A service‐oriented Grid environment for integration of distributed kidney models and resources

Chu, Xingchen; Lonie, Andrew; Harris, Peter; Thomas, Sophie; Buyya, Rajkumar

doi:10.1002/cpe.1285

Cited by 11 publications

(14 citation statements)

References 30 publications

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“…Visitors to the GridPortal are then given interactive access to these curated models and their descriptions and may modify selected parameters to launch custom simulations. A KidneyGrid portal has been developed145 (Figures 8 & 9) and is being seeded with some of the models mentioned here.…”

Section: Toward An Open Renal Physiomementioning

confidence: 99%

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Kidney modeling and systems physiology

Thomas

2009

WIREs Mechanisms of Disease

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We present an overview of currently available resources in renal systems physiology and indicate directions for development toward the renal physiome. After a brief resumé of objectives, we summarize legacy-modeling studies that can serve as the foundation for a more complete toolset. These include detailed models of practically all renal cell types and nephron segments and a variety of models of nephro-vascular exchanges in the medulla, of renal hemodynamics, and studies of tubuloglomerular feedback and autoregulation. Recent detailed anatomical reconstructions have brought surprising new results to bear on classic unsolved problems. In parallel with the modeling environment, progress has been made toward the quantitative database and model repository resources that must accompany the modeling environment in order to attain the goal of an open-ended, flexible, and collaborative infrastructure for renal systems biology, with an indication of prospects for integration with initiatives in the larger IUPS Physiome Project.

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Section: Toward An Open Renal Physiomementioning

confidence: 99%

Section: Toward An Open Renal Physiomementioning

confidence: 99%

Kidney modeling and systems physiology

Thomas

2009

WIREs Mechanisms of Disease

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“…First, they are imprecise because they were developed using conventional mathematical approaches that are not suitable for modelling of complex physiological organs such as kidney. Second, they are often not used by clinicians because of their mathematical difficulty and because their parameters cannot be easily determined (Vttamsingh et al, 1985;Chang and Fujita, 1999;Chu et al, 2007). Third, they do not consider the non-linear response of kidney tissues in different physiological normal and abnormal cases.…”

Section: Introductionmentioning

confidence: 98%

Adaptive modelling of creatinine concentration in human blood

Amer

2011

IJBET

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The need and importance of modelling and evaluation of renal function has grown with the increasing need for kidney replacement, design of artificial kidney and proper diagnosis and treatment of kidney disorders. This paper explores a fuzzy model of the creatinine concentration (C r ) in human blood using adaptive Neurofuzzy Inference System (NFIS). This model aims to predict blood creatinine using blood urea, sodium and potassium without a need for actual measurement of C r in blood. The creatinine level in blood is an efficient tool for evaluation and assessment of renal function. The proposed model was validated by comparing the predicted creatinine values from the model with the experimental creatinine values. The obtained results showed that the developed model is capable of estimating the blood creatinine with a Root-Mean Square Error (RMSE) of 0.76 and also the predicted C r values agree closely with the experimentally measured C r values. Practically, this implies that the developed neurofuzzy model is adequate one for prediction of creatinine concentration in human blood and consequently will lead to a proper evaluation of renal function. He is an Associate Professor at the Biomedical Engineering Department in Jordan University of Science and Technology. His research interests include design of intelligent medical systems, mobile-phone-based medical systems, fuzzy and neuro-fuzzy-based systems, intelligent medical sensors, intelligent therapeutic systems and remote monitoring of physiological signals. He has two granted patents and published more than 30 research papers in international journals and conference proceedings.

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“…The development, architecture, features and performance of the KIDNEYGRID platform included within the Virtual Kidney have been described by Chu et al (2008). The steps involved in running a Grid-enabled session are the following.…”

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confidence: 99%

The Virtual Kidney: an eScience interface and Grid portal

Harris

Buyya

Chu

et al. 2009

Phil. Trans. R. Soc. A.

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The Virtual Kidney uses a web interface and distributed computing to provide experimental scientists and analysts with access to computational simulations and knowledge databases hosted in geographically separated laboratories. Users can explore a variety of complex models without requiring the specific programming environment in which applications have been developed. This initiative exploits high-bandwidth communication networks for collaborative research and for shared access to knowledge resources. The Virtual Kidney has been developed within a specialist community of renal scientists but is transferable to other areas of research requiring interaction between published literature and databases, theoretical models and simulations and the formulation of effective experimental designs.A web-based three-dimensional interface provides access to experimental data, a parameter database and mathematical models. A multi-scale kidney reconstruction includes blood vessels and serially sectioned nephrons. Selection of structures provides links to the database, returning parameter values and extracts from the literature.Models are run locally or remotely with a Grid resource broker managing scheduling, monitoring and visualization of simulation results and application, credential and resource allocation. Simulation results are viewed graphically or as scaled colour gradients on the Virtual Kidney structures, allowing visual and quantitative appreciation of the effects of simulated parameter changes.

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A service‐oriented Grid environment for integration of distributed kidney models and resources

Cited by 11 publications

References 30 publications

Kidney modeling and systems physiology

Kidney modeling and systems physiology

Adaptive modelling of creatinine concentration in human blood

The Virtual Kidney: an eScience interface and Grid portal

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