Skip to content

ACKNOWLEDGMENTS.md

Latest commit

 

History

History
207 lines (185 loc) · 11.5 KB

ACKNOWLEDGMENTS.md

File metadata and controls

207 lines (185 loc) · 11.5 KB
Raw

Acknowledgments

BNGsim was developed and validated using a large body of excellent open-source software from the systems-biology and scientific-Python communities. Most of these tools are not redistributed with BNGsim. They were used as reference implementations, parity oracles, library dependencies, data corpora, or standards. We gratefully acknowledge their authors and cite their work below.

Third-party software and data that BNGsim redistributes (vendored code and model/test-data corpora, with their license terms) are listed separately in NOTICE.

Reference simulators and cross-validation oracles

BNGsim's numerical results were cross-validated against these simulation engines:

  • libRoadRunner — the primary ODE and SSA parity oracle.
    • Somogyi ET, Bouteiller J-M, Glazier JA, König M, Medley JK, Swat MH, Sauro HM. libRoadRunner: a high performance SBML simulation and analysis library. Bioinformatics. 2015;31(20):3315–3321. doi:10.1093/bioinformatics/btv363
    • Welsh C, Xu J, Smith L, König M, Choi K, Sauro HM. libRoadRunner 2.0: a high-performance SBML simulation and analysis library. Bioinformatics. 2023;39(1):btac770. doi:10.1093/bioinformatics/btac770
  • AMICI — the reference for BNGsim's forward-sensitivity and gradient work.
    • Fröhlich F, Weindl D, Schälte Y, Pathirana D, Paszkowski Ł, Lines GT, Stapor P, Hasenauer J. AMICI: high-performance sensitivity analysis for large ordinary differential equation models. Bioinformatics. 2021;37(20):3676–3677. doi:10.1093/bioinformatics/btab227
  • COPASI — an independent third oracle (variable-volume, rate-rule, and stochastic cross-checks).
    • Hoops S, Sahle S, Gauges R, Lee C, Pahle J, Simus N, Singhal M, Xu L, Mendes P, Kummer U. COPASI—a COmplex PAthway SImulator. Bioinformatics. 2006;22(24):3067–3074. doi:10.1093/bioinformatics/btl485
  • BioNetGen (BNG2.pl, run_network, Network3) — used to generate reaction networks and reference trajectories, and as a rule-based ODE/SSA oracle.
    • Harris LA, Hogg JS, Tapia J-J, Sekar JAP, Gupta S, Korsunsky I, Arora A, Barua D, Sheehan RP, Faeder JR. BioNetGen 2.2: advances in rule-based modeling. Bioinformatics. 2016;32(21):3366–3368. doi:10.1093/bioinformatics/btw469
    • Blinov ML, Faeder JR, Goldstein B, Hlavacek WS. BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics. 2004;20(17):3289–3291. doi:10.1093/bioinformatics/bth378
  • NFsim — the network-free stochastic oracle (also vendored into BNGsim; see NOTICE).
    • Sneddon MW, Faeder JR, Emonet T. Efficient modeling, simulation and coarse-graining of biological complexity with NFsim. Nat Methods. 2011;8(2):177–183. doi:10.1038/nmeth.1546

Vendored simulation engines

BNGsim embeds these rule-based engines (license terms in NOTICE):

  • NFsim — network-free simulation (cited above).
  • RuleMonkey — the canonical rule/species-graph library and stochastic simulator. BNGsim vendors the current C++ version; the original simulator is described in:
    • Colvin J, Monine MI, Gutenkunst RN, Hlavacek WS, Von Hoff DD, Posner RG. RuleMonkey: software for stochastic simulation of rule-based models. BMC Bioinformatics. 2010;11:404. doi:10.1186/1471-2105-11-404

Model-description standards and formats

BNGsim reads and/or writes these community standards:

