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arXiv 2017年

作者： Bertalan, Tom Wu, Yan Laing, Carlo William Gear, C. Kevrekidis, Ioannis G. Department of Chemical and Biological Engineering Princeton University PrincetonNJ United States Department of Bioengineering University of California San DiegoCA United States Institute of Natural and Mathematical Sciences Massey University Auckland New Zealand Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States

Finding accurate reduced descriptions for large, complex, dynamically evolving networks is a crucial enabler to their simulation, analysis, and, ultimately, design. Here we propose and illustrate a systematic and powerful approach to obtaining good collective coarse-grained observables-variables successfully summarizing the detailed state of such networks. Finding such variables can naturally lead to successful reduced dynamic models for the networks. The main premise enabling our approach is the assumption that the behavior of a node in the network depends (after a short initial transient) on the node identity: a set of descriptors that quantify the node properties, whether intrinsic (e.g. parameters in the node evolution equations) or structural (imparted to the node by its connectivity in the particular network structure). The approach creates a natural link with modeling and "computational enabling technology" developed in the context of Uncertainty Quantification. In our case, however, we will not focus on ensembles of different realizations of a problem, each with parameters randomly selected from a distribution. We will instead study many coupled heterogeneous units, each characterized by randomly assigned (heterogeneous) parameter value(s). One could then coin the term Heterogeneity Quantification for this approach, which we illustrate through a model dynamic network consisting of coupled oscillators with one intrinsic heterogeneity (oscillator individual frequency) and one structural heterogeneity (oscillator degree in the undirected network). The computational implementation of the approach, its shortcomings and possible extensions are also discussed. Copyright © 2017, The Authors. All rights reserved.

关键词： Data mining

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Education for the future

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Science 2018年第6396期360卷 1409-1412页

作者： Cheung, Man Kit Schwendimann, Beat A. Ward, Adrian Pietrzak, Barbara Mollaoglu, Gurkan Duong, Michael Tran Allareddy, Veerasathpurush Ulltveit-Moe, Nils Dutton-Regester, Ken Zhang, Jian Scult, Matthew A. Singh, Poonam C. Yan, Hong Young Naz, Saima Isaacson, Kyle Dennis, Allison F. Beardsley, Felicia Rounds Al-Humaidan, Eyad Ibrahim Lo, Cody Jones, Tyler Sood, Prashant Nieuwenhuis, Rense Ali, Basant A. Yu, Kun-Hsing Arthur, Patrick Kobina Kumar, Brijesh Buschke, Falko Chen, Alexander Cingl, Lubomír O’Mullane, Anthony Peter Coetzee, Vinet Zaidi, Syed Shan-e-Ali Konstantinides, Nikos School of Life Sciences Chinese University of Hong Kong Shatin New Territories Hong Kong Graduate School of Education University of California Berkeley Berkeley 94720 CA United States University of Queensland St. Lucia 4072 QLD Australia Department of Hydrobiology Faculty of Biology Biological and Chemical Research Centre University of Warsaw Warsaw 02-089 Poland Department of Oncological Sciences University of Utah School of Medicine Salt Lake City 84112 UT United States Perelman School of Medicine University of Pennsylvania Philadelphia 19104 PA United States University of Illinois College of Dentistry Chicago 60612 IL United States Department of Information and Communication Technology University of Agder 4879 Grimstad Norway Department of Genetics and Computational Biology QIMR Berghofer Medical Research Institute Brisbane 4006 QLD Australia School of Safety and Environmental Engineering Hunan Institute of Technology Hengyang Hunan 421002 China Lahore 54000 Punjab Pakistan Department of Psychology and Neuroscience Duke University Durham 27708 NC United States National Museum of Marine Biology and Aquarium Chercheng Pingtung County 94450 Taiwan Division of Plant Microbe Interactions CSIR–National Botanical Research Institute Lucknow 226001 Uttar Pradesh India Program in Cell Molecular Developmental Biology and Biophysics (CMDB) Johns Hopkins University Baltimore 21218 MD United States Stem Cell and Tissue Re-Engineering Program King Faisal Specialist Hospital and Research Centre Riyadh Saudi Arabia Department of Sociology and Anthropology University of La Verne La Verne 91750 CA United States Department of Bioengineering University of Utah Salt Lake City 84112 UT United States MRC Centre for Medical Mycology University of Aberdeen Aberdeen AB25 2ZD United Kingdom Faculty of Medicine University of British Columbia Vancouver V6T 1Z2 BC Canada School of Engineering Brown University Providence 02912 RI United States

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Author Correction: π-HuB: the proteomic navigator of the human body

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Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

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Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing

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Nature biotechnology 2020年第4期38卷 504页

