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SSRN 2023年

作者： Lin, Xiaobo Tee, Shern R. Searles, Debra J. Cummings, Peter T. Multiscale Modeling and Simulation Center Vanderbilt University NashvilleTN United States Department of Chemical and Biomolecular Engineering Vanderbilt University NashvilleTN United States Australian Institute for Bioengineering and Nanotechnology The University of Queensland BrisbaneQLD Australia School of Chemistry and Molecular Biosciences The University of Queensland BrisbaneQLD Australia School of Engineering and Physical Sciences Heriot-Watt University EdinburghSCT United Kingdom

Molecular dynamics (MD) simulations using the constant potential method (CPM) can provide nanoscale insight to explain and optimize supercapacitor charging dynamics and charge storage. We report CPM MD operando simulations for charging of nanoporous carbide-derived carbon supercapacitors with four distinct electrolytes, including ionic liquid (IL), mixed IL-solvent, and solvent-in-salt electrolytes. Instead of employing a coarse-grained model, we used an all-atom model for the electrolytes, allowing us to uncover the essential effects of solvents on the charging mechanism. We find that the water-in-salt electrolyte, lithium bis(trifluoromethanesulfonyl)imide / water, leads to the greatest charge storage among the studied combinations and exhibits a significantly higher integral and differential capacitance on the negative electrode, associated with a strong cation-driven charging mechanism. Our simulations also demonstrate the varying contributions of the different electrode regions to supercapacitor performance, with an especially high local capacitance (up to [[EQUATION]]250 F/g) within the interfacial region of the electrodes. These molecular insights provide important guidance for optimizing supercapacitor performance by carefully tuning electrode nanostructure and electrolyte composition. © 2023, The Authors. All rights reserved.

关键词： Supercapacitor

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Optical dispersions through intracellular inhomogeneities

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Physical Review Research 2023年第2期5卷 L022043-L022043页

作者： Masaki Watabe Yasuhiro Hirano Atsuko Iwane Osamu Matoba Koichi Takahashi Laboratory for Biologically Inspired Computing RIKEN Center for Biosystems Dynamics Research Suita Osaka 565-0874 Japan Cell Modeling and Simulation Group Exploratory Research Center on Life and Living Systems Okazaki Aichi 444-8787 Japan Interdisciplinary Research Unit National Institute of Basic Biology Okazaki Aichi 444-8585 Japan Nuclear Dynamics Group Graduate School of Frontier Biosciences Osaka University Suita Osaka 565-0871 Japan Laboratory for Cell Field Structure RIKEN Center for Biosystems Dynamics Research Higashi-Hiroshima Hiroshima 739-0046 Japan The Graduate School of System Informatics Kobe University Kobe Hyogo 657-8501 Japan Institute for Advanced Bioscience Keio University Fujisawa Kanagawa 252-8520 Japan

The transport of intensity equation (TIE) exhibits a noninterferometric correlation between the intensity and phase variations of intermediate fields (e.g., light and electrons) in biological imaging. Previous TIE formulations have generally assumed free-space propagation of monochromatic, coherent field functions crossing phase distributions along a longitudinal direction. In this study, we modify the TIE with fractal (or self-similar) organization models based on intracellular refractive index turbulence. We then implement TIE simulations over a broad range of fractal dimensions and wavelengths. simulation results show how the intensity propagation through the spatial fluctuation of intracellular refractive index interconnects fractal dimensionality with intensity dispersion (or transmissivity) within the picometer to micrometer wavelength range. Additionally, we provide a spatial autocorrelation of phase derivatives, which allows for the direct measurement and reconstruction of intracellular fractal profiles from optical and electron microscopy imaging.

关键词： Biological optics Classical optics Organelle Electron microscopy Finite-element method Optical microscopy

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Identification of Key Genes and Pathways Involved in Compensatory Pancreatic Beta Cell Hyperplasia During Insulin Resistance

Identification of Key Genes and Pathways Involved in Compens...