  • SBML (Systems Biology Markup Language) — core model interchange format.
    • Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, et al. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics. 2003;19(4):524–531. doi:10.1093/bioinformatics/btg015
    • Keating SM, Waltemath D, König M, et al. SBML Level 3: an extensible format for the exchange and reuse of biological models. Mol Syst Biol. 2020;16(8):e9110. doi:10.15252/msb.20199110
  • SED-ML (Simulation Experiment Description Markup Language) — read/write of simulation descriptions; also the format of the SED-ML protocol files BNGsim consumes from BioModels (via the sys-bio/temp-biomodels mirror).
    • Waltemath D, Adams R, Bergmann FT, Hucka M, Kolpakov F, Miller AK, Moraru II, Nickerson D, Sahle S, Snoep JL, Le Novère N. Reproducible computational biology experiments with SED-ML — the Simulation Experiment Description Markup Language. BMC Syst Biol. 2011;5:198. doi:10.1186/1752-0509-5-198
  • COMBINE archive / OMEX — pack/unpack of SBML + SED-ML + manifest.
    • Bergmann FT, Adams R, Moodie S, Cooper J, Glont M, Golebiewski M, Hucka M, Laibe C, Miller AK, Nickerson DP, Olivier BG, Rodriguez N, Sauro HM, Scharm M, Soiland-Reyes S, Waltemath D, Yvon F, Le Novère N. COMBINE archive and OMEX format: one file to share all information to reproduce a modeling project. BMC Bioinformatics. 2014;15:369. doi:10.1186/s12859-014-0369-z
  • Antimony — read/write via the Antimony library.
    • Smith LP, Bergmann FT, Chandran D, Sauro HM. Antimony: a modular model definition language. Bioinformatics. 2009;25(18):2452–2454. doi:10.1093/bioinformatics/btp401
  • BNGL and the BNG .net format (BioNetGen, cited above), Content MathML, and KiSAO (the Kinetic Simulation Algorithm Ontology, used to tag SED-ML algorithms) are also handled.

Numerical and infrastructure libraries

  • SUNDIALS — the CVODES and KINSOL solvers underpin BNGsim's ODE integration, forward sensitivities, and steady-state solves (bundled in binary wheels).
    • Hindmarsh AC, Brown PN, Grant KE, Lee SL, Serban R, Shumaker DE, Woodward CS. SUNDIALS: Suite of nonlinear and differential/algebraic equation solvers. ACM Trans Math Softw. 2005;31(3):363–396. doi:10.1145/1089014.1089020
  • SuiteSparse / KLU — sparse direct solver for the sparse-Jacobian path.
    • Davis TA, Palamadai Natarajan E. Algorithm 907: KLU, a direct sparse solver for circuit simulation problems. ACM Trans Math Softw. 2010;37(3):Article 36. doi:10.1145/1824801.1824814
  • LAPACK — dense linear algebra used by the numerical backends.
    • Anderson E, Bai Z, Bischof C, et al. LAPACK Users' Guide. 3rd ed. Philadelphia: SIAM; 1999. doi:10.1137/1.9780898719604
  • libSBML — the SBML read/write API.
    • Bornstein BJ, Keating SM, Jouraku A, Hucka M. LibSBML: an API library for SBML. Bioinformatics. 2008;24(6):880–881. doi:10.1093/bioinformatics/btn051
  • NumPy — array programming throughout BNGsim.
    • Harris CR, Millman KJ, van der Walt SJ, et al. Array programming with NumPy. Nature. 2020;585(7825):357–362. doi:10.1038/s41586-020-2649-2
  • SciPy — scientific-computing routines (used in the test, benchmark, and parity suites).
    • Virtanen P, Gommers R, Oliphant TE, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17(3):261–272. doi:10.1038/s41592-019-0686-2
  • SymPy — symbolic differentiation for code-generated sensitivities and Jacobians.
    • Meurer A, Smith CP, Paprocki M, et al. SymPy: symbolic computing in Python. PeerJ Comput Sci. 2017;3:e103. doi:10.7717/peerj-cs.103
  • pandas — the Result.dataframe export.
    • McKinney W. Data structures for statistical computing in Python. In: Proceedings of the 9th Python in Science Conference (SciPy). 2010:56–61. doi:10.25080/Majora-92bf1922-00a
  • ExprTk (Arash Partow; https://github.com/ArashPartow/exprtk) — the vendored C++ math-expression evaluator; MIR (Vladimir Makarov; https://github.com/vnmakarov/mir) — the optional micro-JIT backend; and pybind11 (Wenzel Jakob, Jason Rhinelander, Dean Moldovan; https://github.com/pybind/pybind11) — the C++/Python binding layer. These are software projects without an associated paper.