作者： Christian Lieven Moritz E Beber Brett G Olivier Frank T Bergmann Meric Ataman Parizad Babaei Jennifer A Bartell Lars M Blank Siddharth Chauhan Kevin Correia Christian Diener Andreas Dräger Birgitta E Ebert Janaka N Edirisinghe José P Faria Adam M Feist Georgios Fengos Ronan M T Fleming Beatriz García-Jiménez Vassily Hatzimanikatis Wout van Helvoirt Christopher S Henry Henning Hermjakob Markus J Herrgård Ali Kaafarani Hyun Uk Kim Zachary King Steffen Klamt Edda Klipp Jasper J Koehorst Matthias König Meiyappan Lakshmanan Dong-Yup Lee Sang Yup Lee Sunjae Lee Nathan E Lewis Filipe Liu Hongwu Ma Daniel Machado Radhakrishnan Mahadevan Paulo Maia Adil Mardinoglu Gregory L Medlock Jonathan M Monk Jens Nielsen Lars Keld Nielsen Juan Nogales Intawat Nookaew Bernhard O Palsson Jason A Papin Kiran R Patil Mark Poolman Nathan D Price Osbaldo Resendis-Antonio Anne Richelle Isabel Rocha Benjamín J Sánchez Peter J Schaap Rahuman S Malik Sheriff Saeed Shoaie Nikolaus Sonnenschein Bas Teusink Paulo Vilaça Jon Olav Vik Judith A H Wodke Joana C Xavier Qianqian Yuan Maksim Zakhartsev Cheng Zhang Novo Nordisk Foundation Center for Biosustainability Technical University of Denmark Lyngby Denmark. Systems Biology Lab Amsterdam Institute of Molecular and Life Sciences (AIMMS) Vrije Universiteit Amsterdam Amsterdam the Netherlands. BioQUANT/COS Heidelberg University Heidelberg Germany. Ecole Polytechnique Fédérale de Lausanne Laboratory of Computational Systems Biotechnology Lausanne Switzerland. iAMB-Institute of Applied Microbiology ABBt-Aachen Biology and Biotechnology RWTH Aachen University Aachen Germany. Department of Bioengineering University of California La Jolla CA USA. Department of Chemical Engineering and Applied Chemistry University of Toronto Toronto Ontario Canada. Human Systems Biology Laboratory Instituto Nacional de Medicina Genomica & Coordinación de la Investigación Científica-Red de Apoyo a la Investigación UNAM Mexico City Mexico. Institute for Systems Biology Seattle WA USA. Computational Systems Biology of Infection and Antimicrobial-Resistant Pathogens Institute for Biomedical Informatics (IBMI) Tübingen Germany. Department of Computer Science University of Tübingen Tübingen Germany. German Center for Infection Research (DZIF) partner site Tübingen Tübingen Germany. Australian Institute for Bioengineering and Nanotechnology The University of Queensland Brisbane Queensland Australia. Argonne National Laboratory Lemont IL USA. Analytical Biosciences Division of Systems Biomedicine and Pharmacology Leiden Academic Centre for Drug Research Leiden University Leiden the Netherlands. Department of Systems Biology Centro Nacional de Biotecnología Consejo Superior de Investigaciones Científicas (CNB-CSIC) Madrid Spain. Centro de Biotecnología y Genómica de Plantas (CBGP UPM-INIA) Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) Madrid Spain. Department of Animal and Aquacultural Sciences Faculty of Biosciences Norwegian University of Life Sciences Oslo Norway

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Bioprinting: Microfluidics‐Enabled Multimaterial Maskless Stereolithographic Bioprinting (Adv. Mater. 27/2018)

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Advanced Materials 2018年第27期30卷

作者： Amir K. Miri Daniel Nieto Luis Iglesias Hossein Goodarzi Hosseinabadi Sushila Maharjan Guillermo U. Ruiz‐Esparza Parastoo Khoshakhlagh Amir Manbachi Mehmet Remzi Dokmeci Shaochen Chen Su Ryon Shin Yu Shrike Zhang Ali Khademhosseini Division of Engineering in Medicine Department of Medicine Brigham and Women's Hospital Harvard Medical School Cambridge MA 02139 USA Harvard‐MIT Division of Health Sciences and Technology Massachusetts Institute of Technology Cambridge MA 02139 USA Microoptics and GRIN Optics Group Applied Physics Department Faculty of Physics University of Santiago de Compostela Santiago de Compostela 15782 Spain Polymeric Materials Research Group Department of Materials Science and Engineering Sharif University of Technology Tehran *** Iran Department of Biomedical Engineering Whiting School of Engineering Johns Hopkins University Baltimore MD 21218 USA Department of NanoEngineering University of California San Diego La Jolla CA 92093 USA Department of Bioengineering University of California San Diego La Jolla CA 92093 USA Materials Science and Engineering Program University of California San Diego La Jolla CA 92093 USA Center for Minimally Invasive Therapeutics (C‐MIT) University of California‐Los Angeles Los Angeles CA 90095 USA Department of Radiology David Geffen School of Medicine University of California‐Los Angeles Los Angeles CA 90095 USA Department of Bioengineering Department of Chemical and Biomolecular Engineering Henry Samueli School of Engineering and Applied Sciences University of California‐Los Angeles Los Angeles CA 90095 USA California NanoSystems Institute (CNSI) University of California‐Los Angeles Los Angeles CA 90095 USA Department of Bioindustrial Technologies Konkuk University Seoul 143‐701 Republic of Korea

