The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core Release 2

Exact stochastic simulation of coupled chemical reactions

Gillespie, D. T. (1977) · The Journal of Physical Chemistry · doi:10.1021/j100540a008 · Cited by 10,215

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

Hucka, M., Finney, A., Sauro, H. M., Bolouri, H., Doyle, J. C., Kitano, H., Arkin, A. P., Bornstein, B. J., Bray, D., Cornish-Bowden, A., et al. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg015 · Cited by 3,060

An $n^{5/2} $ Algorithm for Maximum Matchings in Bipartite Graphs

Hopcroft, J. E., & Karp, R. M. (1973) · SIAM Journal on Computing · doi:10.1137/0202019 · Cited by 2,799

CODATA recommended values of the fundamental physical constants: 2010

Mohr, P. J., Taylor, B. N., & Newell, D. B. (2012) · Reviews of Modern Physics · doi:10.1103/revmodphys.84.1527 · Cited by 2,476

A rigorous derivation of the chemical master equation

Gillespie, D. T. (1992) · Physica A Statistical Mechanics and its Applications · doi:10.1016/0378-4371(92)90283-v · Cited by 1,079

Stochastic Modelling for Systems Biology

Wilkinson, D. (2006) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420010664 · Cited by 1,041

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

Metabolic control theory: A structural approach

Reder, C. (1988) · Journal of Theoretical Biology · doi:10.1016/s0022-5193(88)80073-0 · Cited by 551

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

O'Brien, E. J., Lerman, J. A., Chang, R. L., Hyduke, D. R., & Palsson, B. Ø. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.52 · Cited by 471

Introductory Graph Theory.

Catlin, P. A., & Chartrand, G. (1987) · American Mathematical Monthly · doi:10.2307/2322751 · Cited by 393

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 2,897

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

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

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

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 263

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 245

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

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

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

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

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

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 128

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

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

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

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

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

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 64

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 60

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 56

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

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

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

Capela, J., Lagoa, D., 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

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

Henkel, R., Wolkenhauer, O., & Waltemath, D. (2015) · Database · doi:10.1093/database/bau130 · Cited by 50

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 47

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

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 56

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 43

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

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 38

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 43

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

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 39

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

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

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 36

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

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

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

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 34

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 39

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 31

The systems biology simulation core algorithm

Keller, R., Dörr, A., Tabira, A., Funahashi, A., Ziller, M. J., Adams, R. R., Rodríguez, N., Novère, N. G. L., Hiroi, N. F., Planatscher, H., et al. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-55 · Cited by 31

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

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

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 30

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

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 33

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 29

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

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

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 25

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 27

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

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

Neural network with multiple connection weights

Zhang, J., Hu, J., & Liu, J. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107481 · Cited by 26

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

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

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

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

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

Payne, D., Renz, A., Dunphy, L. J., Lewis, T., Dräger, A., & Papin, J. A. (2021) · npj Systems Biology and Applications · doi:10.1038/s41540-021-00198-2 · Cited by 23

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

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 26

Visualizing metabolic network dynamics through time-series metabolomic data

Buchweitz, L. F., Yurkovich, J. T., Blessing, C., Kohler, V., Schwarzkopf, F., King, Z. A., Yang, L., Jóhannsson, F., Sigurjónsson, Ó. E., Rolfsson, Ó., et al. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3415-z · Cited by 22

Towards standardization guidelines for in silico approaches in personalized medicine

Brunak, S., Collin, C. B., Cathaoir, K. Ó., Golebiewski, M., Kirschner, M. W., Kockum, I. S., Moser, H., & Waltemath, D. (2020) · Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics · doi:10.1515/jib-2020-0006 · Cited by 22

Respectful Modeling: Addressing Uncertainty in Dynamic System Models for Molecular Biology

Tsigkinopoulou, A., Baker, S. M., & Rainer Breitling, A. (2017) · Trends in biotechnology · doi:10.1016/j.tibtech.2016.12.008 · Cited by 22

Microbiome modeling: a beginner's guide

Lange, E., Kranert, L., Krüger, J., Benndorf, D., & Heyer, R. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1368377 · Cited by 21

A Converter from the Systems Biology Markup Language to the Synthetic Biology Open Language.

Nguyen, T. T., Roehner, N., Zundel, Z., & Myers, C. (2016) · ACS Synthetic Biology · doi:10.1021/acssynbio.5b00212 · Cited by 23

Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus

Renz, A., & Dräger, A. (2021) · npj Systems Biology and Applications · doi:10.1038/s41540-021-00188-4 · Cited by 21

Prediction of Survival After Partial Hepatectomy Using a Physiologically Based Pharmacokinetic Model of Indocyanine Green Liver Function Tests

Köller, A., Grzegorzewski, J., Tautenhahn, H., & König, M. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.730418 · Cited by 21

Multi-scale models of whole cells: progress and challenges

Georgouli, K., Yeom, J.-S., Blake, R. C., & Navid, A. (2023) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2023.1260507 · Cited by 20

Human intestinal lipid storage through sequential meals reveals faster dinner appearance is associated with hyperlipidemia

Jácome-Sosa, M., Hu, Q., Manrique‐Acevedo, C., Phair, R. D., & Parks, E. J. (2021) · JCI Insight · doi:10.1172/jci.insight.148378 · Cited by 22

BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language

Hoyt, C. T., Domingo‐Fernándéz, D., & Hofmann‐Apitius, M. (2018) · Database · doi:10.1093/database/bay126 · Cited by 22

SBML2Modelica: integrating biochemical models within open-standard simulation ecosystems

Maggioli, F., Mancini, T., & Tronci, E. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz860 · Cited by 21

The role of metadata in reproducible computational research

Leipzig, J., Nüst, D., Hoyt, C. T., Soiland-Reyes, S., Ram, K., & Greenberg, J. (2020) · Patterns · doi:10.1016/j.patter.2021.100322 · Cited by 129

Pickaxe: a Python library for the prediction of novel metabolic reactions

Shebek, K. M., Strutz, J., Broadbelt, L. J., & Tyo, K. E. J. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05149-8 · Cited by 19

Delay-differential SEIR modeling for improved modelling of infection dynamics

Kiselev, I. N., Akberdin, I. R., & Kolpakov, F. (2023) · Scientific Reports · doi:10.1038/s41598-023-40008-9 · Cited by 33

A biochemical network modeling of a whole-cell

Burke, P. E. P., Campos, C. B. D. L., Costa, L. D. F., & Quiles, M. G. (2020) · Scientific Reports · doi:10.1038/s41598-020-70145-4 · Cited by 18

Ubiquity: a framework for physiological/mechanism-based pharmacokinetic/pharmacodynamic model development and deployment

Harrold, J. M., & Abraham, A. K. (2014) · Journal of Pharmacokinetics and Pharmacodynamics · doi:10.1007/s10928-014-9352-6 · Cited by 18

A Modular Mathematical Model of Exercise-Induced Changes in Metabolism, Signaling, and Gene Expression in Human Skeletal Muscle

Akberdin, I. R., Kiselev, I. N., Pintus, S. S., Sharipov, R., Vertyshev, A. Y., Виноградова, О. Л., Popov, D. V., & Kolpakov, F. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms221910353 · Cited by 19

MIMO: an efficient tool for molecular interaction maps overlap

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Thoroughly Calibrated Modular Agent-Based Model of the Human Cardiovascular and Renal Systems for Blood Pressure Regulation in Health and Disease

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