Abstract
Stochasticity is a fundamental feature of biology at the single cell level. Quantitative experimental data ranging from microscopy to single-cell transcriptomic is continually expanding our understanding of the role of stochasticity in gene expression and other cellular processes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anderson, D., Kurtz, T.: Continuous time Markov chain models for chemical reaction networks. In: Koeppl, H., Setti, G., di Bernardo, M., Densmore, D. (eds.) Design and Analysis of Biomolecular Circuits. Springer, New York (2011). https://doi.org/10.1007/978-1-4419-6766-4_1
Gardner, T.S., Cantor, C.R., Collins, J.J.: Construction of a genetic toggle switch in Escherichia coli. Nature 403(6767), 339–342 (2000)
Georgoulas, A., Hillston, J., Sanguinetti, G.: Unbiased Bayesian inference for population Markov jump processes via random truncations. Stat. Comput. 27(4), 991–1002 (2017)
Gupta, A., Mikelson, J., Khammash, M.: A finite state projection algorithm for the stationary solution of the chemical master equation. J. Chem. Phys 147(15), 154101 (2017)
Munsky, B., Khammash, M.: The finite state projection algorithm for the solution of the chemical master equation. J. Chem. Phys. 124(4), 044104 (2006)
Schnoerr, D., Sanguinetti, G., Grima, R.: Approximation and inference methods for stochastic biochemical kinetics–a tutorial review. J. Phys. A Math Theor. 50(9), 093001 (2017)
Székely Jr., T., Burrage, K.: Stochastic simulation in systems biology. Comput. Struct. Biotechnol. J. 12(20–21), 14–25 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Gupta, A., Khammash, M., Sanguinetti, G. (2019). Bayesian Parameter Estimation for Stochastic Reaction Networks from Steady-State Observations. In: Bortolussi, L., Sanguinetti, G. (eds) Computational Methods in Systems Biology. CMSB 2019. Lecture Notes in Computer Science(), vol 11773. Springer, Cham. https://doi.org/10.1007/978-3-030-31304-3_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-31304-3_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31303-6
Online ISBN: 978-3-030-31304-3
eBook Packages: Computer ScienceComputer Science (R0)