The Systems Biology Markup Language (SBML): language specification for level 3 version 2 core release 2

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A rigorous derivation of the chemical master equation

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Stochastic Modelling for Systems Biology

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

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Metabolic control theory: A structural approach

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Genome‐scale models of metabolism and gene expression extend and refine growth phenotype prediction

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Introductory Graph Theory.

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Controlled vocabularies and semantics in systems biology

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CODATA Recommended Values of the Fundamental Physical Constants: 2018

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UML toolkit

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XML In A Nutshell

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Identifiers.org and MIRIAM Registry: community resources to provide persistent identification

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Asymptotic analysis of multiscale approximations to reaction networksPreprint

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Conservation analysis in biochemical networks: computational issues for software writers.

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Developing Software With Uml: Object-Oriented Analysis and Design in Practice

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Guidelines for implementing Dublin Core in XML

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Introductory graph theory

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Systems Biology Markup Language (SBML)

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Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions

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The SBML discrete stochastic models test suite

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The International System of Units (SI), 8th edition

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Date and Time Formats

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Standard mathematical tables and formulae

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Systems Biology Markup Language (SBML) Level 1: Structures and Facilities for Basic Model Definitions

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Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition) ¹

Klionsky, D. J., Abdel-Aziz, A. K., Abdelfatah, S., Abdellatif, M., Abdoli, A., Abel, S., Abeliovich, H., Abildgaard, M. H., Abudu, Y. P., Acevedo-Arozena, A., et al. (2021) · Autophagy · doi:10.1080/15548627.2020.1797280 · Cited by 2,622 · see citing works

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

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

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

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BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree

Norsigian, C. J., Pusarla, N., McConn, J. L., Yurkovich, J. T., Dräger, A., Palsson, B. Ø., & King, Z. A. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1054 · Cited by 250 · see citing works

A systematic assessment of current genome-scale metabolic reconstruction tools

Mendoza, S. N., Olivier, B. G., Molenaar, D., & Teusink, B. (2019) · Genome biology · doi:10.1186/s13059-019-1769-1 · Cited by 230 · see citing works

Modeling formalisms in Systems Biology

Machado, D., Costa, R. S., Rocha, M., Ferreira, E. C., Tidor, B., & Rocha, I. (2011) · AMB Express · doi:10.1186/2191-0855-1-45 · Cited by 189 · see citing works

Reproducibility in systems biology modelling

Tiwari, K., Kananathan, S., Roberts, M. G., Meyer, J. P., Sharif Shohan, M. U., Xavier, A., Maire, M., Zyoud, A., Men, J., Ng, S., et al. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209982 · Cited by 131 · see citing works

COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms

Ostaszewski, M., Mazein, A., Gillespie, M. E., Kuperstein, I., Niarakis, A., Hermjakob, H., Pico, A. R., Willighagen, E., Evelo, C. T., Hasenauer, J., et al. (2020) · Scientific Data · doi:10.1038/s41597-020-0477-8 · Cited by 135 · see citing works

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

Bermúdez-Edo, M., Elsaleh, T., Barnaghi, P. M., & Taylor, K. L. (2017) · Personal and Ubiquitous Computing · doi:10.1007/s00779-017-1010-8 · Cited by 132 · see citing works

Credible practice of modeling and simulation in healthcare: ten rules from a multidisciplinary perspective

Erdemir, A., Mulugeta, L., Ku, J. P., Drach, A., Horner, M., Morrison, T. M., Peng, G. C. Y., Vadigepalli, R., Lytton, W. W., & Myers, J. G. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02540-4 · Cited by 118 · see citing works

Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation

Collin, C. B., Gebhardt, T., Golebiewski, M., Karaderi, T., Hillemanns, M., Khan, F. M., Salehzadeh‐Yazdi, A., Kirschner, M. W., Krobitsch, S., & Kuepfer, L. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020166 · Cited by 122 · see citing works

Modelling Multi-Pulse Population Dynamics from Ultrafast Spectroscopy

van Wilderen, L. J. G. W., Lincoln, C. N., & van Thor, J. J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017373 · Cited by 120 · see citing works

Metage2Metabo, microbiota-scale metabolic complementarity for the identification of key species

Belcour, A., Frioux, C., Aïte, M., Bretaudeau, A., Hildebrand, F., & Siegel, A. (2020) · eLife · doi:10.7554/elife.61968 · Cited by 99 · see citing works

