CoChat
Sign Up
Cited by 26Gene Regulatory Network AnalysisSimulation Techniques and Applicationsstochastic dynamics and bifurcation

The DOI matches its Crossref record; the references it cites are not checked yet.

Stochastic simulation algorithms for computational systems biology: Exact, approximate, and hybrid methods

Simoni, Giulia, Reali, Federico, Priami, Corrado, Marchetti, Luca·2019·WIREs Systems Biology and Medicine
DOI 10.1002/wsbm.1459
Open this paper in CoChat — free account

Abstract

Nowadays, mathematical modeling is playing a key role in many different research fields. In the context of system biology, mathematical models and their associated computer simulations constitute essential tools of investigation. Among the others, they provide a way to systematically analyze systems perturbations, develop hypotheses to guide the design of new experimental tests, and ultimately assess the suitability of specific molecules as novel therapeutic targets. To these purposes, stochastic simulation algorithms (SSAs) have been introduced for numerically simulating the time evolution of a well‐stirred chemically reacting system by taking proper account of the randomness inherent in such a system. In this work, we review the main SSAs that have been introduced in the context of exact, approximate, and hybrid stochastic simulation. Specifically, we will introduce the direct method (DM), the first reaction method (FRM), the next reaction method (NRM) and the rejection‐based SSA (RSSA) in the area of exact stochastic simulation. We will then present the τ‐leaping method and the chemical Langevin method in the area of approximate stochastic simulation and an implementation of the hybrid RSSA (HRSSA) in the context of hybrid stochastic‐deterministic simulation. Finally, we will consider the model of the sphingolipid metabolism to provide an example of application of SSA to computational system biology by exemplifying how different simulation strategies may unveil different insights into the investigated biological phenomenon. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Computational Methods

Overview

Integrity signals

Registry

Crossref · 10.1002/wsbm.1459

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • All SSA methods tested generate exact realizations consistent with the underlying Chemical Master Equation.

    “All SSA methods tested generate exact realizations, in the sense that they should each give statistics consistent with the underlying Chemical Master Equation of the model [43], and their simulation times are hence directly comparable.”

    From Catalyst: Fast and flexible modeling of reaction networks (2023) · cites this paper as [43]