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Publisher Correction: On the issue of transparency and reproducibility in nanomedicine

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Nature nanotechnology 2019年第8期14卷 811页

作者： Hon S Leong Kimberly S Butler C Jeffrey Brinker May Azzawi Steve Conlan Christine Dufès Andrew Owen Steve Rannard Chris Scott Chunying Chen Marina A Dobrovolskaia Serguei V Kozlov Adriele Prina-Mello Ruth Schmid Peter Wick Fanny Caputo Patrick Boisseau Rachael M Crist Scott E McNeil Bengt Fadeel Lang Tran Steffen Foss Hansen Nanna B Hartmann Lauge P W Clausen Lars M Skjolding Anders Baun Marlene Ågerstrand Zhen Gu Dimitrios A Lamprou Clare Hoskins Leaf Huang Wantong Song Huiliang Cao Xuanyong Liu Klaus D Jandt Wen Jiang Betty Y S Kim Korin E Wheeler Andrew J Chetwynd Iseult Lynch Sayed Moein Moghimi André Nel Tian Xia Paul S Weiss Bruno Sarmento José das Neves Hélder A Santos Luis Santos Samir Mitragotri Steve Little Dan Peer Mansoor M Amiji Maria José Alonso Alke Petri-Fink Sandor Balog Aaron Lee Barbara Drasler Barbara Rothen-Rutishauser Stefan Wilhelm Handan Acar Roger G Harrison Chuanbin Mao Priyabrata Mukherjee Rajagopal Ramesh Lacey R McNally Sara Busatto Joy Wolfram Paolo Bergese Mauro Ferrari Ronnie H Fang Liangfang Zhang Jie Zheng Chuanqi Peng Bujie Du Mengxiao Yu Danielle M Charron Gang Zheng Chiara Pastore Department of Urology Mayo Clinic Rochester MN USA. Department of Physiology and Biomedical Engineering Mayo Clinic Rochester MN USA. Department of Nanobiology Sandia National Laboratories Albuquerque NM USA. Center for Micro-Engineered Materials University of New Mexico Albuquerque Albuquerque NM USA. Departments of Chemical and Biological Engineering University of New Mexico Albuquerque NM USA. Department of Molecular Genetics and Microbiology University of New Mexico Albuquerque NM USA. UNM Comprehensive Cancer Center University of New Mexico Albuquerque NM USA. Cardiovascular Research Group School of Healthcare Science Manchester Metropolitan University Manchester UK. British Society for Nanomedicine Liverpool UK. Institute of Life Science Swansea University Medical School Swansea University Swansea UK. Strathclyde Institute of Pharmacy and Biomedical Sciences University of Strathclyde Glasgow UK. Department of Molecular and Clinical Pharmacology Institute of Translational Medicine University of Liverpool Liverpool UK. Department of Chemistry School of Physical Sciences University of Liverpool Liverpool UK. Centre for Cancer Research and Cell Biology Queen's University of Belfast Belfast UK. National Center for Nanoscience and Technology of China Beijing China. Cancer Research Technology Program Nanotechnology Characterization Laboratory Frederick National Laboratory for Cancer Research Frederick MD USA. Laboratory of Animal Sciences Program Center for Advanced Preclinical Research Frederick National Laboratory for Cancer Research Frederick National Laboratory for Cancer Research Frederick MD USA. Trinity Translational Medicine Institute Department of Clinical Medicine Trinity College Dublin Dublin Ireland. Laboratory for Biological Characterisation of Advanced Materials Trinity Translational Medicine Institute Trinity College Dublin Dublin Ireland. Nanomedicine Group Advanced Materials and Bioengineering Research (AMBER) centre Centre

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Fano Resonances: Tunable Fano Resonance and Plasmon–Exciton Coupling in Single Au Nanotriangles on Monolayer WS2at Room Temperature (Adv. Mater. 22/2018)

