检索结果-内蒙古大学图书馆

SSRN 2018年

作者： MacLeod, Graham Bozek, Danielle A. Rajakulendran, Nishani Monteiro, Vernon Ahmadi, Moloud Steinhart, Zachary Kushida, Michelle M. Yu, Helen Coutinho, Fiona J. Restall, Ian Hao, Xiaoguang Hart, Traver Luchman, H. Artee Weiss, Samuel Dirks, Peter B. Angers, Stephane Leslie Dan Faculty of Pharmacy University of Toronto ONM5S 3M2 Canada Hotchkiss Brain Institute Department of Cell Biology and Anatomy Cumming School of Medicine University of Calgary CalgaryABT2N 4N1 Canada Developmental and Stem Cell Biology Program and Arthur and Sonia Labatt Brain Tumour Research Centre The Hospital for Sick Children TorontoONM5G 0A4 Canada Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center HoustonTX77030 United States Department of Molecular Genetics Department of Laboratory Medicine and Pathobiology Division of Neurosurgery Department of Surgery University of Toronto ON Canada Division of Neurosurgery The Hospital for Sick Children TorontoONM5G 0A4 Canada Department of Biochemistry Faculty of Medicine University of Toronto ONM5G 0A4 Canada

Successful glioblastoma (GBM) therapies have remained elusive due to limitations in understanding mechanisms of growth and survival of the tumorigenic population. Using CRISPR-Cas9 approaches in patientderived GBM stem cells to interrogate function of the coding genome, we identify diverse actionable pathways responsible for growth that reveal the gene-essential circuitry of GBM stemness. In particular, we describe the Sox developmental transcription factor family;H3K79 methylation by DOT1L;and ufmylation stress responsiveness programs as essential for GBM stemness. Additionally, we find mechanisms of temozolomide resistance and sensitivity that could lead to combination strategies with this standard of care treatment. By reaching beyond static genome analysis of bulk tumors, with a genome wide functional approach, we dive deep into a broad range of biological processes to provide new understanding of GBM growth and treatment resistance. © 2018, The Authors. All rights reserved.

关键词： Stem cells

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Heterogeneity analysis provides evidence for a genetically homogeneous subtype of bipolar-disorder

arXiv

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arXiv 2024年

作者： McGrouther, Caroline C. Rangan, Aaditya V. Florio, Arianna Di Elman, Jeremy A. Schork, Nicholas J. Kelsoe, John Stahl, Eli A. Breen, Gerome Forstner, Andreas J. McQuillin, Andrew Ripke, Stephan Trubetskoy, Vassily Mattheisen, Manuel Wang, Yunpeng Coleman, Jonathan R.I. Gaspar, Héléna A. de Leeuw, Christiaan A. Steinberg, Stacy Whitehead Pavlides, Jennifer M. Trzaskowski, Maciej Byrne, Enda M. Pers, Tune H. Holmans, Peter A. Richards, Alexander L. Abbott, Liam Agerbo, Esben Akil, Huda Albani, Diego Alliey-Rodriguez, Ney Als, Thomas D. Anjorin, Adebayo Antilla, Verneri Awasthi, Swapnil Badner, Judith A. Bækvad-Hansen, Marie Barchas, Jack D. Bass, Nicholas Bauer, Michael Belliveau, Richard Bergen, Sarah E. Pedersen, Carsten Bøcker Bøen, Erlend Boks, Marco P. Boocock, James Budde, Monika Bunney, William Burmeister, Margit Bybjerg-Grauholm, Jonas Byerley, William Casas, Miquel Cerrato, Felecia Cervantes, Pablo Chambert, Kimberly Charney, Alexander W. Chen, Danfeng Churchhouse, Claire Clarke, Toni-Kim Coryell, William Craig, David W. Cruceanu, Cristiana Curtis, David Czerski, Piotr M. Dale, Anders M. de Jong, Simone Degenhardt, Franziska Del-Favero, Jurgen DePaulo, J. Raymond Djurovic, Srdjan Dobbyn, Amanda L. Dumont, Ashley Elvsåshagen, Torbjørn Escott-Price, Valentina Fan, Chun Chieh Fischer, Sascha B. Flickinger, Matthew Foroud, Tatiana M. Forty, Liz Frank, Josef Fraser, Christine Freimer, Nelson B. Frisén, Louise Gade, Katrin Gage, Diane Garnham, Julie Giambartolomei, Claudia Pedersen, Marianne Giørtz Goldstein, Jaqueline Gordon, Scott D. Gordon-Smith, Katherine Green, Elaine K. Green, Melissa J. Greenwood, Tiffany A. Grove, Jakob Guan, Weihua Guzman-Parra, José Hamshere, Marian L. Hautzinger, Martin Heilbronner, Urs Herms, Stefan Hipolito, Maria Hoffmann, Per Holland, Dominic Huckins, Laura Jamain, Stéphane Johnson, Jessica S. Kandaswamy, Radhika Karlsson, Robert Kennedy, James L. Kittel-Schneider, Sarah Knowles, James A. Kogevinas, Manolis Koller, Anna C. Kupka, Ralph Lavebratt, Catharina Lawrence, Jacob Lawson, Courant Institute of Mathematical Sciences New York University New YorkNY United States School of Medicine Division of Psychological Medicine and Clinical Neurosciences Cardiff University Cardiff United Kingdom Department of Psychiatry University of California San Diego San DiegoCA United States The Translational Genomics Research Institute Quantitative Medicine and Systems Biology PhoenixAZ United States Department of Psychiatry University of California San Diego La Jolla CA United States Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY United States Department of Psychiatry Icahn School of Medicine at Mount Sinai New YorkNY United States Medical and Population Genetics Broad Institute CambridgeMA United States MRC Social Genetic and Developmental Psychiatry Centre King’s College London London United Kingdom NIHR BRC for Mental Health King’s College London London United Kingdom University of Basel Basel Switzerland Institute of Human Genetics University of Bonn School of Medicine University Hospital Bonn DE Bonn Germany Centre for Human Genetics University of Marburg DE Marburg Germany Institute of Medical Genetics and Pathology University Hospital Basel Basel Switzerland Division of Psychiatry University College London London United Kingdom Stanley Center for Psychiatric Research Broad Institute CambridgeMA United States Department of Psychiatry and Psychotherapy Charité - Universitätsmedizin Berlin Germany Analytic and Translational Genetics Unit Massachusetts General Hospital BostonMA United States iSEQ Center for Integrative Sequencing Aarhus University DK Aarhus Denmark Department of Biomedicine - Human Genetics Aarhus University DK Aarhus Denmark Department of Clinical Neuroscience Centre for Psychiatry Research Karolinska Institutet Stockholm Sweden Department of Psychiatry Psychosomatics and Psychotherapy Center of Mental Health University Hospital Würzburg DE Würzburg Ge

