Institution
Institute for Systems Biology
Nonprofit•Seattle, Washington, United States•
About: Institute for Systems Biology is a nonprofit organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Population & Proteomics. The organization has 1277 authors who have published 2777 publications receiving 353165 citations.
Topics: Population, Proteomics, Gene, Proteome, Systems biology
Papers published on a yearly basis
Papers
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TL;DR: An open-source software toolkit, SpectraST, is developed and validated to enable proteomics researchers to build spectral libraries and to integrate this promising approach in their data-analysis pipeline.
Abstract: Spectral searching, based on matching experimental peptide spectra to reference spectral libraries, is gaining interest as an alternative to traditional sequence-database searching in mass spectrometry–based proteomics. A software tool, SpectraST, now allows users to build their own high-quality spectral libraries from raw data.
257 citations
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Washington University in St. Louis1, Discovery Institute2, Institute for Systems Biology3, Université libre de Bruxelles4, Genome Institute of Singapore5, Johns Hopkins University6, University of Cambridge7, Baylor College of Medicine8, Broad Institute9, Harvard University10, University of Texas MD Anderson Cancer Center11, University of California, Santa Cruz12, University of North Carolina at Chapel Hill13, National Institutes of Health14
TL;DR: Results from the TCGA PanCancer Atlas project will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing.
256 citations
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Technical University of Denmark1, VU University Amsterdam2, Heidelberg University3, École Polytechnique Fédérale de Lausanne4, RWTH Aachen University5, University of California, San Diego6, University of Toronto7, Institute for Systems Biology8, National Autonomous University of Mexico9, University of Tübingen10, University of Queensland11, Argonne National Laboratory12, Leiden University13, Technical University of Madrid14, Spanish National Research Council15, Hanze University of Applied Sciences16, Norwegian University of Life Sciences17, Wellcome Trust18, KAIST19, Max Planck Society20, Humboldt University of Berlin21, Wageningen University and Research Centre22, Agency for Science, Technology and Research23, Sungkyunkwan University24, King's College London25, Royal Institute of Technology26, Chinese Academy of Sciences27, University of Virginia28, Chalmers University of Technology29, University of Arkansas for Medical Sciences30, Oxford Brookes University31, University of Minho32, Nova Southeastern University33, University of Düsseldorf34
TL;DR: A community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality, and advocate adoption of the latest version of the Systems Biology Markup Language level 3 flux balance constraints (SBML3FBC) package as the primary description and exchange format.
Abstract: We acknowledge D. Dannaher and A. Lopez for their supporting work on the Angular parts of MEMOTE; resources and support from the DTU Computing Center; J. Cardoso, S. Gudmundsson, K. Jensen and D. Lappa for their feedback on conceptual details; and P. D. Karp and I. Thiele for critically reviewing the manuscript. We thank J. Daniel, T. Kristjansdottir, J. Saez-Saez, S. Sulheim, and P. Tubergen for being early adopters of MEMOTE and for providing written testimonials. J.O.V. received the Research Council of Norway grants 244164 (GenoSysFat), 248792 (DigiSal) and 248810 (Digital Life Norway); M.Z. received the Research Council of Norway grant 244164 (GenoSysFat); C.L. received funding from the Innovation Fund Denmark (project “Environmentally Friendly Protein Production (EFPro2)”); C.L., A.K., N. S., M.B., M.A., D.M., P.M, B.J.S., P.V., K.R.P. and M.H. received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 686070 (DD-DeCaF); B.G.O., F.T.B. and A.D. acknowledge funding from the US National Institutes of Health (NIH, grant number 2R01GM070923-13); A.D. was supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections; N.E.L. received funding from NIGMS R35 GM119850, Novo Nordisk Foundation NNF10CC1016517 and the Keck Foundation; A.R. received a Lilly Innovation Fellowship Award; B.G.-J. and J. Nogales received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 686585 for the project LIAR, and the Spanish Ministry of Economy and Competitivity through the RobDcode grant (BIO2014-59528-JIN); L.M.B. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 633962 for project P4SB; R.F. received funding from the US Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE-SC0010429; A.M., C.Z., S.L. and J. Nielsen received funding from The Knut and Alice Wallenberg Foundation, Advanced Computing program, grant #DE-SC0010429; S.K.’s work was in part supported by the German Federal Ministry of Education and Research (de.NBI partner project “ModSim” (FKZ: 031L104B)); E.K. and J.A.H.W. were supported by the German Federal Ministry of Education and Research (project “SysToxChip”, FKZ 031A303A); M.K. is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054); J.A.P. and G.L.M. acknowledge funding from US National Institutes of Health (T32-LM012416, R01-AT010253, R01-GM108501) and the Wagner Foundation; G.L.M. acknowledges funding from a Grand Challenges Exploration Phase I grant (OPP1211869) from the Bill & Melinda Gates Foundation; H.H. and R.S.M.S. received funding from the Biotechnology and Biological Sciences Research Council MultiMod (BB/N019482/1); H.U.K. and S.Y.L. received funding from the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea; H.U.K. received funding from the Bio & Medical Technology Development Program of the NRF, the Ministry of Science and ICT (NRF-2018M3A9H3020459); P.B., B.J.S., Z.K., B.O.P., C.L., M.B., N.S., M.H. and A.F. received funding through Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517); D.-Y.L. received funding from the Next-Generation BioGreen 21 Program (SSAC, PJ01334605), Rural Development Administration, Republic of Korea; G.F. was supported by the RobustYeast within ERA net project via SystemsX.ch; V.H. received funding from the ETH Domain and Swiss National Science Foundation; M.P. acknowledges Oxford Brookes University; J.C.X. received support via European Research Council (666053) to W.F. Martin; B.E.E. acknowledges funding through the CSIRO-UQ Synthetic Biology Alliance; C.D. is supported by a Washington Research Foundation Distinguished Investigator Award. I.N. received funding from National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) (grant P20GM125503).
