1
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Kaizu K, Takahashi K. Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico. Dev Growth Differ 2023; 65:554-564. [PMID: 37856476 DOI: 10.1111/dgd.12897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 09/06/2023] [Accepted: 10/14/2023] [Indexed: 10/21/2023]
Abstract
With advances in high-throughput, large-scale in vivo measurement and genome modification techniques at the single-nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer-aided design is a powerful tool to design new cells. Whole-cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome-wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole-cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole-cell modeling has been extended to multiple scales, including high-resolution modeling at the single-nucleotide and single-amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole-cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole-genome scale.
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Affiliation(s)
- Kazunari Kaizu
- RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
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2
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Mutsuddy A, Erdem C, Huggins JR, Salim M, Cook D, Hobbs N, Feltus FA, Birtwistle MR. Computational speed-up of large-scale, single-cell model simulations via a fully integrated SBML-based format. BIOINFORMATICS ADVANCES 2023; 3:vbad039. [PMID: 37020976 PMCID: PMC10070034 DOI: 10.1093/bioadv/vbad039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 02/25/2023] [Accepted: 03/22/2023] [Indexed: 04/05/2023]
Abstract
Summary Large-scale and whole-cell modeling has multiple challenges, including scalable model building and module communication bottlenecks (e.g. between metabolism, gene expression, signaling, etc.). We previously developed an open-source, scalable format for a large-scale mechanistic model of proliferation and death signaling dynamics, but communication bottlenecks between gene expression and protein biochemistry modules remained. Here, we developed two solutions to communication bottlenecks that speed-up simulation by ∼4-fold for hybrid stochastic-deterministic simulations and by over 100-fold for fully deterministic simulations. Fully deterministic speed-up facilitates model initialization, parameter estimation and sensitivity analysis tasks. Availability and implementation Source code is freely available at https://github.com/birtwistlelab/SPARCED/releases/tag/v1.3.0 implemented in python, and supported on Linux, Windows and MacOS (via Docker).
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Affiliation(s)
- Arnab Mutsuddy
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Cemal Erdem
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Jonah R Huggins
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA
- School of Computing, Clemson University, Clemson, SC, USA
| | | | | | | | - F Alex Feltus
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA
| | - Marc R Birtwistle
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA
- Department of Bioengineering, Clemson University, Clemson, SC, USA
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3
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Martins dos Santos V, Anton M, Szomolay B, Ostaszewski M, Arts I, Benfeitas R, Dominguez Del Angel V, Domínguez-Romero E, Ferk P, Fey D, Goble C, Golebiewski M, Gruden K, Heil KF, Hermjakob H, Kahlem P, Klapa MI, Koehorst J, Kolodkin A, Kutmon M, Leskošek B, Moretti S, Müller W, Pagni M, Rezen T, Rocha M, Rozman D, Šafránek D, T. Scott W, Sheriff RSM, Suarez Diez M, Van Steen K, Westerhoff HV, Wittig U, Wolstencroft K, Zupanic A, Evelo CT, Hancock JM. Systems Biology in ELIXIR: modelling in the spotlight. F1000Res 2022; 11:ELIXIR-1265. [PMID: 36742342 PMCID: PMC9871403 DOI: 10.12688/f1000research.126734.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/24/2022] [Indexed: 11/09/2022] Open
Abstract
In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.
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Affiliation(s)
- Vitor Martins dos Santos
- Laboratory of Bioprocess Engineering, Wageningen University & Research, Wageningen, 6708 PB, The Netherlands
| | - Mihail Anton
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Gothenburg, SE-41258, Sweden
| | - Barbara Szomolay
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, L-4367, Luxembourg
| | - Ilja Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Rui Benfeitas
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | | | | | - Polonca Ferk
- Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Centre ELIXIR-SI, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - Dirk Fey
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, 4, Ireland
| | - Carole Goble
- Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, SI-1000, Slovenia
| | | | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | - Pascal Kahlem
- Scientific Network Management SL, Barcelona, 08015, Spain
| | - Maria I. Klapa
- Metabolic Engineering & Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT), Patras, 26504, Greece
| | - Jasper Koehorst
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
| | - Alexey Kolodkin
- Competence Center for Methodology and Statistics; Transversal Translational Medicine, Translational Medicine Operations Hub, Luxembourg Institute of Health, Strassen, L-1445, Luxembourg
- ISBE.NL, VU University of Amsterdam, Amsterdam, The Netherlands
| | - Martina Kutmon
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6200 MD, The Netherlands
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Brane Leskošek
- Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Centre ELIXIR-SI, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | | | - Wolfgang Müller
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Marco Pagni
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Tadeja Rezen
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Damjana Rozman
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - David Šafránek
- Faculty of Informatics, Masaryk University, Brno, 602 00, Czech Republic
| | - William T. Scott
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
- UNLOCK, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
| | - Rahuman S. Malik Sheriff
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | - Maria Suarez Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
| | - Kristel Van Steen
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, 3000, Belgium
- BIO3 - Systems Genetics, GIGA-R Medical Genomics, University of Liege, Liege, 4000, Belgium
| | | | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Katherine Wolstencroft
- Leiden Institute of Advanced Computer Science, Leiden University, Leiden, 2333 CA, The Netherlands
| | - Anze Zupanic
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, SI-1000, Slovenia
| | - Chris T. Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - John M. Hancock
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
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4
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Martins dos Santos V, Anton M, Szomolay B, Ostaszewski M, Arts I, Benfeitas R, Dominguez Del Angel V, Domínguez-Romero E, Ferk P, Fey D, Goble C, Golebiewski M, Gruden K, Heil KF, Hermjakob H, Kahlem P, Klapa MI, Koehorst J, Kolodkin A, Kutmon M, Leskošek B, Moretti S, Müller W, Pagni M, Rezen T, Rocha M, Rozman D, Šafránek D, T. Scott W, Sheriff RSM, Suarez Diez M, Van Steen K, Westerhoff HV, Wittig U, Wolstencroft K, Zupanic A, Evelo CT, Hancock JM. Systems Biology in ELIXIR: modelling in the spotlight. F1000Res 2022; 11:ELIXIR-1265. [PMID: 36742342 PMCID: PMC9871403 DOI: 10.12688/f1000research.126734.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/20/2024] [Indexed: 06/05/2024] Open
Abstract
In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.