Background: Bipolar Disorder (BD) is a complex disease. It is heterogeneous, both at the phenotypic and genetic level, although the extent and impact of this heterogeneity is not fully understood. One way to assess this heterogeneity is to look for patterns in the subphenotype data. Because of the variability in how phenotypic data was collected by the various BD studies over the years, homogenizing this subphenotypic data is a challenging task, and so is replication. An alternative methodology, taken here, is to set aside the intricacies of subphenotype and allow the genetic data itself to determine which subjects define a homogeneous genetic subgroup (termed ‘bicluster’ below). Results: In this paper, we leverage recent advances in heterogeneity analysis to look for genetically-driven subgroups (i.e., biclusters) within the broad phenotype of Bipolar Disorder. We first apply this covariate-corrected biclustering algorithm to a cohort of 2524 BD cases and 4106 controls from the Bipolar Disease Research Network (BDRN) within the Psychiatric Genomics Consortium (PGC). We find evidence of genetic heterogeneity delineating a statistically significant bicluster comprising a subset of BD cases which exhibits a disease-specific pattern of differential-expression across a subset of SNPs. This disease-specific genetic pattern (i.e., ‘genetic subgroup’) replicates across the remaining data-sets collected by the PGC containing 5781/8289, 3581/7591, and 6825/9752 cases/controls, respectively. This genetic subgroup (discovered without using any BD subtype information) was more prevalent in Bipolar type-I than in Bipolar type-II. Conclusions: Our methodology has successfully identified a replicable homogeneous genetic subgroup of bipolar disorder. This subgroup may represent a collection of correlated genetic risk-factors for BDI. By investigating the subgroup’s bicluster-informed polygenic-risk-scoring (PRS), we find that the disease-specific pattern highlighted by the bicluste

关键词： Economic and social effects

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LifeTime and improving European healthcare through cell-based interceptive medicine (vol 587, pg 377, 2020)

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NATURE 2021年第7852期592卷 E8-E8页

