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Coarse-grained modeling and simulation of actin filament behavior based on Brownian dynamics method

MCB Molecular and Cellular Biomechanics 2009年第3期6卷 161-173页

作者： Shimada, Yoshitaka Adachi, Taiji Inoue, Yasuhiro Hojo, Masaki Department of Mechanical Engineering and Science Kyoto University Yoshida-Honmachi Sakyo Kyoto 606-8501 Japan Computational Cell Biomechanics Team VCAD System Research Program RIKEN Japan

The actin filament, which is the most abundant component of the cytoskeleton, plays important roles in fundamental cellular activities such as shape determination, cell motility, and mechanosensing. In each activity, the actin filament dynamically changes its structure by polymerization, depolymerization, and severing. These phenomena occur on the scales ranging from the dynamics of actin molecules to filament structural changes with its deformation due to the various forces, for example, by the membrane and solvent. To better understand the actin filament dynamics, it is important to focus on these scales and develop its mathematical model. Thus, the objectives of this study were to model and simulate actin filament polymerization, depolymerization, and severing based on the Brownian dynamics method. In the model, the actin monomers and the solvent were considered as globular particles and a continuum, respectively. The motion of the actin molecules was assumed to follow the Langevin equation. The polymerization, which increases the filament length, was determined by the distance between the center of the actin particle at the barbed end and actin particles in the solvent. The depolymerization, which decreases the filament length, was modeled such that the number of dissociation particles from the filament end per unit time was constant. In addition, the filament severing, in which one filament divides into two, was modeled to occur at an equal rate along the filament. Then, we simulated the actin filament dynamics using the developed model, and analyzed the filament elongation rate, its turnover, and the effects of filament severing on the polymerization and depolymerization. Results indicated that the model reproduced the linear dependence of the filament elongation on time, filament turnover process by polymerization and depolymerization, and acceleration of the polymerization and depolymerization by severing, which qualitatively agreed with those observed in ex

关键词： Coarse-grained modeling

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Topic activation analysis for document streams based on document arrival rate and relevance 05

Topic activation analysis for document streams based on docu...

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20th Annual ACM Symposium on Applied Computing

作者： Cui, Chunhua Kitagawa, Hiroyuki Master's Program in Science and Engineering University of Tsukuba Graduate School of Systems and Information Engineering Center for Computational Sciences University of Tsukuba

ISBN: (纸本)9781581139648

With the advance of network technology in recent years, the dissemination and exchange of massive documents has become commonplace. Accordingly, the importance of content analysis techniques is increasing. Topic analysis in large-scale document streams such as E-mails and news articles is an important research issue. This paper addresses techniques for "topic activation analysis" for document streams. For example, when news articles with a strong relationship to a given topic arrive frequently in a news stream, we can regard the activation level of the topic as high. In [I], Kleinberg proposed a method for analyzing document streams. Although the main objective of his method was to detect bursts of topics, it can also be used for topic activation analysis. His method, however, has a serious limitation in that it only looks at the arrival rate of documents and ignores the degree of relevance for each document. Another limitation is that his method is "batch-oriented." This paper first proposes a novel topic activation analysis scheme that incorporates both document arrival rate and relevance to address the first problem. It then presents an incremental scheme more appropriate for a document streaming environment. The proposed schemes are validated by experiments using real CNN news articles. Copyright 2005 ACM.

关键词： Computer networks

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The crystallite size-disorder relationship based on the spiral paracrystal

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Acta Crystallographica Section A: Foundations of Crystallography 2000年第6期56卷 549-553页

作者： Eads, J.L. Millane, R.P. Whistler Center for Carbohydrate Research Computational Science and Engineering Program Purdue University West Lafayette IN 47907-1160 United States

A simple physical model of the relationship between crystallite size and disorder for paracrystalline materials is presented, based on the spiral paracrystal and distortion of the lattice cells. Simulations show that the model leads to relationships similar to the α* rule. Average crystallite sizes predicted by the model are in agreement with experimental data and also allow crystallite-size distributions to be proposed. The model provides a more satisfactory and complete explanation of this relationship than do current descriptions.