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International Symposium on Computational Science and Computing, ISCSC 2018

作者： Siddiqa, Amnah Ahmad, Jamil Paracha, Rehan Zafar Bibi, Zurah Ali, Amjad Research Center for Modeling and Simulation National University of Sciences and Technology H-12 Islamabad Pakistan Atta-ur-Rehman School for Applied Biosciences National University of Sciences and Technology H-12 Islamabad Pakistan

ISBN: (纸本)9783030021153

Restoration of pancreatic beta cell mass is anticipated as beneficial treatment for both types of Diabetes Mellitus (DM) (I & II). Previously, compensatory increase of pancreatic beta cell mass has been observed in various insulin resistance conditions. Investigation of regulatory mechanism behind the pancreatic beta cell hyperplasia through the liver derived growth factors have profound implications in treatment of DM. Therefore, this study is conducted with a specific aim to identify the key regulatory elements and pathways involved in the compensatory increase of pancreatic beta cells during insulin resistance in liver. For this purpose, three transcriptomics datasets of different insulin resistant mouse models (S961 induced, high fat diet induced and db/db) are analyzed in this study. Common differentially expressed genes (DEGs) and significantly altered pathways in these datasets are obtained. These genes and pathways represent the key elements of the gene regulatory circuit involved in compensatory pancreatic beta cell hyperplasia during insulin resistance. These findings have increased our current knowledge regarding the genes and gene circuits involved collectively in different insulin resistance conditions. © 2019, Springer Nature Switzerland AG.

关键词： Genes

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HCV kinetic and modeling analyses indicate similar time to cure among sofosbuvir combination regimens with daclatasvir, simeprevir or ledipasvir

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Journal of Virus Eradication 2016年 2卷 15-15页

作者： Harel Dahari Laetitia Canini Frederik Graw Susan L. Uprichard EvaldoS A. Araujo Guillaume Penaranda Emilie Coquet Aurélie Riso Christophe Renou Marc Bourlière Scott J. Cotler Philippe Halfon Loyola University Medical Center Division of Hepatology The Program for Experimental & Theoretical Modeling Maywood Illinois USA Theoretical Biology & Biophysics Group Los Alamos National Laboratory Los Alamos New Mexico USA University of Edinburgh Center for Immunity Infection and Evolution Edinburgh UK Heidelberg University Center for Modeling and Simulation in the Biosciences BioQuant Center Heidelberg Germany University of Sao Paulo Hospital das Clinicas Sao Paulo Brazil Alphabio Laboratory Marseille France Saint Joseph Hospital Division of Hepatology Marseille France Hyeres Hospital Division of Hepatology Hyeres France European Hospital Internal Medicine and Infectious Disease Marseille France

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Qualitative modeling and analysis of the indoleamine 2,3-dioxygenase regulatory network in homeostasis and disease

Qualitative modeling and analysis of the indoleamine 2,3-dio...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Kashif Asghar Jamil Ahmed Javaria Ashraf Amjad Ali Hussnain Ahmed Janjua Sheeba Murad Atta-ur-Rehman School of Applied Biosciences National University of Science & Technology (NUST) Islamabad Pakistan Research Center for Modeling & Simulation National University of Science & Technology(NUST) Islamabad Pakistan

ISBN: (纸本)9781479951499

Indoleamine 2,3-dioxygenase (IDO) has key role in immune regulation and it acts as a constituent of the inflammatory reaction allied with infection, allergic diseases, autoimmunity and cancer. IDO function as an immunosuppressive enzyme, which degrades the tryptophan into kynurenine and other downstream metabolites. Interferon gamma (IFN-γ) is a potent inducer of IDO. Transforming growth factor-beta (TGF-β) is the negative regulator of IDO. Both the TGF-β and IFN-γ have opposite effects on various cellular functions. IFN-γ and TGF-β inhibit the activity of each other through different pathways. In the pathological state when the homeostasis disturb, the impairment of IDO provides significant role in the pathogenesis of disease. The modeling of the Gene regulatory network (GRN) of IDO, IFN-γ and TGF-β using of René Thomas Formalism, help us in understanding their dynamical behaviors. We observe two types of steady states behaviors in this study. The cycle shows the homeostasis of IDO and TGF-β while IFN-γ is inactive (silent). The stable state shows the high expression levels of IFN-γ and IDO while TGF-β is silent. These findings recognized prospective novel targets for therapy in a range of clinical conditions, including, inflammation, tumor resistance and autoimmunity.