作者： Rajewsky, Nikolaus Almouzni, Genevieve Gorski, Stanislaw A. Aerts, Stein Amit, Ido Bertero, Michela G. Bock, Christoph Bredenoord, Annelien L. Cavalli, Giacomo Chiocca, Susanna Clevers, Hans De Strooper, Bart Eggert, Angelika Ellenberg, Jan Fernandez, Xose M. Figlerowicz, Marek Gasser, Susan M. Hubner, Norbert Kjems, Jorgen Knoblich, Jurgen A. Krabbe, Grietje Lichter, Peter Linnarsson, Sten Marine, Jean-Christophe Marioni, John C. Marti-Renom, Marc A. Netea, Mihai G. Nickel, Dorthe Nollmann, Marcelo Novak, Halina R. Parkinson, Helen Piccolo, Stefano Pinheiro, Ines Pombo, Ana Popp, Christian Reik, Wolf Roman-Roman, Sergio Rosenstiel, Philip Schultze, Joachim L. Stegle, Oliver Tanay, Amos Testa, Giuseppe Thanos, Dimitris Theis, Fabian J. Torres-Padilla, Maria-Elena Valencia, Alfonso Vallot, Celine van Oudenaarden, Alexander Vidal, Marie Voet, Thierry Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine in the Helmholtz Association Berlin Germany Charité-Universitätsmedizin Berlin Germany Berlin Institute of Health (BIH) Berlin Germany German Center for Cardiovascular Research (DZHK) Partner Site Berlin Berlin Germany Institute for Biology Humboldt University of Berlin Berlin Germany Cardiovascular and Metabolic Sciences Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany Department of Pediatric Oncology/Hematology Charité-Universitätsmedizin Berlin Berlin Germany Institut Curie CNRS PSL Research University Sorbonne Université Nuclear Dynamics Unit Equipe Labellisée Ligue contre le cancer Paris France VIB Center for Brain and Disease Research Leuven Belgium Department of Human Genetics KU Leuven Leuven Belgium Department of Neurosciences KU Leuven Leuven Belgium UK Dementia Research Institute at UCL University College London London UK Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK Department of Immunology Weizmann Institute of Science Rehovot Israel Centre for Genomic Regulation (CRG) Barcelona Institute of Science and Technology Barcelona Spain CNAG-CRG Centre for Genomic Regulation Barcelona Institute of Science and Technology Barcelona Spain Universitat Pompeu Fabra Barcelona Spain ICREA Barcelona Spain CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria Department of Laboratory Medicine Medical University of Vienna Vienna Austria Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases Vienna Austria Department of Medical Humanities Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht The Netherlands Institute of Human Genetics UMR 9002 CNRS and University of Montpellier Montpellier France Department of Experimental Oncology IEO European Institute of Oncology IRCCS Milan Italy Department of Oncology and Hemato

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Jamming of Deformable Polygons

arXiv

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arXiv 2018年

作者： Boromand, Arman Signoriello, Alexandra Ye, Fangfu O'Hern, Corey S. Shattuck, Mark D. Beijing Natl. Laboratory for Condensed Matter Physics and Cas Key Laboratory of Soft Matter Physics Institute of Physics Chinese Academy of Sciences Beijing China Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States School of Physical Sciences University of Chinese Academy of Sciences Beijing China Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department City College of City University of New York New YorkNY10031 United States

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Quantum annealing versus classical machine learning applied to a simplified computational biology problem

arXiv

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arXiv 2018年

作者： Li, Richard Di Felice, Rosa Rohs, Remo Lidar, Daniel Department of Chemistry University of Southern California Los AngelesCA90089 United States Computational Biology and Bioinformatics Program Department of Biological Sciences University of Southern California Los AngelesCA90089 United States Center for Quantum Information Science & Technology University of Southern California Los AngelesCA90089 United States Department of Physics and Astronomy University of Southern California Los AngelesCA90089 United States Center S3 CNR Institute of Nanoscience Via Campi 213/A Modena41125 Italy Department of Computer Science University of Southern California Los AngelesCA90089 United States Department of Electrical Engineering University of Southern California Los AngelesCA90089 United States

Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to predict binding specificity. Using simplified datasets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified datasets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems. Copyright © 2018, The Authors. All rights reserved.

