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Use of a food and drug administration-approved type 1 diabetes mellitus simulator to evaluate and optimize a proportional-integral-derivative controller

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Journal of Diabetes Science and technology 2012年第6期6卷 1401-1412页

作者： Laxminarayan, Srinivas Reifman, Jaques Steil, Garry M. DoD Biotechnology High-Performance Computing Software Applications Institute Telemedicine and Advanced Technology Research Center U.S. Army Medical Research and Materiel Command Fort Detrick MD 21702 504 Scott St. United States Children's Hospital Boston Boston MA United States

Background: Clinical studies have shown that the Medtronic proportional-integral-derivative (PID) control with insulin feedback (IFB) provides stable 24 h glucose control, but with high postprandial glucose. We coupled this algorithm to a Food and Drug Administration-approved type 1 diabetes mellitus simulator to determine whether a proportional-derivative controller with preprogrammed basal rates (PD_BASAL) would have better performance. Methods: We performed simulation studies on 10 adult subjects to (1) obtain the basal profiles for the PD_BASAL controller;(2) define the pharmacokinetic/pharmacodynamic profile used to efect IFB, (3) optimize the PID and PD_BASAL control parameters, (4) evaluate improvements obtained with IFB, and (5) develop a method to simulate changes in insulin sensitivity and assess the ability of each algorithm to respond to such changes. Results: PD_BASAL control significantly reduced peak postprandial glucose [252 (standard error = 11) versus 279 (14) mg/dl;p < .001] and increased nadir glucose [102 (3) versus 92 (3) mg/dl;p < .001] compared with PID control (both implemented with IFB). However, with PD_BASAL control, fasting glucose remained elevated following a 30% decrease in insulin sensitivity [156 (6) mg/dl;different from the target of 110 mg/dl;p < .001] and remained below target following a 30% increase in insulin sensitivity [84 (2) mg/dl;p < .001]. In both cases, PID control returned glucose levels to target. Conclusions: PD_BASAL provides better postprandial glucose control than PID but is not appropriate for subjects whose basal requirements change with insulin sensitivity. Simulations used to compare different control strategies should assess this variability. © Diabetes technology Society.

关键词： Blood glucose Closed-loop control Insulin delivery Insulin feedback Proportional gain Safe operating region Type 1 diabetes mellitus simulator

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Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

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Human genomics 2016年第1期10 Suppl 1卷 12页

