IDEAS home Printed from https://ideas.repec.org/a/hin/complx/1232868.html
   My bibliography  Save this article

HSimulator: Hybrid Stochastic/Deterministic Simulation of Biochemical Reaction Networks

Author

Listed:
  • Luca Marchetti
  • Rosario Lombardo
  • Corrado Priami

Abstract

HSimulator is a multithread simulator for mass-action biochemical reaction systems placed in a well-mixed environment. HSimulator provides optimized implementation of a set of widespread state-of-the-art stochastic, deterministic, and hybrid simulation strategies including the first publicly available implementation of the Hybrid Rejection-based Stochastic Simulation Algorithm (HRSSA). HRSSA, the fastest hybrid algorithm to date, allows for an efficient simulation of the models while ensuring the exact simulation of a subset of the reaction network modeling slow reactions. Benchmarks show that HSimulator is often considerably faster than the other considered simulators. The software, running on Java v6.0 or higher, offers a simulation GUI for modeling and visually exploring biological processes and a Javadoc-documented Java library to support the development of custom applications. HSimulator is released under the COSBI Shared Source license agreement (COSBI-SSLA).

Suggested Citation

  • Luca Marchetti & Rosario Lombardo & Corrado Priami, 2017. "HSimulator: Hybrid Stochastic/Deterministic Simulation of Biochemical Reaction Networks," Complexity, Hindawi, vol. 2017, pages 1-12, December.
  • Handle: RePEc:hin:complx:1232868
    DOI: 10.1155/2017/1232868
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2017/1232868.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/8503/2017/1232868.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2017/1232868?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hiroaki Kitano, 2002. "Computational systems biology," Nature, Nature, vol. 420(6912), pages 206-210, November.
    2. Leroy Hood & David Galas, 2003. "The digital code of DNA," Nature, Nature, vol. 421(6921), pages 444-448, January.
    3. Lufen Chang & Michael Karin, 2001. "Mammalian MAP kinase signalling cascades," Nature, Nature, vol. 410(6824), pages 37-40, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Raffaele Pezzilli & Antonio M. Morselli-Labate, 2009. "Alcoholic Pancreatitis: Pathogenesis, Incidence and Treatment with Special Reference to the Associated Pain," IJERPH, MDPI, vol. 6(11), pages 1-20, November.
    2. Kazunari Iwamoto & Yuki Shindo & Koichi Takahashi, 2016. "Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signaling Pathway," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-18, November.
    3. Samuel Bandara & Johannes P Schlöder & Roland Eils & Hans Georg Bock & Tobias Meyer, 2009. "Optimal Experimental Design for Parameter Estimation of a Cell Signaling Model," PLOS Computational Biology, Public Library of Science, vol. 5(11), pages 1-12, November.
    4. Xubin Lu & Hui Jiang & Abdelaziz Adam Idriss Arbab & Bo Wang & Dingding Liu & Ismail Mohamed Abdalla & Tianle Xu & Yujia Sun & Zongping Liu & Zhangping Yang, 2023. "Investigating Genetic Characteristics of Chinese Holstein Cow’s Milk Somatic Cell Score by Genetic Parameter Estimation and Genome-Wide Association," Agriculture, MDPI, vol. 13(2), pages 1-17, January.
    5. Ting Hu & Karoliina Oksanen & Weidong Zhang & Ed Randell & Andrew Furey & Guang Sun & Guangju Zhai, 2018. "An evolutionary learning and network approach to identifying key metabolites for osteoarthritis," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-18, March.
    6. repec:plo:pone00:0062218 is not listed on IDEAS
    7. Joshua Russell-Buckland & Christopher P Barnes & Ilias Tachtsidis, 2019. "A Bayesian framework for the analysis of systems biology models of the brain," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-29, April.
    8. repec:plo:pcbi00:1003471 is not listed on IDEAS