BioModels: expanding horizons to include more modelling approaches and formats

Glont, M., Nguyen, T. V. N., Graesslin, M., Hälke, R., Ali, R., Schramm, J., Wimalaratne, S. M., Kothamachu, V. B., Rodriguez, N., Swat, M. J., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1023 · Cited by 89 · see citing works

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 · see citing works

Recent advances on constraint-based models by integrating machine learning

Rana, P., Berry, C., Ghosh, P., & Fong, S. S. (2019) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2019.11.007 · Cited by 69 · see citing works

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

Gong, Z., Chen, J., Jiao, X., Gong, H., Pan, D., Liu, L., Zhang, Y., & Tan, T. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108319 · Cited by 63 · see citing works

Precise generation of systems biology models from KEGG pathways

Wrzodek, C., Büchel, F., Ruff, M., Dräger, A., & Zell, A. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-15 · Cited by 64 · see citing works

Periodic propagating waves coordinate RhoGTPase network dynamics at the leading and trailing edges during cell migration

Bolado‐Carrancio, A., Rukhlenko, O. S., Nikonova, E. V., Tsyganov, M. A., Wheeler, A. P., García-Muñoz, A., Kölch, W., von Kriegsheim, A., & Kholodenko, B. Ν. (2020) · eLife · doi:10.7554/elife.58165 · Cited by 60 · see citing works

Toward Community Standards and Software for Whole-Cell Modeling

Waltemath, D., Karr, J. R., Bergmann, F., Chelliah, V., Hucka, M., Krantz, M., Liebermeister, W., Mendes, P., Myers, C. J., Pir, P., et al. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2560762 · Cited by 57 · see citing works

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 · see citing works

merlin , an improved framework for the reconstruction of high-quality genome-scale metabolic models

Capela, J., Lagoa, D. R., Rodrigues, R., Cunha, E., Cruz, F., Barbosa, A., Bastos, J. R. H., Lima, D., Ferreira, E. C., Rocha, M., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac459 · Cited by 54 · see citing works

Combining computational models, semantic annotations and simulation experiments in a graph database

Henkel, R., Wolkenhauer, O., & Waltemath, D. (2015) · Database J. Biol. Databases Curation · doi:10.1093/database/bau130 · Cited by 52 · see citing works

Hepatectomy-Induced Alterations in Hepatic Perfusion and Function - Toward Multi-Scale Computational Modeling for a Better Prediction of Post-hepatectomy Liver Function

Christ, B., Collatz, M., Dahmen, U., Herrmann, K., Höpfl, S., König, M., Lambers, L., Marz, M., Meyer, D., Radde, N. E., et al. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.733868 · Cited by 46 · see citing works

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 44 · see citing works

A pathway model of glucose-stimulated insulin secretion in the pancreatic β-cell

Maheshvare, M. D., Raha, S., König, M., & Pal, D. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1185656 · Cited by 43 · see citing works

Fluxer: a web application to compute, analyze and visualize genome-scale metabolic flux networks

Hari, A., & Lobo, D. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa409 · Cited by 41 · see citing works

SBML Level 3 package: Hierarchical Model Composition, Version 1 Release 3

Smith, L. P., Hucka, M., Hoops, S., Finney, A., Ginkel, M., Myers, C. J., Moraru, I. I., & Liebermeister, W. (2015) · PubMed · doi:10.2390/biecoll-jib-2015-268 · Cited by 41 · see citing works

Closing the gap between formats for storing layout information in systems biology

Hoksza, D., Gawron, P., Ostaszewski, M., Hasenauer, J., & Schneider, R. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz067 · Cited by 32 · see citing works

MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics

Haiman, Z. B., Zielinski, D. C., Koike, Y., Yurkovich, J. T., & Palsson, B. Ø. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008208 · Cited by 39 · see citing works

The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE)

Waltemath, D., Golebiewski, M., Blinov, M. L., Gleeson, P., Hermjakob, H., Hucka, M., Inau, E. T., Keating, S., König, M., Krebs, O., et al. (2020) · Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics · doi:10.1515/jib-2020-0005 · Cited by 38 · see citing works

Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms

Suthers, P. F., Foster, C., Sarkar, D., Wang, L., & Maranas, C. D. (2020) · Metabolic Engineering · doi:10.1016/j.ymben.2020.11.013 · Cited by 38 · see citing works

