LibSBML: an API Library for SBML

Untitled work

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The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models

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Minimum information requested in the annotation of biochemical models (MIRIAM)

Novère, N. L., Finney, A., Hucka, M., Bhalla, U. S., Campagne, F., Collado-Vides, J., Crampin, E. J., Halstead, M., Klipp, E., Mendes, P., et al. (2005) · Nature Biotechnology · doi:10.1038/nbt1156 · Cited by 622

System Modeling in Cellular Biology: From Concepts to Nuts and Bolts

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Systems Biology Markup Language (SBML) Level 2: Structures and Facilities for Model DefinitionsPreprint

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Adding semantics in kinetics models of biochemical pathways

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Systems Biology Markup Language (SBML) Level 2: Structures and Facilities for Model DefinitionsPreprint

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Software Infrastructure for Effective Communication and Reuse of Computational Models

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Software Infrastructure for Effective Communication and Reuse of Computational Models

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The Reactome pathway Knowledgebase

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The Reactome pathway knowledgebase

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Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0

Schellenberger, J., Que, R., Fleming, R. M. T., Thiele, I., Orth, J. D., Feist, A. M., Zielinski, D. C., Bordbar, A., Lewis, N. E., Rahmanian, S., et al. (2011) · Nature Protocols · doi:10.1038/nprot.2011.308 · Cited by 1,835

COBRApy: COnstraints-Based Reconstruction and Analysis for Python

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Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

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Fast automated reconstruction of genome-scale metabolic models for microbial species and communities

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Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology

Karp, P. D., Paley, S. M., Krummenacker, M., Latendresse, M., Dale, J. M., Lee, T. J., Kaipa, P., Gilham, F., Spaulding, A., Popescu, L., et al. (2010) · Briefings in Bioinformatics · doi:10.1093/bib/bbp043 · Cited by 694

A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

Herrgård, M. J., Swainston, N., Dobson, P., Dunn, W. B., Arga, K. Y., Arvas, M., Blüthgen, N., Borger, S., Costenoble, R., Heinemann, M., et al. (2008) · Nature Biotechnology · doi:10.1038/nbt1492 · Cited by 588

BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models

Li, C., Donizelli, M., Rodríguez, N., Dharuri, H. K., Endler, L., Chelliah, V., Li, L., He, E., Henry, A., Stefan, M. I., et al. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-92 · Cited by 561

MEMOTE for standardized genome-scale metabolic model testing

Lieven, C., Beber, M. E., Olivier, B. G., Bergmann, F. T., Ataman, M., Babaei, P., Bartell, J. A., Blank, L. M., Chauhan, S., Correia, K., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0446-y · Cited by 509

OptFlux: an open-source software platform for in silico metabolic engineering

Rocha, I., Maia, P., Evangelista, P., Vilaça, P., Soares, S., Pinto, J. P., Nielsen, J., Patil, K. R., Ferreira, E. C., & Rocha, M. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-45 · Cited by 390

RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor

Wang, H., Marcišauskas, S., Sánchez, B. J., Domenzain, I., Hermansson, D., Agren, R., Nielsen, J., & Kerkhoven, E. J. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006541 · Cited by 381

Genome-scale models of bacterial metabolism: reconstruction and applications

Durot, M., Bourguignon, P.-Y., & Schächter, V. (2008) · FEMS Microbiology Reviews · doi:10.1111/j.1574-6976.2008.00146.x · Cited by 342

Predicting perturbation patterns from the topology of biological networks

Santolini, M., & Barabási, A.-Ĺ. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1720589115 · Cited by 324

gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

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Controlled vocabularies and semantics in systems biology

Courtot, M., Juty, N., Knüpfer, C., Waltemath, D., Zhukova, A., Dräger, A., Dumontier, M., Finney, A., Golebiewski, M., Hastings, J., et al. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.77 · Cited by 316

Lessons Learned from Quantitative Dynamical Modeling in Systems Biology

Raue, A., Schilling, M., Bachmann, J., Matteson, A., Schelke, M., Kaschek, D., Hug, S., Kreutz, C., Harms, B. D., Theis, F. J., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0074335 · Cited by 316