Citations

The citation lists haven’t been fetched yet

References 96 · Cited by 26

Research works printed but not linked: 91

  1. 10.11948/2015034 · DOI
  2. Ahn T.‐H. &Sandu A.(2011a). Fully implicit tau‐leaping methods for the stochastic simulation of chemical kinetics. InProceedings of the 19th high performane computing symposia(pp. 118–125) Boston MA.
  3. 10.1016/j.procs.2011.04.250 · DOI
  4. 10.1051/proc:2005001 · DOI
  5. 10.1038/nature03232 · DOI
  6. 10.1063/1.2799998 · DOI
  7. 10.1063/1.2819665 · DOI
  8. 10.1137/15M1024664 · DOI
  9. 10.1063/1.2218339 · DOI
  10. 10.1007/s11538-009-9467-x · DOI
  11. Bentele M. &Eils R.(2005). General stochastic hybrid method for the simulation of chemical reaction processes in cells. InComputational methods in systems biology(pp. 248–251). · DOI
  12. Burden R. L., 2016, Numerical analysis
  13. 10.1002/9780470753767 · DOI
  14. 10.1109/MSP.2007.273051 · DOI
  15. 10.1063/1.2436869 · DOI
  16. 10.1063/1.1992473 · DOI
  17. 10.1063/1.2159468 · DOI
  18. 10.1063/1.2745299 · DOI
  19. 10.1063/1.1778376 · DOI
  20. Cao Y. &Petzold L. R.(2005). Trapezoidal tau‐leaping formula for the stochastic simulation of biochemical systems. InProceedings of foundations of systems biology in engineering (FOSBE 2005)(pp. 149–152) Santa Barbara CA.
  21. 10.1093/bioinformatics/bti308 · DOI
  22. 10.1063/1.1833357 · DOI
  23. 10.1126/science.1070919 · DOI · Paper page
  24. Ethier S. N., 1986, Markov processes: Characterization and, Convergence, 43, 484
  25. 10.1038/nbt1356 · DOI
  26. 10.1021/jp993732q · DOI
  27. 10.1016/0021-9991(76)90041-3 · DOI · Paper page
  28. 10.1021/j100540a008 · DOI
  29. 10.1063/1.481811 · DOI
  30. 10.1063/1.1378322 · DOI
  31. 10.1021/jp0128832 · DOI
  32. 10.1021/jp806431b · DOI
  33. 10.1063/1.1613254 · DOI
  34. 10.1093/bioinformatics/btl465 · DOI
  35. 10.1186/1752-0509-5-26 · DOI
  36. 10.1049/iet-syb:20060035 · DOI
  37. 10.1063/1.2354085 · DOI
  38. 10.1063/1.1505860 · DOI
  39. 10.1063/1.4905196 · DOI
  40. 10.1063/1.4771660 · DOI
  41. 10.1063/1.4711143 · DOI
  42. 10.4236/am.2013.41A036 · DOI
  43. 10.1016/j.ces.2011.05.035 · DOI
  44. 10.1002/psp4.12304 · DOI · Paper page
  45. 10.1016/j.ymgme.2017.12.183 · DOI
  46. 10.1214/12-AAP841 · DOI
  47. 10.1007/BFb0120765 · DOI
  48. 10.1016/0304-4149(78)90020-0 · DOI
  49. Li H., 2006, Logarithmic direct method for discrete stochastic simulation of chemically reacting systems, Journal of Chemical Physics, 16, 1
  50. 10.1063/1.4934972 · DOI
  51. 10.1038/nbt1054 · DOI
  52. 10.1073/pnas.6.7.410 · DOI
  53. 10.1016/j.jcp.2016.04.056 · DOI
  54. 10.1007/978-3-319-63113-4 · DOI
  55. 10.1109/TCBB.2009.47 · DOI
  56. 10.1016/S0168-9525(98)01659-X · DOI
  57. 10.1021/cr2002917 · DOI
  58. 10.1038/s41598-017-00854-w · DOI
  59. 10.1038/s41598-019-40230-4 · DOI
  60. 10.1137/130925657 · DOI
  61. 10.1007/s10543-015-0556-y · DOI
  62. 10.1007/978-3-540-24743-2_31 · DOI
  63. 10.1093/bib/bbn050 · DOI
  64. 10.1007/s10483-007-1009-y · DOI
  65. 10.1038/nature13476 · DOI
  66. Press W. H., 2007, Numerical recipes 3rd edition: The art of scientific computing
  67. 10.1142/p1004 · DOI
  68. 10.1007/b98885 · DOI
  69. 10.1063/1.3154624 · DOI
  70. 10.1063/1.3297948 · DOI
  71. 10.1063/1.1627296 · DOI
  72. 10.1038/srep41231 · DOI
  73. 10.3389/fams.2017.00006 · DOI
  74. 10.1063/1.1835951 · DOI
  75. Sandmann W. (2009). Streamlined formulation of adaptive explicit‐implicit tau‐leaping with automatic tau selection. InProceedings of winter simulation conference(pp. 1104–1112).https://doi.org/10.1109/WSC.2009.5429309 · DOI
  76. 10.1063/1.4928635 · DOI
  77. 10.1098/rsif.2008.0086.focus · DOI
  78. 10.1016/j.jcp.2007.10.021 · DOI
  79. 10.1063/1.2919546 · DOI
  80. 10.1073/pnas.162041399 · DOI · Paper page
  81. 10.1063/1.5016338 · DOI
  82. 10.1063/1.4927916 · DOI
  83. 10.1063/1.4896985 · DOI
  84. Thanh V. H. &Zunino R.(2012). Tree‐based search for stochastic simulation algorithm. InProceedings of the ACM symposium on applied computing(pp. 1415–1416).https://doi.org/10.1145/2245276.2232001 · DOI
  85. 10.1504/IJCBDD.2014.066542 · DOI
  86. 10.1063/1.4922923 · DOI
  87. 10.1109/TCBB.2016.2530066 · DOI
  88. 10.1063/1.4976703 · DOI
  89. 10.1063/1.1810475 · DOI
  90. 10.1038/nrg2509 · DOI
  91. 10.1186/s12918-015-0176-9 · DOI

Metadata

Authors
Simoni, G., Reali, F., Priami, C., & Marchetti, L.
Affiliations
  • The Microsoft Research - University of Trento Centre for Computational and Systems Biology (IT)
  • University of Pisa (IT)
Corresponding author
Marchetti, Luca
ORCID
Venue
WIREs Systems Biology and Medicine
Publisher
Wiley
Published
2019-07
Volume
11
Issue
6
Pages
e1459-e1459
ISSN
  • 1939-005X (ISSN-L)
  • 1939-5094
DOI
10.1002/wsbm.1459
OpenAlex
W2955596421
Cited by
26
References
96
Citations by year
2026: 3 · 2025: 2 · 2024: 3 · 2023: 5 · 2022: 3 · 2021: 6 · 2020: 4
FWCI
0.29
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • WIREs Systems Biology and Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
  • Institutional Research Information System (Università degli Studi di Trento) (submittedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
Keywords
stochastic simulation algorithm, computational systems biology, direct method, tau-leaping, chemical Langevin equation, sphingolipid metabolism, chemical reaction systems
MeSH terms
Algorithms, Animals, Ceramides, Computer Simulation, Humans, Models, Biological, Sphingolipids, Systems Biology

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review