作者： A. K. Srivastava Y. Wang R. Huang C. Skinner T. Thompson L. Pollard T. Wood F. Luo R. Stevenson R. Polimanti J. Gelernter X. Lin I. Y. Lim Y. Wu A. L. Teh L. Chen I. M. Aris S. E. Soh M. T. Tint J. L. MacIsaac F. Yap K. Kwek S. M. Saw M. S. Kobor M. J. Meaney K. M. Godfrey Y. S. Chong J. D. Holbrook Y. S. Lee P. D. Gluckman N. Karnani A. Kapoor D. Lee A. Chakravarti C. Maercker F. Graf M. Boutros G. Stamoulis F. Santoni P. Makrythanasis A. Letourneau M. Guipponi N. Panousis M. Garieri P. Ribaux E. Falconnet C. Borel S. E. Antonarakis S. Kumar J. Curran J. Blangero S. Chatterjee J. Akiyama D. Auer C. Berrios L. Pennacchio T. R. Donti G. Cappuccio M. Miller P. Atwal A. Kennedy A. Cardon C. Bacino L. Emrick J. Hertecant F. Baumer B. Porter M. Bainbridge P. Bonnen B. Graham R. Sutton Q. Sun S. Elsea Z. Hu P. Wang Y. Zhu J. Zhao M. Xiong David A. Bennett A. Hidalgo-Miranda S. Romero-Cordoba S. Rodriguez-Cuevas R. Rebollar-Vega E. Tagliabue M. Iorio E. D’Ippolito S. Baroni B. Kaczkowski Y. Tanaka H. Kawaji A. Sandelin R. Andersson M. Itoh T. Lassmann Y. Hayashizaki P. Carninci A. R. R. Forrest C. A. Semple E. A. Rosenthal B. Shirts L. Amendola C. Gallego M. Horike-Pyne A. Burt P. Robertson P. Beyers C. Nefcy D. Veenstra F. Hisama R. Bennett M. Dorschner D. Nickerson J. Smith K. Patterson D. Crosslin R. Nassir N. Zubair T. Harrison U. Peters G. Jarvik F. Menghi K. Inaki X. Woo P. Kumar K. Grzeda A. Malhotra H. Kim D. Ucar P. Shreckengast K. Karuturi J. Keck J. Chuang E. T. Liu B. Ji A. Tyler G. Ananda G. Carter H. Nikbakht M. Montagne M. Zeinieh A. Harutyunyan M. Mcconechy N. Jabado P. Lavigne J. Majewski J. B. Goldstein M. Overman G. Varadhachary R. Shroff R. Wolff M. Javle A. Futreal D. Fogelman L. Bravo W. Fajardo H. Gomez C. Castaneda C. Rolfo J. A. Pinto K. C. Akdemir L. Chin S. Patterson C. Statz S. Mockus S. N. Nikolaev X. I. Bonilla L. Parmentier B. King F. Bezrukov G. Kaya V. Zoete V. Seplyarskiy H. Sharpe T. McKee K. Popadin N. Basset-Seguin R. Ben Chaabene M. Andrianova C. Verdan K. Grosdemange O. Sumara M. E JCSRI Greenwood Genetic Center Greenwood USA School of Computing Clemson University Clemson USA Biochemical Genetics Laboratory Greenwood Genetic Center Greenwood USA Department Psychiatry Yale Sch Med and VA CT Healthcare Center West Haven USA Department Genetics Yale Sch Med and VA CT Healthcare Center West Haven USA Department Neurobiology Yale Sch Med and VA CT Healthcare Center West Haven USA Singapore Institute for Clinical Sciences Singapore Singapore National University of Singapore Singapore Singapore University of British Columbia Vancouver Canada KK Women’s and Children’s Hospital Singapore Singapore University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK University of Auckland Auckland New Zealand McKusick-Nathans Institute of Genetic Medicine Johns Hopkins University School of Medicine Baltimore USA Esslingen University of Applied Sciences Esslingen Germany German Cancer Research Center Heidelberg Germany Department of Genetic Medicine and Development University of Geneva Medical School Geneva Switzerland Geneva University Hospitals-HUG Service of Genetic Medicine Geneva Switzerland GE3 Institute of Genetics and Genomics of Geneva University of Geneva Medical School Geneva Switzerland South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Edinburg USA South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Brownsville USA Institute of Genetic Medicine Johns Hopkins University Baltimore USA Genomics Division Lawrence Berkeley National Laboratory Berkeley USA Molecular and Human Genetics Baylor College of Medicine Houston USA Department of Translational Medical Sciences Federico II University Naples Italy Metabolon Inc Durham USA Section of Pediatric Neurology and Neuroscience Baylor College of Medicine Houston USA Tawam Hospital Abu Dhabi United Arab Emirates Stanford Medical School Stanford USA Sch

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s

关键词： Pulmonary Arterial Hypertension Oral Squamous Cell Carcinoma Deep Neural Network Autosomal Recessive Polycystic Kidney Disease Whole Genome Bisulfite Sequencing

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Large-scale orthology predictions for inferring gene functions across multiple species

Large-scale orthology predictions for inferring gene functio...

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2010 DoD High Performance Computing Modernization Program Users Group Conference, HPCMP UGC 2010

作者： Yu, Chenggang Desai, Valmik Zavaljevski, Nela Reifman, Jaques Biotechnology HPC Software Applications Institute Telemedicine and Advanced Technology Research Center Ft. Detrick MD United States

ISBN: (纸本)9780769543925

An effective approach to infer the functions of genes is to use the concept of gene orthology. Because orthologous genes are likely to share similar functions, the functions of genes in an unstudied species can be inferred through the functions of their orthologs in a studied model species. To infer gene functions for a multitude of species, we developed a high-throughput orthology prediction method, termed PhyloTrace. PhyloTrace is both highly accurate and computationally efficient for large-scale applications, having the ability to infer orthologous genes across thousands of species. This is accomplished through three major steps: 1) all-against-all gene comparisons for every pair of genes, 2) pair-wise orthology predictions for every two genomes, and 3) the generation of orthologous clusters that contain orthologous genes across multiple genomes. We employed the previously developed Pipe man parallelization program to break down a set of millions of input sequences into small chunks and then processed them in parallel. We successfully predicted orthologs for over 900 bacterial genomes, achieving a false-positive prediction rate of 2.0, which was a significant improvement compared with the widely used bidirectional best-hit method, which yielded a false-positive rate of 5.5. © 2011 IEEE.

