , Adulto , Algoritmos , Glicemia/análise , Diabetes Mellitus Tipo 1/sangue , Feminino , França , Hemoglobina A Glicada/análise , Humanos , Hipoglicemia/induzido quimicamente , Hipoglicemiantes/efeitos adversos , Hipoglicemiantes/uso terapêutico , Infusões Parenterais , Infusões Subcutâneas , Sistemas de Infusão de Insulina/efeitos adversos , Insulina Lispro/efeitos adversos , Insulina Lispro/uso terapêutico , Insulina Regular Humana/administração & dosagem , Insulina Regular Humana/efeitos adversos , Insulina Regular Humana/uso terapêutico , Masculino , Pessoa de Meia-Idade , Pâncreas Artificial/efeitos adversos , Projetos Piloto , Estudo de Prova de Conceito
9.
Artigo em Inglês | MEDLINE | ID: mdl-25570727

RESUMO

The hypothalamic-pituitary-adrenal (HPA) axis is critical in maintaining homeostasis under physical and psychological stress by modulating cortisol levels in the body. Dysregulation of cortisol levels is linked to numerous stress-related disorders. In this paper, an automated treatment methodology is proposed, employing a variant of nonlinear model predictive control (NMPC), called explicit MPC (EMPC). The controller is informed by an unknown input observer (UIO), which estimates various hormonal levels in the HPA axis system in conjunction with the magnitude of the stress applied on the body, based on measured concentrations of adreno-corticotropic hormones (ACTH). The proposed closed-loop control strategy is tested on multiple in silico patients and the effectiveness of the controller performance is demonstrated.


Assuntos
Sistema Hipotálamo-Hipofisário/fisiopatologia , Modelos Biológicos , Dinâmica não Linear , Sistema Hipófise-Suprarrenal/fisiopatologia , Simulação por Computador , Humanos , Estresse Psicológico/fisiopatologia
10.
Artigo em Inglês | MEDLINE | ID: mdl-23293047

RESUMO

Model-based design of experiments (MBDOE) assists in the planning of highly effective and efficient experiments. Although the foundations of this field are well-established, the application of these techniques to understand cellular processes is a fertile and rapidly advancing area as the community seeks to understand ever more complex cellular processes and systems. This review discusses the MBDOE paradigm along with applications and challenges within the context of cellular processes and systems. It also provides a brief tutorial on Fisher information matrix (FIM)-based and Bayesian experiment design methods along with an overview of existing software packages and computational advances that support MBDOE application and adoption within the Systems Biology community. As cell-based products and biologics progress into the commercial sector, it is anticipated that MBDOE will become an essential practice for design, quality control, and production.


Assuntos
Biologia Celular , Teoria da Informação , Modelos Biológicos , Projetos de Pesquisa , Teorema de Bayes , Simulação por Computador , Humanos , Biologia de Sistemas
11.
Mol Biol Rep ; 40(2): 1103-25, 2013 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-23086300

RESUMO

Biochemical networks comprise many diverse components and interactions between them. It has intracellular signaling, metabolic and gene regulatory pathways which are highly integrated and whose responses are elicited by extracellular actions. Previous modeling techniques mostly consider each pathway independently without focusing on the interrelation of these which actually functions as a single system. In this paper, we propose an approach of modeling an integrated pathway using an event-driven modeling tool, i.e., Petri nets (PNs). PNs have the ability to simulate the dynamics of the system with high levels of accuracy. The integrated set of signaling, regulatory and metabolic reactions involved in Saccharomyces cerevisiae's HOG pathway has been collected from the literature. The kinetic parameter values have been used for transition firings. The dynamics of the system has been simulated and the concentrations of major biological species over time have been observed. The phenotypic characteristics of the integrated system have been investigated under two conditions, viz., under the absence and presence of osmotic pressure. The results have been validated favorably with the existing experimental results. We have also compared our study with the study of idFBA (Lee et al., PLoS Comput Biol 4:e1000-e1086, 2008) and pointed out the differences between both studies. We have simulated and monitored concentrations of multiple biological entities over time and also incorporated feedback inhibition by Ptp2 which has not been included in the idFBA study. We have concluded that our study is the first to the best of our knowledge to model signaling, metabolic and regulatory events in an integrated form through PN model framework. This study is useful in computational simulation of system dynamics for integrated pathways as there are growing evidences that the malfunctioning of the interplay among these pathways is associated with disease.


Assuntos
Simulação por Computador , Proteínas Quinases Ativadas por Mitógeno/fisiologia , Modelos Biológicos , Proteínas de Saccharomyces cerevisiae/fisiologia , Saccharomyces cerevisiae/fisiologia , Retroalimentação Fisiológica , Regulação Fúngica da Expressão Gênica , Redes Reguladoras de Genes , Redes e Vias Metabólicas , Pressão Osmótica , Monoéster Fosfórico Hidrolases/fisiologia , Transdução de Sinais , Estresse Fisiológico , Equilíbrio Hidroeletrolítico
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...