关键词： Simulated annealing

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The Genomic Landscape of Pediatric Myelodysplastic Syndromes

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BLOOD 2016年第22期128卷 956-956页

作者： Schwartz, Jason R. Walsh, Michael P. Ma, Jing Lamprecht, Tamara Wang, Shuoguo Wu, Gang Easton, John Ribeiro, Raul C. Klco, Jeffery M. Department of Oncology St. Jude Children's Research Hospital Memphis TN Department of Pathology St. Jude Children's Research Hospital Memphis TN Department of Computational Biology St. Jude Children's Research Hospital Memphis TN Department of Computational Biology St. Jude Children's Research Hospital Memphis TN Department of Computational Biology and Bioinformatics St. Jude Children's Research Hospital Memphis TN Department of Oncology and Global Medicine International Outreach Program St. Jude Children's Research Hospital Memphis TN

Myelodysplastic syndromes are uncommon in children (incidence of ~2 cases/million) and have a poor prognosis. Despite the wealth of knowledge about the genomic landscape of adult MDS, much less is understood about pediatric MDS, and many recurrent mutations found in adults are not common in children (Hirabayashi, Blood 2012). Furthermore, the clinical presentation, bone marrow morphology, and cytogenetic abnormalities are also different when comparing adult and pediatric MDS, suggesting disparate underlying mechanisms. Here we describe the somatic and germline genomic landscape of pediatric MDS using whole exome sequencing (WES) and RNA-sequencing. We evaluated 88 diagnostic bone marrow samples obtained from the St. Jude Children's Research Hospital Tissue Bank from patients diagnosed between 1988 and 2015. This cohort contains 34 primary MDS, including Refractory Cytopenia of Childhood/RCC (n=19) and Refractory Anemia with Excess Blasts/RAEB (n=15). For comparison, we also included 32 treatment-related (tMDS), 14 MDS/MPN (including 10 Juvenile Myelomonocytic Leukemia/JMML), and 8 cases of AML with Myelodysplasia-Related Changes/AML-MRC (including 5 cases that previously would have been classified as RAEB in transformation/RAEB-T). WES was completed for 87 tumor/normal pairs (tumor only, n=1) using the Nextera Rapid Capture Expanded Exome (Illumina). Normal comparator gDNA was obtained from flow-sorted lymphocytes and variants were classified as germline if present at greater than 30% variant allele frequency (VAF) in the lymphocyte sample; thus, bone marrow mosaicism cannot be excluded. Mean sequencing coverage for the tumor and normal samples were 102x and 105x, respectively. An average of 7.9 variants were observed per patient in the primary MDS cohort (RCC=6.3, RAEB=10.2), compared to 25.5/patient in the tMDS cohort (p=0.001). Copy number information, obtained from WES data, determined that deletions involving chromosome 7 were frequent (n=28, 32%). Approximatel

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Pitfalls of restriction enzyme analysis in identifying, characterizing,typing, and naming viral pathogens in the era of whole genome data, as illustrated by HAdV type 55

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Virologica Sinica 2016年第5期31卷 448-453页

作者： Qiwei Zhang Shoaleh Dehghan Donald Seto Biosafety Level-3 Laboratory School of Public HealthSouthern Medical University(Guangdong Provincial Key Laboratory of Tropical Disease Research) Chemistry Department American University Bioinformatics and Computational Biology Program School of Systems BiologyGeorge Mason University

Restriction endonuclease analysis(REA),or restriction fragment length polymorphism(RFLP),was useful for identifying and determining the relatedness and putative identities of microbial strains(Tang et al.,1997)and for... 详细信息

关键词： and naming viral pathogens in the era of whole genome data DLL QS characterizing,typing Pitfalls of restriction enzyme analysis in identifying REA type

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Leap forward with insect genomics

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Insect Science 2016年第3期23卷 332-334页

作者： Bin Chen Xian-Hui Wang Zhijian Tu Yong-Ping Huang Institute of Entomology and Molecular Biology Chongqing Normal University Chongqing China State Key Laboratory of IntegratedManagement of Pest Insects & Rodents Institute of Zoology Chinese Academy of Sciences Beijing China Department of Biochemistry Fralin Life Science Institute and Program in Genetics Bioinformatics and Computational Biology Virginia Tech Blacksburg Virginia USAand Key Laboratory of Insect Development and Evolutionary Biology Institute of Plant Physiology and Ecology Shanghai Institutes forBiological Sciences Chinese Academy of Sciences Shanghal China

Insects comprise the largest animal group in the world. More than a million insect species have been identified, leaving possibly two to three times more to be discovered. Systematic understanding of the genetic content of an insect species began more than 15 years ago when the genome sequence of the fruit fly, Drosophila melanogaster was reported （Adams et al., 2000）. For the first time, scientists were able to glean unprecedented insights into the genetic blueprint of a model insect, presenting its 13 600 protein coding genes. Such fundamental information opened a floodgate for comparative and functional analysis of these genes and enabled systems approaches to comparative, evolutionary and population genomic studies. In recent years, next-generation and third-generation sequencing platforms brought genome sequencing to more than a hundred insect species, reach- ing far beyond the few well-studied model organisms. The international consortium for insect genomes is work- ing toward an ambitious goal to sequence 5000 species of insects.