SBML Level 3: an extensible format for the exchange and reuse of biological models

Keating, S., Waltemath, D., König, M., Zhang, F., Dräger, A., Chaouiya, C., Bergmann, F., Finney, A., Gillespie, C. S., Helikar, T., et al. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199110 · Cited by 301

Pathway Tools version 19.0 update: software for pathway/genome informatics and systems biology

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Reproducible computational biology experiments with SED-ML - The Simulation Experiment Description Markup Language

Waltemath, D., Adams, R. R., Bergmann, F., Hucka, M., Kolpakov, F., Miller, A. K., Moraru, I. I., Nickerson, D. P., Sahle, S., Snoep, J. L., et al. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-198 · Cited by 259

Snoopy – A Unifying Petri Net Tool

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Proceedings of the 2017 Winter Simulation Conference

Chan, W.-M., D’Ambrogio, A., Zacharewicz, G., Mustafee, N., Wainer, G., & Page, E. (2017) · HAL · Cited by 251

Pathway databases and tools for their exploitation: benefits, current limitations and challenges

Bauer‐Mehren, A., Furlong, L. I., & Sanz, F. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.47 · Cited by 192

Path2Models: large-scale generation of computational models from biochemical pathway maps

Büchel, F., Rodríguez, N., Swainston, N., Wrzodek, C., Czauderna, T., Keller, R., Mittag, F., Schubert, M., Glont, M., Golebiewski, M., et al. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-116 · Cited by 173

The Physiome Model Repository 2

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Revealing disease-associated pathways by network integration of untargeted metabolomics

Pirhaji, L., Milani, P., Leidl, M., Curran, T., Avila-Pacheco, J., Clish, C. B., White, F. M., Saghatelian, A., & Fraenkel, E. (2016) · Nature Methods · doi:10.1038/nmeth.3940 · Cited by 167

SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools

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Tellurium: An extensible python-based modeling environment for systems and synthetic biology

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Revising the Representation of Fatty Acid, Glycerolipid, and Glycerophospholipid Metabolism in the Consensus Model of Yeast Metabolism

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libRoadRunner: a high performance SBML simulation and analysis library

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Towards programming languages for genetic engineering of living cells

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MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases

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GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models

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IoT-Lite: a lightweight semantic model for the internet of things and its use with dynamic semantics

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Rule‐based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems

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sybil – Efficient constraint-based modelling in R

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Antimony: a modular model definition language

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iBioSim: a tool for the analysis and design of genetic circuits

Myers, C. J., Barker, N., Jones, K., Kuwahara, H., Madsen, C. K., & Nguyen, N. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp457 · Cited by 113

Further developments towards a genome-scale metabolic model of yeast

Dobson, P. D., Smallbone, K., Jameson, D., Simeonidis, E., Lanthaler, K., Pir, P., Lü, C., Swainston, N., Dunn, W. B., Fisher, P., et al. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-145 · Cited by 113

Genome-scale metabolic reconstructions of Bifidobacterium adolescentis L2-32 and Faecalibacterium prausnitzii A2-165 and their interaction

Elsemman, I. E., Karlsson, F. H., Shoaie, S., Nookaew, I., Soliman, T. H. A., & Nielsen, J. (2014) · BMC Systems Biology · doi:10.1186/1752-0509-8-41 · Cited by 104

Cooperative development of logical modelling standards and tools with CoLoMoTo

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Genome-scale metabolic reconstruction of the symbiosis between a leguminous plant and a nitrogen-fixing bacterium

diCenzo, G. C., Tesi, M., Pfau, T., Mengoni, A., & Fondi, M. (2020) · Nature Communications · doi:10.1038/s41467-020-16484-2 · Cited by 96

Integration of metabolic databases for the reconstruction of genome-scale metabolic networks