    9. Dana Cohen, 2022. "General Designs Reveal a Purine-Pyrimidine Structural Code in Human DNA," Mathematics, MDPI, vol. 10(15), pages 1-20, August.
    10. repec:plo:pone00:0149263 is not listed on IDEAS
    11. Mark Read & Paul S. Andrews & Jon Timmis & Vipin Kumar, 2011. "Techniques for grounding agent-based simulations in the real domain: a case study in experimental autoimmune encephalomyelitis," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 18(1), pages 67-86, May.
    12. Wenpin Hou & Takeyuki Tamura & Wai-Ki Ching & Tatsuya Akutsu, 2016. "Finding And Analyzing The Minimum Set Of Driver Nodes In Control Of Boolean Networks," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 19(03), pages 1-32, May.
    13. Héctor García Martín & Vinay Satish Kumar & Daniel Weaver & Amit Ghosh & Victor Chubukov & Aindrila Mukhopadhyay & Adam Arkin & Jay D Keasling, 2015. "A Method to Constrain Genome-Scale Models with 13C Labeling Data," PLOS Computational Biology, Public Library of Science, vol. 11(9), pages 1-34, September.
    14. repec:plo:pcbi00:1004131 is not listed on IDEAS
    15. Chandra, Yanto & Wilkinson, Ian F., 2017. "Firm internationalization from a network-centric complex-systems perspective," Journal of World Business, Elsevier, vol. 52(5), pages 691-701.
    16. Jacobo Ayensa-Jiménez & Marina Pérez-Aliacar & Teodora Randelovic & José Antonio Sanz-Herrera & Mohamed H. Doweidar & Manuel Doblaré, 2020. "Analysis of the Parametric Correlation in Mathematical Modeling of In Vitro Glioblastoma Evolution Using Copulas," Mathematics, MDPI, vol. 9(1), pages 1-22, December.
    17. Qing-Ju Jiao & Yan-Kai Zhang & Lu-Ning Li & Hong-Bin Shen, 2011. "BinTree Seeking: A Novel Approach to Mine Both Bi-Sparse and Cohesive Modules in Protein Interaction Networks," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-12, November.
    18. Tom C Freeman & Leon Goldovsky & Markus Brosch & Stijn van Dongen & Pierre Mazière & Russell J Grocock & Shiri Freilich & Janet Thornton & Anton J Enright, 2007. "Construction, Visualisation, and Clustering of Transcription Networks from Microarray Expression Data," PLOS Computational Biology, Public Library of Science, vol. 3(10), pages 1-11, October.
    19. Yuksel Bayraktar & Esme Isik & Ibrahim Isik & Ayfer Ozyilmaz & Metin Toprak & Fatma Kahraman Guloglu & Serdar Aydin, 2022. "Analyzing of Alzheimer’s Disease Based on Biomedical and Socio-Economic Approach Using Molecular Communication, Artificial Neural Network, and Random Forest Models," Sustainability, MDPI, vol. 14(13), pages 1-15, June.
    20. Kansuporn Sriyudthsak & Fumihide Shiraishi & Masami Yokota Hirai, 2013. "Identification of a Metabolic Reaction Network from Time-Series Data of Metabolite Concentrations," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-9, January.
    21. Dan Frumkin & Adam Wasserstrom & Shai Kaplan & Uriel Feige & Ehud Shapiro, 2005. "Genomic Variability within an Organism Exposes Its Cell Lineage Tree," PLOS Computational Biology, Public Library of Science, vol. 1(5), pages 1-13, October.
    22. Julia Gilhodes & Florence Dalenc & Jocelyn Gal & Christophe Zemmour & Eve Leconte & Jean Marie Boher & Thomas Filleron, 2020. "Comparison of Variable Selection Methods for Time-to-Event Data in High-Dimensional Settings," Post-Print hal-02934793, HAL.
    23. repec:plo:pcbi00:0030142 is not listed on IDEAS
    24. Hany A Omar & Wafaa R Mohamed & Hany H Arab & El-Shaimaa A Arafa, 2016. "Tangeretin Alleviates Cisplatin-Induced Acute Hepatic Injury in Rats: Targeting MAPKs and Apoptosis," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-18, March.
    25. Mika Gustafsson & Michael Hörnquist, 2010. "Gene Expression Prediction by Soft Integration and the Elastic Net—Best Performance of the DREAM3 Gene Expression Challenge," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-8, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:1232868. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.