MomentClosure.jl is a tool to automatically obtain time-evolution equations of moments up to an arbitrary order for virtually any chemical reaction network or system of stochastic differential equations (SDEs), implementing a wide array of moment closure approximations commonly used in stochastic biochemical kinetics [1]. MomentClosure is (attempted to be) fairly well-integrated within the broader Julia ecosystem utilising a number of familiar packages:
- MomentClosure can be immediately applied to reaction network models defined using Catalyst and SDE systems built with ModelingToolkit.
- Moment equations are generated as a ModelingToolkit
ODESystem(with some extra help from Symbolics and SymbolicUtils). - The resulting
ODESystemcan be solved using any DifferentialEquations ODE solvers, enabling further study of the system using parameter estimation, sensitivity analysis and bifurcation analysis tools.
Please see the documentation for information on using the package, theory behind it and in-depth examples.
- Chemical reaction networks containing any number of molecular species and reactions with any type of smooth propensity functions are supported. Models can be defined using Catalyst as
ModelingToolkit.ReactionSystem. Alternatively, built-in functionality (heavily based on Catalyst) can be used for model initialisation. - Added support for reaction networks involving reaction products that are geometrically distributed random variables. An example of such network is an autoregulatory gene network with bursty protein production where the burst size follows a geometric distribution.
- Equations describing the time evolution of means and central moments of the number of molecules of each species in the system can be generated up to arbitrary order [2, 3]. Note that non-polynomial propensity functions are Taylor expanded to a specified order. Raw moment equations can also be generated for mass-action systems (where all propensity functions are polynomials).
- SDE systems defined as
ModelingToolkit.SDESystemare supported (big thanks to @FHoltorf). Similarly to reaction networks with non-polynomial propensities, non-polynomial drift and diffusion coefficients are Taylor expanded.
- Currently implemented moment closure approximations include:
- Moment equations are constructed as a
ModelingToolkit.ODESystemthat can be solved using any DifferentialEquations ODE solver. Moreover, parameter estimation, sensitivity analysis and bifurcation analysis tools can be applied to further study the resulting system of equations. - Latexify can be used to generate LaTeX expressions of the corresponding moment equations.
If you use MomentClosure in your work, please cite our paper:
@article{MomentClosure2021,
author = {Sukys, Augustinas and Grima, Ramon},
title = "{MomentClosure.jl: automated moment closure approximations in Julia}",
journal = {Bioinformatics},
volume = {38},
number = {1},
pages = {289-290},
year = {2021},
month = {06},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btab469},
url = {https://doi.org/10.1093/bioinformatics/btab469},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/38/1/289/41891091/btab469.pdf},
}
[1] D. Schnoerr, G. Sanguinetti, and R. Grima, "Approximation and inference methods for stochastic biochemical kinetics - a tutorial review", Journal of Physics A: Mathematical and Theoretical 50, 093001 (2017). https://doi.org/10.1088/1751-8121/aa54d9
[2]: A. Ale, P. Kirk, and M. P. H. Stumpf, "A general moment expansion method for stochastic kinetic models", The Journal of Chemical Physics 138, 174101 (2013). https://doi.org/10.1063/1.4802475
[3]: C. H. Lee, "A Moment Closure Method for Stochastic Chemical Reaction Networks with General Kinetics", MATCH Communications in Mathematical and in Computer Chemistry 70, 785-800 (2013). https://match.pmf.kg.ac.rs/electronic_versions/Match70/n3/match70n3_785-800.pdf
[4]: D. Schnoerr, G. Sanguinetti, and R. Grima, "Comparison of different moment-closure approximations for stochastic chemical kinetics", The Journal of Chemical Physics 143, 185101 (2015). https://doi.org/10.1063/1.4934990
[5]: E. Lakatos, A. Ale, P. D. W. Kirk, and M. P. H. Stumpf, "Multivariate moment closure techniques for stochastic kinetic models", The Journal of Chemical Physics 143, 094107 (2015). https://doi.org/10.1063/1.4929837
[6]: A. Singh and J. P. Hespanha, "Lognormal Moment Closures for Biochemical Reactions", in Proceedings of the 45th IEEE Conference on Decision and Control, ISSN: 0191-2216 (Dec. 2006), pp. 2063-2068. https://doi.org/10.1109/CDC.2006.376994
[7]: M. Soltani, C. A. Vargas-Garcia, and A. Singh, "Conditional Moment Closure Schemes for Studying Stochastic Dynamics of Genetic Circuits", IEEE Transactions on Biomedical Circuits and Systems 9, 518-526 (2015). https://doi.org/10.1109/TBCAS.2015.2453158
[8]: Z. Cao and R. Grima, "Linear mapping approximation of gene regulatory networks with stochastic dynamics", Nature Communications 9, 3305 (2018). https://doi.org/10.1038/s41467-018-05822-0