Radrich, K., Tsuruoka, Y., Dobson, P. D., Gevorgyan, A., Swainston, N., Baart, G. J. E., & Schwartz, J. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-114 · Cited by 94

Fine‐tuning the P. pastoris iMT1026 genome‐scale metabolic model for improved prediction of growth on methanol or glycerol as sole carbon sources

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SynBioSS: the synthetic biology modeling suite

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Software support for SBGN maps: SBGN-ML and LibSBGN

van Iersel, M., Villéger, A. C., Czauderna, T., Boyd, S. E., Bergmann, F., Luna, A., Demir, E., Sorokin, A., Doğrusöz, U., Matsuoka, Y., et al. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts270 · Cited by 90

GPU accelerated biochemical network simulation

Zhou, Y., Liepe, J., Sheng, X., Stumpf, M. P. H., & Barnes, C. P. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr015 · Cited by 87

StochPy: A Comprehensive, User-Friendly Tool for Simulating Stochastic Biological Processes

Maarleveld, T. R., Olivier, B. G., & Bruggeman, F. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0079345 · Cited by 85

Flux balance analysis: A geometric perspective

Smallbone, K., & Simeonidis, E. (2009) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2009.01.027 · Cited by 81

An overview of the CellML API and its implementation

Miller, A. K., Marsh, J., Reeve, A. M., Garny, A., Britten, R. D., Halstead, M. D. B., Cooper, J., Nickerson, D. P., & Nielsen, P. F. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-178 · Cited by 81

JSBML: a flexible Java library for working with SBML

Dräger, A., Rodriguez, N., Dumousseau, M., Dörr, A., Wrzodek, C., Novère, N. L., Zell, A., & Hucka, M. (2011) · Bioinform. · doi:10.1093/bioinformatics/btr361 · Cited by 82

AMICI: high-performance sensitivity analysis for large ordinary differential equation models

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Harmonizing semantic annotations for computational models in biology

Neal, M. L., König, M., Nickerson, D., Mısırlı, G., Kalbasi, R., Dräger, A., Atalag, K., Chelliah, V., Cooling, M. T., Cook, D. L., et al. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bby087 · Cited by 74

Random sampling of elementary flux modes in large-scale metabolic networks

Machado, D., Soons, Z. I. T. A., Patil, K. R., Ferreira, E. C., & Rocha, I. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts401 · Cited by 74

BioModels: Content, Features, Functionality, and Use

Juty, N., Ali, R., Glont, M., Keating, S., Rodríguez, N., Swat, M. J., Wimalaratne, S., Hermjakob, H., Novère, N. G. L., Laibe, C., et al. (2015) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.3 · Cited by 67

Integrating systems biology models and biomedical ontologies

Hoehndorf, R., Dumontier, M., Gennari, J. H., Wimalaratne, S., de Bono, B., Cook, D. L., & Gkoutos, G. V. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-124 · Cited by 60

Dynamic genome-scale metabolic modeling of the yeast Pichia pastoris

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Modeling and simulation of atrazine biodegradation in bacteria and its effect in other living systems

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Designing and encoding models for synthetic biology

Endler, L., Rodríguez, N., Juty, N., Chelliah, V., Laibe, C., Li, C., & Novère, N. G. L. (2009) · Journal of The Royal Society Interface · doi:10.1098/rsif.2009.0035.focus · Cited by 63

Robustness encoded across essential and accessory replicons of the ecologically versatile bacterium Sinorhizobium meliloti

diCenzo, G. C., Benedict, A. B., Fondi, M., Walker, G. C., Finan, T. M., Mengoni, A., & Griffitts, J. S. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007357 · Cited by 56

Improving Collaboration by Standardization Efforts in Systems Biology

Dräger, A., & Palsson, B. Ã. (2014) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2014.00061 · Cited by 60

AutoBioCAD: Full Biodesign Automation of Genetic Circuits

Rodrigo, G., & Jaramillo, A. (2012) · ACS Synthetic Biology · doi:10.1021/sb300084h · Cited by 56