关键词：碳水化合物构象碳水化合物/化学结晶结晶学 X线/方法模型分子

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High-resolution dynamic imaging of chromatin DNA communication using Oligo-LiveFISH

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Cell 2025年

作者： Zhu, Yanyu Balaji, Ashwin Han, Mengting Andronov, Leonid Roy, Anish R. Wei, Zheng Chen, Crystal Miles, Leanne Cai, Sa Gu, Zhengxi Tse, Ariana Yu, Betty Chentzu Uenaka, Takeshi Lin, Xueqiu Spakowitz, Andrew J. Moerner, W.E. Qi, Lei S. Department of Bioengineering Stanford University Stanford 94305 CA United States Department of Chemistry Stanford University Stanford 94305 CA United States Biophysics PhD Program Stanford University Stanford 94305 CA United States Computational Biology Program Public Health Sciences Division and Translational Data Science IRC Fred Hutchinson Cancer Center Seattle 98109 WA United States Department of Chemical Engineering Stanford University Stanford 94305 CA United States Department of Materials Science and Engineering Stanford University Stanford 94305 CA United States Institute for Stem Cell Biology & Regenerative Medicine and Department of Pathology Stanford University School of Medicine Stanford 94305 CA United States Sarafan ChEM-H Stanford University Stanford 94305 CA United States Chan Zuckerberg Biohub San Francisco 94080 CA United States

Three-dimensional (3D) genome dynamics are crucial for cellular functions and disease. However, real-time, live-cell DNA visualization remains challenging, as existing methods are often confined to repetitive regions, suffer from low resolution, or require complex genome engineering. Here, we present Oligo-LiveFISH, a high-resolution, reagent-based platform for dynamically tracking non-repetitive genomic loci in diverse cell types, including primary cells. Oligo-LiveFISH utilizes fluorescent guide RNA (gRNA) oligo pools generated by computational design, in vitro transcription, and chemical labeling, delivered as ribonucleoproteins. Utilizing machine learning, we characterized the impact of gRNA design and chromatin features on imaging efficiency. Multi-color Oligo-LiveFISH achieved 20-nm spatial resolution and 50-ms temporal resolution in 3D, capturing real-time enhancer and promoter dynamics. Our measurements and dynamic modeling revealed two distinct modes of chromatin communication, and active transcription slows enhancer-promoter dynamics at endogenous genes like FOS. Oligo-LiveFISH offers a versatile platform for studying 3D genome dynamics and their links to cellular processes and disease. © 2025 Elsevier Inc.

关键词： CRISPR imaging DNA communication dynamic tracking enhancer-promoter interaction high-resolution imaging live DNA imaging non-repetitive genome imaging Oligo-LiveFISH polymer modeling primary cell

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Automated Deep Learning Empowered Breast Cancer Diagnosis UsingBiomedical Mammogram Images

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Computers, Materials & Continua 2022年第6期71卷 4221-4235页

作者： JoséEscorcia-Gutierrez Romany F.Mansour Kelvin Belen Javier Jiménez-Cabas Meglys Pérez Natasha Madera Kevin Velasquez Electronics and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla08001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt Mechatronics Engineering Program Universidad Autónoma del CaribeBarranquilla08001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla08001Colombia

Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic *** the same time,breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging *** mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death *** the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false *** resolving the issues of false positives of breast cancer diagnosis,this paper presents an automated deep learning based breast cancer diagnosis(ADL-BCD)model using digital *** goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital *** proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image *** addition,Deep Convolutional Neural Network based Residual Network(ResNet 34)is applied for feature extraction ***,a hyper parameter tuning process using chimp optimization algorithm(COA)is applied to tune the parameters involved in ResNet 34 *** wavelet neural network(WNN)is used for the classification of digital mammograms for the detection of breast *** ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance *** simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.

关键词： Breast cancer digital mammograms deep learning wavelet neural network Resnet 34 disease diagnosis

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High-fidelity numerical simulation of a chevron nozzle jet flow

High-fidelity numerical simulation of a chevron nozzle jet f...

