---
_id: '1658'
abstract:
- lang: eng
text: Continuous-time Markov chain (CTMC) models have become a central tool for
understanding the dynamics of complex reaction networks and the importance of
stochasticity in the underlying biochemical processes. When such models are employed
to answer questions in applications, in order to ensure that the model provides
a sufficiently accurate representation of the real system, it is of vital importance
that the model parameters are inferred from real measured data. This, however,
is often a formidable task and all of the existing methods fail in one case or
the other, usually because the underlying CTMC model is high-dimensional and computationally
difficult to analyze. The parameter inference methods that tend to scale best
in the dimension of the CTMC are based on so-called moment closure approximations.
However, there exists a large number of different moment closure approximations
and it is typically hard to say a priori which of the approximations is the most
suitable for the inference procedure. Here, we propose a moment-based parameter
inference method that automatically chooses the most appropriate moment closure
method. Accordingly, contrary to existing methods, the user is not required to
be experienced in moment closure techniques. In addition to that, our method adaptively
changes the approximation during the parameter inference to ensure that always
the best approximation is used, even in cases where different approximations are
best in different regions of the parameter space.
alternative_title:
- LNCS
author:
- first_name: Sergiy
full_name: Bogomolov, Sergiy
id: 369D9A44-F248-11E8-B48F-1D18A9856A87
last_name: Bogomolov
orcid: 0000-0002-0686-0365
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000−0002−2985−7724
- first_name: Andreas
full_name: Podelski, Andreas
last_name: Podelski
- first_name: Jakob
full_name: Ruess, Jakob
id: 4A245D00-F248-11E8-B48F-1D18A9856A87
last_name: Ruess
orcid: 0000-0003-1615-3282
- first_name: Christian
full_name: Schilling, Christian
last_name: Schilling
citation:
ama: Bogomolov S, Henzinger TA, Podelski A, Ruess J, Schilling C. Adaptive moment
closure for parameter inference of biochemical reaction networks. 2015;9308:77-89.
doi:10.1007/978-3-319-23401-4_8
apa: 'Bogomolov, S., Henzinger, T. A., Podelski, A., Ruess, J., & Schilling,
C. (2015). Adaptive moment closure for parameter inference of biochemical reaction
networks. Presented at the CMSB: Computational Methods in Systems Biology, Nantes,
France: Springer. https://doi.org/10.1007/978-3-319-23401-4_8'
chicago: Bogomolov, Sergiy, Thomas A Henzinger, Andreas Podelski, Jakob Ruess, and
Christian Schilling. “Adaptive Moment Closure for Parameter Inference of Biochemical
Reaction Networks.” Lecture Notes in Computer Science. Springer, 2015. https://doi.org/10.1007/978-3-319-23401-4_8.
ieee: S. Bogomolov, T. A. Henzinger, A. Podelski, J. Ruess, and C. Schilling, “Adaptive
moment closure for parameter inference of biochemical reaction networks,” vol.
9308. Springer, pp. 77–89, 2015.
ista: Bogomolov S, Henzinger TA, Podelski A, Ruess J, Schilling C. 2015. Adaptive
moment closure for parameter inference of biochemical reaction networks. 9308,
77–89.
mla: Bogomolov, Sergiy, et al. Adaptive Moment Closure for Parameter Inference
of Biochemical Reaction Networks. Vol. 9308, Springer, 2015, pp. 77–89, doi:10.1007/978-3-319-23401-4_8.
short: S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, C. Schilling, 9308 (2015)
77–89.
conference:
end_date: 2015-09-18
location: Nantes, France
name: 'CMSB: Computational Methods in Systems Biology'
start_date: 2015-09-16
date_created: 2018-12-11T11:53:18Z
date_published: 2015-09-01T00:00:00Z
date_updated: 2023-02-21T16:17:24Z
day: '01'
department:
- _id: ToHe
- _id: GaTk
doi: 10.1007/978-3-319-23401-4_8
ec_funded: 1
intvolume: ' 9308'
language:
- iso: eng
month: '09'
oa_version: None
page: 77 - 89
project:
- _id: 25EE3708-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '267989'
name: Quantitative Reactive Modeling
- _id: 25F42A32-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Z211
name: The Wittgenstein Prize
- _id: 25832EC2-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S 11407_N23
name: Rigorous Systems Engineering
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication_status: published
publisher: Springer
publist_id: '5492'
quality_controlled: '1'
related_material:
record:
- id: '1148'
relation: later_version
status: public
scopus_import: 1
series_title: Lecture Notes in Computer Science
status: public
title: Adaptive moment closure for parameter inference of biochemical reaction networks
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 9308
year: '2015'
...