Optional feature backends and interoperability

  • JAX — the differentiable RHS/Jacobian bridge.
    • Bradbury J, Frostig R, Hawkins P, Johnson MJ, Katariya Y, Leary C, Maclaurin D, Necula G, Paszke A, VanderPlas J, Wanderman-Milne S, Zhang Q. JAX: composable transformations of Python+NumPy programs. 2018. http://github.com/jax-ml/jax
  • Diffrax — the JAX-based differentiable ODE solver backend.
    • Kidger P. On Neural Differential Equations. PhD thesis, University of Oxford; 2021. arXiv:2202.02435
  • xarray — labeled N-D arrays for the Result.to_xarray() / netCDF export.
    • Hoyer S, Hamman J. xarray: N-D labeled Arrays and Datasets in Python. J Open Res Softw. 2017;5(1):10. doi:10.5334/jors.148
  • h5py — HDF5 archival of results (Andrew Collette and contributors; https://www.h5py.org).
  • Vivarium — the bngsim.vivarium process wrapper for multiscale modeling.
    • Agmon E, Spangler RK, Skalnik CJ, Poole W, Peirce SM, Morrison JH, Covert MW. Vivarium: an interface and engine for integrative multiscale modeling in computational biology. Bioinformatics. 2022;38(7):1972–1979. doi:10.1093/bioinformatics/btac049
  • PyBNF (PyBioNetFit) — the parameter-fitting front end that drives BNGsim, and the source of several example models.
    • Mitra ED, Suderman R, Colvin J, Ionkov A, Hu A, Sauro HM, Posner RG, Hlavacek WS. PyBioNetFit and the Biological Property Specification Language. iScience. 2019;19:1012–1036. doi:10.1016/j.isci.2019.08.045

Test suites and model corpora

BNGsim's correctness is checked against, and its benchmarks draw from, these community corpora (vendored corpora and their licenses are in NOTICE):

  • SBML Semantic Test Suite — the SBML Team's conformance suite (https://github.com/sbmlteam/sbml-test-suite).
  • DSMTS (Discrete Stochastic Models Test Suite) — vendored for hermetic stochastic testing.
    • Evans TW, Gillespie CS, Wilkinson DJ. The SBML discrete stochastic models test suite. Bioinformatics. 2008;24(2):285–286. doi:10.1093/bioinformatics/btm566
  • BioModels Database — the curated ODE model corpus (with its SED-ML protocols) used for ODE benchmarking.
    • Malik-Sheriff RS, Glont M, Nguyen TVN, et al. BioModels—15 years of sharing computational models in life science. Nucleic Acids Res. 2020;48(D1):D407–D415. doi:10.1093/nar/gkz1055
  • RuleHub (RuleWorld; https://github.com/RuleWorld/RuleHub), BNGL-Models (https://github.com/wshlavacek/BNGL-Models), and the BioNetGen model distribution (Models2) — vendored BNGL model corpora (see NOTICE).

Algorithms

  • Gillespie's Stochastic Simulation Algorithm (direct method) — the basis of BNGsim's exact SSA.
    • Gillespie DT. Exact stochastic simulation of coupled chemical reactions. J Phys Chem. 1977;81(25):2340–2361. doi:10.1021/j100540a008
  • Extrande — a thinning sampler used as an independent test oracle for the rate-rule-under-SSA hybrid path.
    • Voliotis M, Thomas P, Grima R, Bowsher CG. Stochastic simulation of biomolecular networks in dynamic environments. PLoS Comput Biol. 2016;12(6):e1004923. doi:10.1371/journal.pcbi.1004923

Build, test, and documentation tooling

BNGsim is built with scikit-build-core, CMake, Ninja, and pybind11 (stubs via pybind11-stubgen); tested with pytest and linted with ruff, mypy, and clang-format; documented with Sphinx, MyST-Parser, and Furo. Benchmark and parity tooling additionally uses matplotlib and bioservices. We thank the maintainers of these projects.