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Affiliation(s)
- Vitor Martins dos Santos
- Laboratory of Bioprocess Engineering, Wageningen University & Research, Wageningen, 6708 PB, The Netherlands
| | - Mihail Anton
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Gothenburg, SE-41258, Sweden
| | - Barbara Szomolay
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, L-4367, Luxembourg
| | - Ilja Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Rui Benfeitas
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | | | | | - Polonca Ferk
- Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Centre ELIXIR-SI, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - Dirk Fey
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, 4, Ireland
| | - Carole Goble
- Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, SI-1000, Slovenia
| | | | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | - Pascal Kahlem
- Scientific Network Management SL, Barcelona, 08015, Spain
| | - Maria I. Klapa
- Metabolic Engineering & Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT), Patras, 26504, Greece
| | - Jasper Koehorst
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
| | - Alexey Kolodkin
- Competence Center for Methodology and Statistics; Transversal Translational Medicine, Translational Medicine Operations Hub, Luxembourg Institute of Health, Strassen, L-1445, Luxembourg
- ISBE.NL, VU University of Amsterdam, Amsterdam, The Netherlands
| | - Martina Kutmon
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6200 MD, The Netherlands
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Brane Leskošek
- Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Centre ELIXIR-SI, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | | | - Wolfgang Müller
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Marco Pagni
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Tadeja Rezen
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Damjana Rozman
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - David Šafránek
- Faculty of Informatics, Masaryk University, Brno, 602 00, Czech Republic
| | - William T. Scott
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
- UNLOCK, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
| | - Rahuman S. Malik Sheriff
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | - Maria Suarez Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
| | - Kristel Van Steen
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, 3000, Belgium
- BIO3 - Systems Genetics, GIGA-R Medical Genomics, University of Liege, Liege, 4000, Belgium
| | | | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Katherine Wolstencroft
- Leiden Institute of Advanced Computer Science, Leiden University, Leiden, 2333 CA, The Netherlands
| | - Anze Zupanic
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, SI-1000, Slovenia
| | - Chris T. Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - John M. Hancock
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
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5
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Integrative modeling of the cell. Acta Biochim Biophys Sin (Shanghai) 2022; 54:1213-1221. [PMID: 36017893 PMCID: PMC9909318 DOI: 10.3724/abbs.2022115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
A whole-cell model represents certain aspects of the cell structure and/or function. Due to the high complexity of the cell, an integrative modeling approach is often taken to utilize all available information including experimental data, prior knowledge and prior models. In this review, we summarize an emerging workflow of whole-cell modeling into five steps: (i) gather information; (ii) represent the modeled system into modules; (iii) translate input information into scoring function; (iv) sample the whole-cell model; (v) validate and interpret the model. In particular, we propose the integrative modeling of the cell by combining available (whole-cell) models to maximize the accuracy, precision, and completeness. In addition, we list quantitative predictions of various aspects of cell biology from existing whole-cell models. Moreover, we discuss the remaining challenges and future directions, and highlight the opportunity to establish an integrative spatiotemporal multi-scale whole-cell model based on a community approach.