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Advanced Materials 2018年第22期30卷

作者： Mingsong Wang Alex Krasnok Tianyi Zhang Leonardo Scarabelli He Liu Zilong Wu Luis M. Liz‐Marzán Mauricio Terrones Andrea Alù Yuebing Zheng Department of Mechanical Engineering Materials Science and Engineering Program Texas Materials Institute The University of Texas at Austin Austin TX 78712 USA Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX 78712 USA Department of Materials Science and Engineering The Pennsylvania State University University Park PA 16802 USA Bionanoplasmonics Laboratory CIC biomaGUNE Paseo de Miramón 182 20014 Donostia‐San Sebastián Spain Department of Chemistry and Biochemistry California NanoSystems Institute University of California Los Angeles Los Angeles CA 90095 USA Department of Chemistry The Pennsylvania State University University Park PA 16802 USA Ikerbasque Basque Foundation for Science 48013 Bilbao Spain Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine CIBER‐BBN 20014 Donostia‐San Sebastián Spain Department of Physics and Center for 2‐Dimensional and Layered Materials The Pennsylvania State University University Park PA 16802 USA Department of Materials Science and Engineering & Chemical Engineering Carlos III University of Madrid Avenida Universidad 30 28911 Leganés Madrid Spain IMDEA Materials Institute Eric Kandel 2 Getafe Madrid 28005 Spain

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Human Bone Xenografts: from Preclinical Testing for Regenerative Medicine to Modeling of Diseases

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Current Molecular Biology Reports 2016年第3期2卷 158-170页

作者： Mark Seow Khoon Chong Chaolemeng Bao King Pan Ng Jing Lim Jerry Kok Yen Chan Division of Bioengineering School of Chemical and Biomedical Engineering Nanyang Technological University Singapore Singapore Department of Obstetrics and Gynaecology Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Cancer and Stem Cell Biology Program Duke-NUS Graduate Medical School Singapore Singapore Department of Reproductive Medicine KK Women’s and Children’s Hospital Singapore Singapore

Xenografting involves the transplantation of human tissue or cells into animal models and is an important tool for regenerative medicine research. Implantation of engineered human bone tissues into animal models, for example, is performed in preclinical evaluations of product safety and efficacy. With the advent of improved experimental methodologies, these models are further being exploited to interrogate molecular mechanisms and physiological interactions in vivo. In parallel to these developments, patient-derived xenograft murine models of cancer are increasingly being studied for various applications in cancer research and therapy; it follows that xenograft models in tissue engineering may be adapted for such approaches. In this review, we first discuss the development of human bone xenograft models to recapitulate physiological states in regenerative medicine. Subsequently, we discuss the use of these techniques for applications in modeling pathological states in skeletal oncology, namely, hematopoietic malignancies, bone metastatic disease, and primary bone malignancy.

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Macromol. Chem. Phys. 11/2016

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Macromolecular Chemistry and Physics 2016年第11期217卷

作者： Jonathan Whitlow Settimio Pacelli Arghya Paul BioIntel Research Laboratory Department of Chemical and Petroleum Engineering Bioengineering Program School of Engineering University of Kansas Lawrence KS USA

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Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis

arXiv

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arXiv 2017年

作者： Sung, Jaeyun Kim, Seunghyeon Cabatbat, Josephine Jill T. Jang, Sungho Jin, Yong-Su Jung, Gyoo Yeol Chia, Nicholas Kim, Pan-Jun Asia Pacific Center for Theoretical Physics Pohang Gyeongbuk37673 Korea Republic of Center for Computational and Integrative Biology Massachusetts General Hospital Harvard Medical School BostonMA02114 United States Broad Institute of MIT and Harvard CambridgeMA02142 United States Department of Physics Pohang University of Science and Technology Pohang Gyeongbuk37673 Korea Republic of Abdus Salam International Centre for Theoretical Physics Trieste34151 Italy Department of Chemical Engineering Pohang University of Science and Technology Pohang Gyeongbuk37673 Korea Republic of Department of Food Science and Human Nutrition University of Illinois at Urbana-Champaign UrbanaIL61801 United States Carl R. Woese Institute for Genomic Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Interdisciplinary Bioscience and Bioengineering Pohang University of Science and Technology Pohang Gyeongbuk37673 Korea Republic of Microbiome Program Center for Individualized Medicine Mayo Clinic RochesterMN55905 United States Department of Surgery Mayo Clinic RochesterMN55905 United States Department of Biomedical Engineering Mayo College RochesterMN55905 United States Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of

A system-level framework of complex microbe-microbe and host-microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ~570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently-produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut. Copyright © 2017, The Authors. All rights reserved.

关键词： Bacteria

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