255 citations
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TL;DR: Using the Boolean approach, this work shows what it believes to be the first direct evidence that the underlying genetic network of HeLa cells appears to operate either in the ordered regime or at the border between order and chaos but does not appear to be chaotic.
Abstract: Two important theoretical approaches have been developed to generically characterize the relationship between the structure and function of large genetic networks: The continuous approach, based on reaction-kinetics differential equations, and the Boolean approach, based on difference equations and discrete logical rules. These two approaches do not always coincide in their predictions for the same system. Nonetheless, both of them predict that the highly nonlinear dynamics exhibited by genetic regulatory systems can be characterized into two broad regimes, to wit, an ordered regime where the system is robust against perturbations, and a chaotic regime where the system is extremely sensitive to perturbations. It has been a plausible and long-standing hypothesis that genomic regulatory networks of real cells operate in the ordered regime or at the border between order and chaos. This hypothesis is indirectly supported by the robustness and stability observed in the phenotypic traits of living organisms under genetic perturbations. However, there has been no systematic study to determine whether the gene-expression patterns of real cells are compatible with the dynamically ordered regimes predicted by theoretical models. Using the Boolean approach, here we show what we believe to be the first direct evidence that the underlying genetic network of HeLa cells appears to operate either in the ordered regime or at the border between order and chaos but does not appear to be chaotic.
252 citations
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TL;DR: This work tracked global gene expression in the brains of eight distinct mouse strain–prion strain combinations throughout the progression of the disease to capture the effects of prion strain, host genetics, and PrP concentration on disease incubation time and suggests some possible therapeutic approaches.
Abstract: Prions cause transmissible neurodegenerative diseases and replicate by conformational conversion of normal benign forms of prion protein (PrPC) to disease-causing PrPSc isoforms. A systems approach to disease postulates that disease arises from perturbation of biological networks in the relevant organ. We tracked global gene expression in the brains of eight distinct mouse strain–prion strain combinations throughout the progression of the disease to capture the effects of prion strain, host genetics, and PrP concentration on disease incubation time. Subtractive analyses exploiting various aspects of prion biology and infection identified a core of 333 differentially expressed genes (DEGs) that appeared central to prion disease. DEGs were mapped into functional pathways and networks reflecting defined neuropathological events and PrPSc replication and accumulation, enabling the identification of novel modules and modules that may be involved in genetic effects on incubation time and in prion strain specificity. Our systems analysis provides a comprehensive basis for developing models for prion replication and disease, and suggests some possible therapeutic approaches.
249 citations
Authors
Showing all 1292 results
Name | H-index | Papers | Citations |
---|---|---|---|
Younan Xia | 216 | 943 | 175757 |
Ruedi Aebersold | 182 | 879 | 141881 |
David Haussler | 172 | 488 | 224960 |
Steven P. Gygi | 172 | 704 | 129173 |
Nahum Sonenberg | 167 | 647 | 104053 |
Leroy Hood | 158 | 853 | 128452 |
Mark H. Ellisman | 117 | 637 | 55289 |
Wei Zhang | 112 | 1189 | 93641 |
John Ralph | 109 | 442 | 39238 |
Eric H. Davidson | 106 | 454 | 47058 |
James R. Heath | 103 | 425 | 58548 |
Alan Aderem | 99 | 246 | 46682 |
Anne-Claude Gingras | 97 | 336 | 40714 |
Trey Ideker | 97 | 306 | 72276 |
Michael H. Gelb | 94 | 506 | 34714 |