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6
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Emwas AH, Szczepski K, Al-Younis I, Lachowicz JI, Jaremko M. Fluxomics - New Metabolomics Approaches to Monitor Metabolic Pathways. Front Pharmacol 2022; 13:805782. [PMID: 35387341 PMCID: PMC8977530 DOI: 10.3389/fphar.2022.805782] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 01/24/2022] [Indexed: 12/18/2022] Open
Abstract
Fluxomics is an innovative -omics research field that measures the rates of all intracellular fluxes in the central metabolism of biological systems. Fluxomics gathers data from multiple different -omics fields, portraying the whole picture of molecular interactions. Recently, fluxomics has become one of the most relevant approaches to investigate metabolic phenotypes. Metabolic flux using 13C-labeled molecules is increasingly used to monitor metabolic pathways, to probe the corresponding gene-RNA and protein-metabolite interaction networks in actual time. Thus, fluxomics reveals the functioning of multi-molecular metabolic pathways and is increasingly applied in biotechnology and pharmacology. Here, we describe the main fluxomics approaches and experimental platforms. Moreover, we summarize recent fluxomic results in different biological systems.
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Affiliation(s)
- Abdul-Hamid Emwas
- King Abdullah University of Science and Technology, Core Labs, Thuwal, Saudi Arabia
| | - Kacper Szczepski
- Smart-Health Initiative (SHI) and Red Sea Research Center (RSRC), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Inas Al-Younis
- King Abdullah University of Science and Technology (KAUST), Biological and Environmental Sciences & Engineering Division (BESE), Thuwal, Saudi Arabia
| | - Joanna Izabela Lachowicz
- Department of Medical Sciences and Public Health, University of Cagliari, Cittadella Universitaria, Monserrato, Italy
| | - Mariusz Jaremko
- Smart-Health Initiative (SHI) and Red Sea Research Center (RSRC), Biological and Environmental Sciences & Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
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7
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Maritan M, Autin L, Karr J, Covert MW, Olson AJ, Goodsell DS. Building Structural Models of a Whole Mycoplasma Cell. J Mol Biol 2022; 434:167351. [PMID: 34774566 PMCID: PMC8752489 DOI: 10.1016/j.jmb.2021.167351] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 11/04/2021] [Accepted: 11/05/2021] [Indexed: 02/01/2023]
Abstract
Building structural models of entire cells has been a long-standing cross-discipline challenge for the research community, as it requires an unprecedented level of integration between multiple sources of biological data and enhanced methods for computational modeling and visualization. Here, we present the first 3D structural models of an entire Mycoplasma genitalium (MG) cell, built using the CellPACK suite of computational modeling tools. Our model recapitulates the data described in recent whole-cell system biology simulations and provides a structural representation for all MG proteins, DNA and RNA molecules, obtained by combining experimental and homology-modeled structures and lattice-based models of the genome. We establish a framework for gathering, curating and evaluating these structures, exposing current weaknesses of modeling methods and the boundaries of MG structural knowledge, and visualization methods to explore functional characteristics of the genome and proteome. We compare two approaches for data gathering, a manually-curated workflow and an automated workflow that uses homologous structures, both of which are appropriate for the analysis of mesoscale properties such as crowding and volume occupancy. Analysis of model quality provides estimates of the regularization that will be required when these models are used as starting points for atomic molecular dynamics simulations.
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Affiliation(s)
- Martina Maritan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA. https://twitter.com/MartinaMaritan
| | - Ludovic Autin
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA. https://twitter.com/grinche
| | - Jonathan Karr
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA
| | - David S Goodsell
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037 USA; RCSB Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, the State University of New Jersey, Piscataway, NJ 08854, USA.
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8
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Rigden DJ, Fernández XM. The 2021 Nucleic Acids Research database issue and the online molecular biology database collection. Nucleic Acids Res 2021; 49:D1-D9. [PMID: 33396976 PMCID: PMC7778882 DOI: 10.1093/nar/gkaa1216] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The 2021 Nucleic Acids Research database Issue contains 189 papers spanning a wide range of biological fields and investigation. It includes 89 papers reporting on new databases and 90 covering recent changes to resources previously published in the Issue. A further ten are updates on databases most recently published elsewhere. Seven new databases focus on COVID-19 and SARS-CoV-2 and many others offer resources for studying the virus. Major returning nucleic acid databases include NONCODE, Rfam and RNAcentral. Protein family and domain databases include COG, Pfam, SMART and Panther. Protein structures are covered by RCSB PDB and dispersed proteins by PED and MobiDB. In metabolism and signalling, STRING, KEGG and WikiPathways are featured, along with returning KLIFS and new DKK and KinaseMD, all focused on kinases. IMG/M and IMG/VR update in the microbial and viral genome resources section, while human and model organism genomics resources include Flybase, Ensembl and UCSC Genome Browser. Cancer studies are covered by updates from canSAR and PINA, as well as newcomers CNCdatabase and Oncovar for cancer drivers. Plant comparative genomics is catered for by updates from Gramene and GreenPhylDB. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been substantially updated, revisiting nearly 1000 entries, adding 90 new resources and eliminating 86 obsolete databases, bringing the current total to 1641 databases. It is available at https://www.oxfordjournals.org/nar/database/c/.
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Affiliation(s)
- Daniel J Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
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