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作者： Uzun, Ali Hussaini, M. Yousuff Program in Computational Science and Engineering Florida State University Tallahassee FL 32306-4510 United States

ISBN: (纸本)9781563479748

This paper reports results from our ongoing research on the noise of jets exhausting from chevron nozzles. Results from the simulation of a moderate Reynolds number cold jet flow exhausting from a chevron nozzle designated as SMC006 are presented. This nozzle geometry was experimentally studied by the researchers at the NASA Glenn Research Center. Results from the simulation are compared with the experimental measurements. The SMC006 nozzle contains six symmetric chevrons that have an approximately 18-degree penetration angle. In this study, the flow inside the nozzle geometry and the free jet flow outside are computed simultaneously by a high-order accurate, multi-block, large eddy simulation (LES) code with overset grid capability. For the SMC006 chevron nozzle test case, the governing equations are solved on approximately 400 million grid points using 2048 processors in parallel. The main emphasis of the simulation is to compute the jet flow in as much detail as possible and accurately capture the physical processes that lead to noise generation. It is especially critical to capture the enhanced shear layer mixing due to chevrons that takes place within the first few diameters downstream of the nozzle exit. The goal of the present simulation is to resolve the full noise frequency range of interest. Our calculations resolve the jet flow field in an unprecedented level of detail by utilizing extreme computational resources and and thus they are the first of their kind. Despite some issues such as an order of magnitude lower simulation Reynolds number (relative to experimental value) and unknown initial turbulence intensity levels within the experimental nozzle, it is shown that both the near-field jet turbulence and far-field noise predictions are in good agreement with the experimental measurements. Copyright © 2009 by the authors.

关键词： Reynolds number

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Stylistic analysis of paintings using wavelets and machine learning

Stylistic analysis of paintings using wavelets and machine l...

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17th European Signal Processing Conference, EUSIPCO 2009

作者： Jafarpour, Sina Polatkan, Güngör Brevdo, Eugene Hughes, Shannon Brasoveanu, Andrei Daubechies, Ingrid Princeton University Departments of Electrical Engineering Computer Science and Mathematics Program in Applied and Computational Mathematics Princeton NJ 08544 United States

Wavelet transforms and machine learning tools can be used to assist art experts in the stylistic analysis of paintings. A dual-tree complex wavelet transform, Hidden Markov Tree modeling and Random Forest classifiers are used here for a stylistic analysis of Vincent van Gogh's paintings with results on two stylometry challenges that concern "dating, resp. extracting distinguishing features". © EURASIP, 2009.

关键词： Decision trees

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Fluorescent imaging for in situ measurement of drug target engagement and cell signaling pathways

Fluorescent imaging for in situ measurement of drug target e...

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Microscopy Histopathology and Analytics, Microscopy 2020

作者： McMahon, Nathan Solanki, Allison Jones, Jocelyn Kwon, Sunjong Chang, Young-Hwan Chin, Koei Nederlof, Michel Gray, Joe Gibbs, Summer L. Department of Biomedical Engineering Computational Biology Program Oregon Health and Science University PortlandOR97201 United States Knight Cancer Institute Computational Biology Program Oregon Health and Science University PortlandOR97201 United States Center for Spatial Systems Biomedicine Computational Biology Program Oregon Health and Science University PortlandOR97201 United States Quantitative Imaging Systems PittsburghPA15238 United States

Successful targeted cancer treatment continues to elude modern medicine and its many therapeutic strategies. Herein, we propose a fluorescent imaging technique to measure drug localization and subsequent cell signalin... 详细信息

ISBN: (纸本)9781943580743

关键词： Cell signaling

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Threshold and decay properties of transient isolated turbulent band in plane Couette flow

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Applied Mathematics and Mechanics(English Edition) 2019年第10期40卷 1449-1456页

作者： Jianzhou LU Jianjun TAO Weitao ZHOU Xiangming XIONG CAPT-HEDPS SKLTCS Collaborative Innovation Center of IFSA Department of Mechanics and Engineering Science College of Engineering Peking University Beijing 100871 China The Program in Applied and Computational Mathematics Princeton University Princeton NJ 08544 U. S.A.

By direct numerical simulations of the plane Couette flow (PCF) in a large computational domain, it is shown that an isolated turbulent band decays monotonically at low Reynolds numbers but experiences transient growth before the eventual relaminarization at moderate Reynolds numbers. The lower bound Reynolds number of the transient-growth regime is determined as 286. The width, length, and tilt angle of the iso- lated band structure are defined based on the disturbance kinetic energy in the mid-plane, and the geometric characteristics of the band can be described with a tilted rectangle. It is illustrated that before its eventual fragmentation, the isolated turbulent band decays in a style of longitudinal contraction, where the center, width, and tilt angle of the band keep almost constant but the band length contracts with a statistically constant velocity.

关键词： plane Couette flow (PCF) subcritical transition turbulent band

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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