关键词： Genes

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Nonequilibrium Phase Transitions Associated with DNA Replication

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Physical Review Letters 2011年第6期106卷 060601-060601页

作者： Hyung-June Woo Anders Wallqvist Biotechnology High Performance Computing Software Applications Institute Telemedicine and Advanced Technology Research Center U.S. Army Medical Research and Materiel Command Fort Dietrick Maryland 21702 USA

Thermodynamics governing the synthesis of DNA and RNA strands under a template is considered analytically and applied to the population dynamics of competing replicators. We find a nonequilibrium phase transition for high values of polymerase fidelity in a single replicator, where the two phases correspond to stationary states with higher elongation velocity and lower error rate than the other. At the critical point, the susceptibility linking velocity to thermodynamic force diverges. The overall behavior closely resembles the liquid-vapor phase transition in equilibrium. For a population of self-replicating macromolecules, Eigen’s error catastrophe transition precedes this thermodynamic phase transition during starvation. For a given thermodynamic force, the fitness of replicators increases with increasing polymerase fidelity above a threshold.

关键词： Nucleic acids

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Individualized Performance Prediction During Total Sleep Deprivation: Accounting for Trait Vulnerability to Sleep Loss

Individualized Performance Prediction During Total Sleep Dep...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Sridhar Ramakrishnan Srinivas Laxminarayan David Thorsley Nancy J. Wesensten Thomas J. Balkin Jaques Reifman the DoD Biotechnology High Performance Computing Software Applications Institute (BHSAD Telemedicine and Advanced Medical Technology Research Center (TATRC) USAMRMC Frederick MD 21702 USA the BHSAI TATRC USAMRMC Frederick MD 21702 USA. the Center for Military Psychiatry and Neuroscience Dept. of Behavioral Biology Walter Reed Army Institute of Research (WRAIR) Silver Spring MD 20910 USA. the Center for Military Psychiatry and Neuroscience Dept. of Behavioral Biology WRAIR Silver Spring MD 20910 USA. is a Senior Research Scientist and Director of the BHSAI TATRC USAMRMC ATTN: MCMR-TT 504 Scott Street Fort Detrick MD 21702 USA

ISBN: (纸本)9781424441198

Individual differences in vulnerability to sleep loss can be considerable, and thus, recent efforts have focused on developing individualized models for predicting the effects of sleep loss on performance. Individualized models constructed using a Bayesian formulation, which combines an individual's available performance data with a priori performance predictions from a group-average model, typically need at least 40 h of individual data before showing significant improvement over the group-average model predictions. Here, we improve upon the basic Bayesian formulation for developing individualized models by observing that individuals may be classified into three sleep-loss phenotypes: resilient, average, and vulnerable. For each phenotype, we developed a phenotypespecific group-average model and used these models to identify each individual's phenotype. We then used the phenotypespecific models within the Bayesian formulation to make individualized predictions. Results on psychomotor vigilance test data from 48 individuals indicated that, on average, -85% of individual phenotypes were accurately identified within 30 h of wakefulness. The percentage improvement of the proposed approach in 10-h-ahead predictions was 16% for resilient subjects and 6% for vulnerable subjects. The trade-off for these improvements was a slight decrease in prediction accuracy for average subjects.

关键词： Individualized Performance Accounting for Trait Vulnerability The trade-off for these improvements

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Individual vs. combinatorial effect of elevated CO₂conditions and salinity stress on Arabidopsis thaliana liquid cultures: Comparing the early molecular response using time-series transcriptomic and metabolomic analyses

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BMC Systems Biology 2010年第1期4卷 1-15页

作者： Kanani, Harin Dutta, Bhaskar Klapa, Maria I. Metabolic Engineering and Systems Biology Laboratory Department of Chemical and Biomolecular Engineering University of Maryland College Park MD 20742 United States Metabolic Engineering and Systems Biology Laboratory Institute of Chemical Engineering and High Temperature Chemical Processes (ICE-HT) Foundation for Research and Technology-Hellas (FORTH) GR-265 04 Patras Greece Neogen Chemicals Limited Thane MH 400601 India Biotechnology HPC Software Applications Institute Telemedicine and Advanced Technology Research Center U.S. Army Medical Research and Materiel Command Ft. Detrick MD United States

Background: In this study, we investigated the individual and combinatorial effect of elevated CO2 conditions and salinity stress on the dynamics of both the transcriptional and metabolic physiology of Arabidopsis tha...