Genome-Scale Metabolic Model of Infection with SARS-CoV-2 Mutants Confirms Guanylate Kinase as Robust Potential Antiviral Target

Renz, A., Widerspick, L., & Dräger, A. (2021) · Genes · doi:10.3390/genes12060796 · Cited by 38 · see citing works

Modeling Meets Metabolomics—The WormJam Consensus Model as Basis for Metabolic Studies in the Model Organism Caenorhabditis elegans

Witting, M. A., Hastings, J., Rodríguez, N., Joshi, C. J., Hattwell, J. P. N., Ebert, P. R., van Weeghel, M., Gao, A. W., Wakelam, M. J. O., Houtkooper, R. H., et al. (2018) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2018.00096 · Cited by 36 · see citing works

Multi-Level Kinetic Model of mRNA Delivery via Transfection of Lipoplexes

Ligon, T. S., Leonhardt, C., & Rädler, J. O. (2014) · PLoS ONE · doi:10.1371/journal.pone.0107148 · Cited by 35 · see citing works

Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools

Kutumova, E. O., Akberdin, I. R., Kiselev, I. N., Sharipov, R., Egorova, V. S., Syrocheva, A. O., Parodi, A., Zamyatnin, A. A., & Kolpakov, F. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232012560 · Cited by 38 · see citing works

A Methodology to Annotate Systems Biology Markup Language Models with the Synthetic Biology Open Language

Roehner, N., & Myers, C. J. (2013) · ACS Synthetic Biology · doi:10.1021/sb400066m · Cited by 32 · see citing works

Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis

Jenior, M. L., Leslie, J. L., Powers, D. A., Garrett, E. M., Walker, K. A., Dickenson, M. E., Petri, W. A., Tamayo, R., & Papin, J. A. (2021) · mSystems · doi:10.1128/msystems.00919-21 · Cited by 30 · see citing works

Modular assembly of dynamic models in systems biology

Pan, M., Gawthrop, P. J., Cursons, J., & Crampin, E. J. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009513 · Cited by 32 · see citing works

Community‐scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Cerk, K., Ugalde‐Salas, P., Nedjad, C. G., Lecomte, M., Muller, C., Sherman, D. J., Hildebrand, F., Labarthe, S., & Frioux, C. (2024) · Microbial Biotechnology · doi:10.1111/1751-7915.14396 · Cited by 29 · see citing works

Boolean dynamic modeling of cancer signaling networks: Prognosis, progression, and therapeutics

Sherekar, S., & Viswanathan, G. A. (2021) · Computational and Systems Oncology · doi:10.1002/cso2.1017 · Cited by 29 · see citing works

The systems biology simulation core algorithm

Keller, R., Dörr, A., Tabira, A., Funahashi, A., Ziller, M. J., Adams, R., Rodriguez, N., Novère, N. L., Hiroi, N., Planatscher, H., et al. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-55 · Cited by 34 · see citing works

A Validated Whole-Body PBPK Model of Dextromethorphan and Its Metabolites for Genotype-Based Prediction of CYP2D6 Phenotype and Urinary Metabolic Ratio

Musa, K., Noor, O. N. A., Ibrahim, M., & Saleh, A. (2025) · Specialty journal of Pharmacognosy Phytochemistry and Biotechnology · doi:10.51847/xbesbjhhcx · Cited by 29 · see citing works

Rule-based modeling with Virtual Cell

Schaff, J. C., Vasilescu, D. D., Moraru, I. I., Loew, L. M., & Blinov, M. L. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw353 · Cited by 29 · see citing works

Modelling pyruvate dehydrogenase under hypoxia and its role in cancer metabolism

Eyassu, F., & Angione, C. (2017) · Royal Society Open Science · doi:10.1098/rsos.170360 · Cited by 28 · see citing works

Genome-scale metabolic modelling enables deciphering ethanol metabolism via the acrylate pathway in the propionate-producer Anaerotignum neopropionicum

Benito-Vaquerizo, S., Olm, I. P., de Vroet, T., Schaap, P. J., Sousa, D. Z., dos Santos, V. A. P. M., & Diez, M. S. (2022) · Microbial Cell Factories · doi:10.1186/s12934-022-01841-1 · Cited by 27 · see citing works

Biological computation and computational biology: survey, challenges, and discussion

Dagdia, Z. C., Avdeyev, P., & Bayzid, M. S. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-020-09951-1 · Cited by 28 · see citing works