Large-Scale Metabolic Models: From Reconstruction to Differential Equations

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A Liver-Centric Multiscale Modeling Framework for Xenobiotics

Sluka, J. P., Fu, X., Swat, M., Belmonte, J. M., Cosmanescu, A., Clendenon, S. G., Wambaugh, J. F., & Glazier, J. A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162428 · Cited by 56

FBA reveals guanylate kinase as a potential target for antiviral therapies against SARS-CoV-2

Renz, A., Widerspick, L., & Dräger, A. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa813 · Cited by 56

Memote: A community driven effort towards a standardized genome-scale metabolic model test suitePreprint

Lieven, C., Beber, M. E., Olivier, B. G., Bergmann, F., Ataman, M., Babaei, P., Bartell, J. A., Blank, L. M., Chauhan, S., Correia, K., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/350991 · Cited by 56

In Silico Modeling of Shear-Stress-Induced Nitric Oxide Production in Endothelial Cells through Systems Biology

Koo, A., Nordsletten, D., Umeton, R., Yankama, B., Ayyadurai, S., García‐Cardeña, G., & Dewey, C. F. (2013) · Biophysical Journal · doi:10.1016/j.bpj.2013.03.052 · Cited by 48

von Bertalanffy 1.0: a COBRA toolbox extension to thermodynamically constrain metabolic models

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Defining the nutritional input for genome-scale metabolic models: A roadmap

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Multiscale Metabolic Modeling of C4 Plants: Connecting Nonlinear Genome-Scale Models to Leaf-Scale Metabolism in Developing Maize Leaves

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Metabolic Cancer Biology: Structural-based analysis of cancer as a metabolic disease, new sights and opportunities for disease treatment

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Intrinsic Noise Analyzer: A Software Package for the Exploration of Stochastic Biochemical Kinetics Using the System Size Expansion

Thomas, P., Matuschek, H., & Grima, R. (2012) · PLoS ONE · doi:10.1371/journal.pone.0038518 · Cited by 48

libRoadRunner 2.0: a high performance SBML simulation and analysis library

Welsh, C., Xu, J. H., Smith, L. P., König, M., Choi, K., & Sauro, H. M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac770 · Cited by 45

Integration of a constraint-based metabolic model of Brassica napus developing seeds with 13C-metabolic flux analysis

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PSAMM: A Portable System for the Analysis of Metabolic Models

Steffensen, J. L., Dufault‐Thompson, K., & Zhang, Y. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004732 · Cited by 40

COBRA Toolbox 2.0

Hyduke, D. R., Hyduke, D. R., Schellenberger, J., Que, R. A., Fleming, R. M. T., Thiele, I., Orth, J., Feist, A. M., Zielinski, D. C., Bordbar, A., et al. (2011) · Protocol Exchange · doi:10.1038/protex.2011.234 · Cited by 45

MitoCore: a curated constraint-based model for simulating human central metabolism

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A genome‐scale metabolic reconstruction of soybean and Bradyrhizobium diazoefficiens reveals the cost–benefit of nitrogen fixation

Holland, B. L., Matthews, M. L., Bota, P., Sweetlove, L. J., Long, S. P., & diCenzo, G. C. (2023) · New Phytologist · doi:10.1111/nph.19203 · Cited by 40

An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models

Chindelevitch, L., Trigg, J., Regev, A., & Berger, B. (2014) · Nature Communications · doi:10.1038/ncomms5893 · Cited by 40

MEMOSys: Bioinformatics platform for genome-scale metabolic models

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Arcadia: a visualization tool for metabolic pathways

Villéger, A. C., Pettifer, S. R., & Kell, D. B. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq154 · Cited by 43

KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database

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Autonomous biomedical research with an artificial intelligence agent

Huang, K., Zhang, S., Wang, H., Qu, Y., Lu, Y., Li, R., Roohani, Y., Qiu, L., Cao, S., Li, G. L., et al. (2026) · Science · doi:10.1126/science.adz4351 · Cited by 289

The challenges of informatics in synthetic biology: from biomolecular networks to artificial organisms

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