关键词： Drosophila melanogaster Insect genetics DNA sequencing Genetic code Insect evolution Insect population genetics

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Genomic profiling in advanced stage non-small-cell lung cancer patients with platinum-based chemotherapy identifies germline variants with prognostic value in SMYD2

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Cancer Treatment and Research Communications 2018年 15卷 21-31页

作者： Galván-Femenía, Iván Guindo, Marta Duran, Xavier Calabuig-Fariñas, Sílvia Mercader, Josep Maria Ramirez, Jose Luis Rosell, Rafael Torrents, David Carreras, Anna Kohno, Takashi Jantus-Lewintre, Eloisa Camps, Carlos Perucho, Manuel Sumoy, Lauro Yokota, Jun de Cid, Rafael Genomes For life-GCAT Lab. Program of Predictive and Personalized Medicine of Cancer (PMPPC) Institute for Health Science Research Germans Trias i Pujol (IGTP) Can Ruti Biomedical Campus Crta de Can Ruti Camí de les Escoles S/N Badalona 08916 Barcelona Spain Barcelona Supercomputing Center (BSC-CNS) Joint BSC-CRG-IRB Research Program in Computational Biology Carrer de Jordi Girona 29-31 Barcelona 08034 Spain Department of Medical Oncology Hospital General Universitario de Valencia Avenida Tres Cruces 2 València 46014 Spain Molecular Oncology Laboratory Fundación Hospital General Universitario de Valencia Avda. Tres Cruces s/n València 46014 Spain Department of Pathology Universitat de València Av. de Blasco Ibáñez 13 València 46010 Spain Program of Predictive and Personalized Medicine of Cancer (PMPPC) Institute for Health Science Research Germans Trias i Pujol (IGTP) Can Ruti Biomedical Campus Crta de Can Ruti Camí de les Escoles S/N Badalona 08916 Barcelona Spain ICREA Catalan Institution for Research and Advanced Studies Spain Division of Genome Biology National Cancer Center Research Institute 5-1-1 Tsukiji Chuo-ku Tokyo 104-0045 Japan Molecular Oncology Laboratory Fundación Hospital General Universitario de València Avda. Tres Cruces s/n València 46014 Spain Department of Biotechnology Universitat Politècnica de València Camí de Vera s/n València 46022 Spain Department of Medicine Universitat de València Av. de Blasco Ibáñez 13 València 46010 Spain Genomics and Bioinformatics. Program of Predictive and Personalized Medicine of Cancer (PMPPC) Institute for Health Science Research Germans Trias i Pujol (IGTP) Can Ruti Biomedical Campus Crta de Can Ruti Camí de les Escoles S/N Badalona 08916 Barcelona Spain Programs in Metabolism and Medical & Population Genetics Broad Institute of Harvard and MIT Cambridge Massachusetts United States Diabetes Unit and Center for Human Genetic Research Massachusetts General Hospital Boston Massachuse

Objective: The aim of the study was to investigate the relationship between germline variations as a prognosis biomarker in patients with advanced Non-Small-Cell-Lung-Cancer (NSCLC) subjected to first-line platinum-based treatment. Materials and Methods: We carried out a two-stage genome-wide-association study in non-small-cell lung cancer patients with platinum-based chemotherapy in an exploratory sample of 181 NSCLC patients from Caucasian origin, followed by a validation on 356 NSCLC patients from the same ancestry (Valencia, Spain). Results: We identified germline variants in SMYD2 as a prognostic factor for survival in patients with advanced NSCLC receiving chemotherapy. SMYD2 alleles are associated to a decreased overall survival and with a reduced Time to Progression. In addition, enrichment pathway analysis identified 361 variants in 40 genes to be involved in poorer outcome in advanced-stage NSCLC patients. Conclusion: Germline SMYD2 alleles are associated with bad clinical outcome of first-line platinum-based treatment in advanced NSCLC patients. This result supports the role of SMYD2 in the carcinogenic process, and might be used as prognostic signature directing patient stratification and the choice of therapy. A two-Stage Genome wide association study in Caucasian population reveals germline genetic variation in SMYD2 associated to progression disease in first-line platinum-based treatment in advanced NSCLC patients. SMYD2 profiling might have prognostic / predictive value directing choice of therapy and enlighten current knowledge on pathways involved in human carcinogenesis as well in resistance to chemotherapy. © 2018 The Authors

关键词： Advanced stage Genome-Wide-Association Studies Lung cancer NSCLC Prognostic factors

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

引用

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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