SBMLsqueezer 2: context-sensitive creation of kinetic equations in biochemical networks

Dräger, A., Zielinski, D. C., Keller, R., Rall, M., Eichner, J., Palsson, B. Ø., & Zell, A. (2015) · BMC Systems Biology · doi:10.1186/s12918-015-0212-9 · Cited by 28 · see citing works

High-Quality Genome-Scale Reconstruction of Corynebacterium glutamicum ATCC 13032

Feierabend, M., Renz, A., Zelle, E., Nöh, K., Wiechert, W., & Dräger, A. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.750206 · Cited by 27 · see citing works

FSK-Lab – An open source food safety model integration tool

Aparicio, M. D. A., Buschhardt, T., Swaid, A., Valentin, L., Mesa-Varona, O., Günther, T., Plaza‐Rodríguez, C., & Filter, M. (2018) · Microbial Risk Analysis · doi:10.1016/j.mran.2018.09.001 · Cited by 26 · see citing works

Genome-scale metabolic modeling of P. thermoglucosidasius NCIMB 11955 reveals metabolic bottlenecks in anaerobic metabolism

Mol, V., Bennett, M., Sánchez, B. J., Lisowska, B. K., Herrgård, M. J., Nielsen, A. T., Leak, D. J., & Sonnenschein, N. (2021) · Metabolic Engineering · doi:10.1016/j.ymben.2021.03.002 · Cited by 26 · see citing works

Adapting modeling and simulation credibility standards to computational systems biology

Tatka, L. T., Smith, L. P., Hellerstein, J. L., & Sauro, H. M. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04290-5 · Cited by 25 · see citing works

Computational models in systems biology: standards, dissemination, and best practices

Vieira, L. S., & Laubenbacher, R. (2022) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2022.102702 · Cited by 25 · see citing works

Metabolomics, Standards, and Metabolic Modeling for Synthetic Biology in Plants

Hill, C. B., Czauderna, T., Klapperstück, M., Roessner, U., & Schreiber, F. (2015) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2015.00167 · Cited by 25 · see citing works

Neural network with multiple connection weights

Zhang, J., Hu, J., & Liu, J. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107481 · Cited by 25 · see citing works

Inhibiting the reproduction of SARS-CoV-2 through perturbations in human lung cell metabolic network

Delattre, H., Sasidharan, K., & Soyer, O. S. (2020) · Life Science Alliance · doi:10.26508/lsa.202000869 · Cited by 24 · see citing works

Plant Synthetic Biology: Quantifying the “Known Unknowns” and Discovering the “Unknown Unknowns”

Wright, R. C., & Nemhauser, J. L. (2019) · PLANT PHYSIOLOGY · doi:10.1104/pp.18.01222 · Cited by 24 · see citing works

Improving the reuse of computational models through version control

Waltemath, D., Henkel, R., Hälke, R., Scharm, M., & Olaf Wolkenhauer, A. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt018 · Cited by 23 · see citing works

Leveraging Structured Biological Knowledge for Counterfactual Inference: A Case Study of Viral Pathogenesis

Zucker, J., Paneri, K., Mohammad-Taheri, S., Bhargava, S., Kolambkar, P., Bakker, C., Teuton, J., Hoyt, C. T., Oxford, K. L., Ness, R. O., et al. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2021.3050680 · Cited by 23 · see citing works

An updated genome-scale metabolic network reconstruction of Pseudomonas aeruginosa PA14 to characterize mucin-driven shifts in bacterial metabolism

Payne, D. D., Renz, A., Dunphy, L. J., Lewis, T., Dräger, A., & Papin, J. (2021) · bioRxiv · doi:10.1038/s41540-021-00198-2 · Cited by 22 · see citing works

Physiologically based pharmacokinetic (PBPK) modeling of the role of CYP2D6 polymorphism for metabolic phenotyping with dextromethorphan

Grzegorzewski, J., Brandhorst, J., & König, M. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.1029073 · Cited by 22 · see citing works

Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions

Anderson, L. N., Hoyt, C. T., Zucker, J., McNaughton, A. D., Teuton, J., Karis, K., Arokium-Christian, N. N., Warley, J. T., Stromberg, Z. R., Gyori, B. M., et al. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1502484 · Cited by 23 · see citing works

Visualizing metabolic network dynamics through time-series metabolomic data

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Towards standardization guidelines for in silico approaches in personalized medicine

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