Background: In this study, we investigated the individual and combinatorial effect of elevated CO₂conditions and salinity stress on the dynamics of both the transcriptional and metabolic physiology of Arabidopsis thaliana liquid hydroponic cultures over the first 30 hours of continuous treatment. Both perturbations are of particular interest in plant and agro-biotechnological applications. Moreover, within the timeframe of this experiment, they are expected to affect plant growth to opposite directions. Thus, a major objective was to investigate whether this expected "divergence" was valid for the individual perturbations and to study how it is manifested under the combined stress at two molecular levels of cellular function, using high-throughput ***: We observed that a) high salinity has stronger effect than elevated CO₂at both the transcriptional and metabolic levels, b) the transcriptional responses to the salinity and combined stresses exhibit strong similarity, implying a robust transcriptional machinery acting to the salinity stress independent of the co-occurrence of elevated CO₂, c) the combinatorial effect of the two perturbations on the metabolic physiology is milder than of the salinity stress alone. Metabolomic analysis suggested that the beneficial role of elevated CO₂on salt-stressed plants within the timeframe of this study should be attributed to the provided additional resources;these allow the plants to respond to high salinity without having to forfeit other major metabolic functions, and d) 9 h-12 h and 24 h of treatment coincide with significant changes in the metabolic physiology under any of the investigated stresses. Significant differences between the acute and longer term responses were observed at both molecular ***: This study contributes large-scale dynamic omic data from two levels of cellular function for a plant system under various stresses. It provides an ad

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Unraveling the conundrum of seemingly discordant protein-protein interaction datasets

Unraveling the conundrum of seemingly discordant protein-pro...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Shobhit Gupta Anders Wallqvist Rajkumar Bondugula Joseph Ivanic Jaques Reifman Johnson School Cornell University Ithaca NY USA Department of Defense Biotechnology High Performance Computing Software Applications Institute Telemedicine and Advanced Technology Research Center U.S. Army Medical Research and Materiel Command Fort Detrick MD USA Advanced Biomedical Computing Center SAIC Frederick MD USA

Most high-throughput experimental results of protein-protein interactions (PPIs) are seemingly inconsistent with each other. In this article, we re-evaluated these contradictions within the context of the underlying domain-domain interactions (DDIs) for two Escherichia coli and four Saccharomyces cerevisiae PPI datasets derived from high-throughput (yeast two-hybrid and tandem affinity purification) experimental platforms. For shared DDIs across pairs of compared datasets, we observed a remarkably high pair-wise correlation (Pearson correlation coefficient between 0.80 and 0.84) between datasets of the same organism derived from the same experimental platform. To a lesser degree, this concordance also held true for more general inter-platform and intra-species comparisons (Pearson correlation coefficient between 0.52 and 0.89). Thus, although varying experimental conditions can influence the ability of individual proteins to interact and, therefore, create apparent differences among PPIs, the physical nature of the underlying interactions, captured by DDIs, is the same and can be used to model and predict PPIs.

关键词： Proteins Correlation Indium tin oxide Databases Organisms Purification Bioinformatics

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Parallel implementation of a bioinformatics pipeline for the design of pathogen diagnostic assays

Parallel implementation of a bioinformatics pipeline for the...

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2009 DoD High Performance Computing Modernization Program - Users Group Conference, HPCMP-UGC 2009

作者： Satya, Ravi Vijaya Kumar, Kamal Zavaljevski, Nela Reifman, Jaques Biotechnology HPC Software Applications Institute Telemedicine and Advanced Technology Research Center Ft. Detrick MD United States

ISBN: (纸本)9780769539461

The genomes of hundreds of pathogens and their near neighbors are now available and many more are being sequenced. With the availability of this genome information, sequence-based pathogen identification has become an increasingly important tool for clinical diagnostics and environmental monitoring of biological threat agents. Chief among sequence-based identification tools are DNA microarrays, which have the ability to test for thousands of pathogens in a single diagnostic test. The design of microarray diagnostic assays involves the identification of short DNA sequences unique to a pathogen or groups of pathogens, where these unique sequences, or "fingerprints" (also referred to as probes) are used to identify the pathogens. To design pathogen fingerprints, we developed TOFI (Tool for Oligonucleotide Fingerprint Identification), a high performance computing software pipeline that designs microarray probes for multiple related pathogens in a single run. The TOFI pipeline is extremely efficient in designing microarray fingerprints for multiple pathogens. Parallel implementation of computationally expensive specificity analysis of the designed fingerprints drastically reduces the overall execution time of the software. Comprehensive performance analysis shows that TOFI achieves super-linear speedup for up to 74 processors. A Web-based user interface, developed using the User Interface Toolkit, provides easy access to the pipeline. Using 74 processors, TOFI took approximately nine hours to design 5,015 in-silico probes for eight Burkholderia genomes with a combined size of more than 50 million base pairs. Experimental validation of these probes with various Burkholderia genomes showed that nearly 80% of the designed fingerprints identify the intended targets. © 2010 IEEE.