关键词： Immune system Cancer Tumors Diseases Steady-state Analytical models

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New measurement criteria for studying alcohol drinking and relapse in rodents

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In silico pharmacology 2013年第1期1卷 13页

作者： Lilian Villarín Pildaín Valentina Vengeliene Franziska Matthäus Max Planck Institute for Molecular Genetics Berlin Germany. Central Institute for Mental Health Institute of Psychopharmacology University of Heidelberg Faculty of Medicine Mannheim Germany Department of Neurobiology and Biophysics Faculty of Natural Sciences Vilnius University Vilnius Lithuania. Center for Modeling and Simulation in the Biosciences (BIOMS) University of Heidelberg Heidelberg Germany.

PURPOSE:Relapse to alcohol use is considered as one of the central features distinguishing dependence from controlled alcohol consumption. Relapse-like drinking in rodents is a transient episode of heavy drinking that follows a period of abstinence. This behaviour is called the alcohol deprivation effect (ADE). Not all animals develop behavioural changes that resemble relapse-like drinking behaviour. The purpose of our study was to develop a generalized quantitative criterion by which animals could be separated into two groups depending on their behaviour during a relapse-like situation (ADE vs. no-ADE). METHODS:An automated drinkometer system was used for data collection. This system measures fluid consumption by means of high-precision sensors attached to the drinking bottles in the home cage of the rat. We used a four bottle free choice paradigm with water 5, 10, and 20% ethanol solutions. For data analysis we developed a new measure of alcohol intake that quantifies net alcohol intake in relation to net consumption of water. This new measure is called water-penalized net ethanol intake. RESULTS:The new measure is more robust than commonly used measurements, such as alcohol preference and intake. It allows the comparison of alcohol intake between different groups of animals and different setups using an arbitrary number of bottles. Based on this new measure we developed a method to automatically select the threshold for the presence of ADE in individual animals. CONCLUSIONS:Separating animals by their behavior during relapse-like situation could be used as one of the criteria for identification of alcohol addicted and non-addicted rats. A classification into presenting ADE or not is also essential to test the effectiveness of newly developed therapeutic drugs.

关键词： Alcohol deprivation effect Animal model of addiction Drinking patterns Four-bottle paradigm Relapse

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Classifying general nonlinear force laws in cell-based models via the continuum limit

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Physical Review E 2012年第2期85卷 021921-021921页

作者： Philip J. Murray Carina M. Edwards Marcus J. Tindall Philip K. Maini Centre for Mathematical Biology Mathematical Institute 24–29 St Giles' Oxford OX1 3LB United Kingdom Center for Modeling and Simulation in the Biosciences Im Neuenheimer Feld 267 University of Heidelberg D-69120 Heidelberg Germany School of Biological Sciences Department of Mathematics and Statistics and Institute for Cardiovascular and Metabolic Research University of Reading Whiteknights Reading Berkshire RG6 6AJ United Kingdom Oxford Centre for Integrative Systems Biology Department of Biochemistry South Parks Road Oxford OX1 3QU United Kingdom

Although discrete cell-based frameworks are now commonly used to simulate a whole range of biological phenomena, it is typically not obvious how the numerous different types of model are related to one another, nor which one is most appropriate in a given context. Here we demonstrate how individual cell movement on the discrete scale modeled using nonlinear force laws can be described by nonlinear diffusion coefficients on the continuum scale. A general relationship between nonlinear force laws and their respective diffusion coefficients is derived in one spatial dimension and, subsequently, a range of particular examples is considered. For each case excellent agreement is observed between numerical solutions of the discrete and corresponding continuum models. Three case studies are considered in which we demonstrate how the derived nonlinear diffusion coefficients can be used to (a) relate different discrete models of cell behavior; (b) derive discrete, intercell force laws from previously posed diffusion coefficients, and (c) describe aggregative behavior in discrete simulations.

关键词： Diffusion

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Structural analysis of functional connectivity related to memory reveals network changes in memory function over the life span

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BMC Neuroscience 2011年第1期12卷 1-1页

作者： Franziska Matthäus Jan-Philip Schmidt Traute Demirakca Carsten Diener Center for Modeling and Simulation in the Biosciences (BIOMS) Heidelberg University Heidelberg Germany Institute for Applied Mathematics Heidelberg University Heidelberg Germany Central Institute of Mental Health Mannheim Germany

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