关键词： Pathogens

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A web-accessible protein structure prediction pipeline

A web-accessible protein structure prediction pipeline

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2009 DoD High Performance Computing Modernization Program - Users Group Conference, HPCMP-UGC 2009

作者： Lee, Michael S. Bondugula, Rajkumar Desai, Valmik Zavaljevski, Nela Yeh, In-Chul Wallqvist, Anders Reifman, Jaques Biotechnology HPC Software Applications Institute Telemedicine and Advanced Technology Research Center US Army Medical Research and Materiel Command Ft. Detrick MD United States US Army Research Laboratory Computational and Information Sciences Directorate Aberdeen Proving Ground MD United States Biotechnology HPC Software Applications Institute Telemedicine and Advanced Technology Research Center Ft. Detrick MD United States

ISBN: (纸本)9780769539461

Proteins are the molecular basis of nearly all structural, catalytic, sensory, and regulatory functions in living organisms. The biological function of a protein is inextricably linked to its three-dimensional (3D) atomic structure. Traditional structure determination methods, such as X-ray and nuclear magnetic resonance techniques, are time-consuming, expensive, and infeasible for the millions of proteins that have been sequenced so far from various organisms. Alternatively, computational structure prediction methods provide a faster and more cost-effective, albeit approximate, alternative to experimental structure determination. We present a high-throughput protein structure prediction pipeline (dubbed "PSPP"), which given input protein sequences infers their 3D atomic structures. The pipeline was designed to be used with high performance computing clusters and to scale with the number of processors. The pipeline encompasses a core Perl module, a parallel job manager, and a Web browser graphical user interface accessible at our Website (***). The software is currently installed at the Department of Defense (DoD) Maui High Performance Computing Center, and it is available for download along with its associated databases from our site. Currently, DoD scientists are using the pipeline in basic science and drug and vaccine development projects. © 2010 IEEE.

关键词： Pipelines

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Parallel Implementation of a Bioinformatics Pipeline for the Design of Pathogen Diagnostic Assays

Parallel Implementation of a Bioinformatics Pipeline for the...

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DoD High Performance Computing Modernization Program Users Group

作者： Ravi Vijaya Satya Kamal Kumar Nela Zavaljevski Jaques Reifman US Army Medical Research and Materiel Command (MRMC) Biotechnology HPC Software Applications Institute Telemedicine and Advanced Technology Research Center Fort Detrick MD USA

The genomes of hundreds of pathogens and their near neighbors are now available and many more are being sequenced. With the availability of this genome information, sequence-based pathogen identification has become an increasingly important tool for clinical diagnostics and environmental monitoring of biological threat agents. Chief among sequence-based identification tools are DNA microarrays, which have the ability to test for thousands of pathogens in a single diagnostic test. The design of microarray diagnostic assays involves the identification of short DNA sequences unique to a pathogen or groups of pathogens, where these unique sequences, or “fingerprints” (also referred to as probes) are used to identify the pathogens. To design pathogen fingerprints, we developed TOFI (Tool for Oligonucleotide Fingerprint Identification), a high performance computing software pipeline that designs microarray probes for multiple related pathogens in a single run. The TOFI pipeline is extremely efficient in designing microarray fingerprints for multiple pathogens. Parallel implementation of computationally expensive specificity analysis of the designed fingerprints drastically reduces the overall execution time of the software. Comprehensive performance analysis shows that TOFI achieves super-linear speedup for up to 74 processors. A Web-based user interface, developed using the User Interface Toolkit, provides easy access to the pipeline. Using 74 processors, TOFI took approximately nine hours to design 5,015 in-silico probes for eight Burkholderia genomes with a combined size of more than 50 million base pairs. Experimental validation of these probes with various Burkholderia genomes showed that nearly 80% of the designed fingerprints identify the intended targets.

关键词： Genomics Fingerprint recognition Pipelines Probes Pathogens Program processors Databases

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