---
_id: '3136'
abstract:
- lang: eng
text: 'Continuous-time Markov chains (CTMC) with their rich theory and efficient
simulation algorithms have been successfully used in modeling stochastic processes
in diverse areas such as computer science, physics, and biology. However, systems
that comprise non-instantaneous events cannot be accurately and efficiently modeled
with CTMCs. In this paper we define delayed CTMCs, an extension of CTMCs that
allows for the specification of a lower bound on the time interval between an
event''s initiation and its completion, and we propose an algorithm for the computation
of their behavior. Our algorithm effectively decomposes the computation into two
stages: a pure CTMC governs event initiations while a deterministic process guarantees
lower bounds on event completion times. Furthermore, from the nature of delayed
CTMCs, we obtain a parallelized version of our algorithm. We use our formalism
to model genetic regulatory circuits (biological systems where delayed events
are common) and report on the results of our numerical algorithm as run on a cluster.
We compare performance and accuracy of our results with results obtained by using
pure CTMCs. © 2012 Springer-Verlag.'
acknowledgement: This work was supported by the ERC Advanced Investigator grant on
Quantitative Reactive Modeling (QUAREM) and by the Swiss National Science Foundation.
alternative_title:
- LNCS
author:
- first_name: Calin C
full_name: Guet, Calin C
id: 47F8433E-F248-11E8-B48F-1D18A9856A87
last_name: Guet
orcid: 0000-0001-6220-2052
- first_name: Ashutosh
full_name: Gupta, Ashutosh
id: 335E5684-F248-11E8-B48F-1D18A9856A87
last_name: Gupta
- 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: Maria
full_name: Mateescu, Maria
id: 3B43276C-F248-11E8-B48F-1D18A9856A87
last_name: Mateescu
- first_name: Ali
full_name: Sezgin, Ali
id: 4C7638DA-F248-11E8-B48F-1D18A9856A87
last_name: Sezgin
citation:
ama: 'Guet CC, Gupta A, Henzinger TA, Mateescu M, Sezgin A. Delayed continuous time
Markov chains for genetic regulatory circuits. In: Vol 7358. Springer; 2012:294-309.
doi:10.1007/978-3-642-31424-7_24'
apa: 'Guet, C. C., Gupta, A., Henzinger, T. A., Mateescu, M., & Sezgin, A. (2012).
Delayed continuous time Markov chains for genetic regulatory circuits (Vol. 7358,
pp. 294–309). Presented at the CAV: Computer Aided Verification, Berkeley, CA,
USA: Springer. https://doi.org/10.1007/978-3-642-31424-7_24'
chicago: Guet, Calin C, Ashutosh Gupta, Thomas A Henzinger, Maria Mateescu, and
Ali Sezgin. “Delayed Continuous Time Markov Chains for Genetic Regulatory Circuits,”
7358:294–309. Springer, 2012. https://doi.org/10.1007/978-3-642-31424-7_24.
ieee: 'C. C. Guet, A. Gupta, T. A. Henzinger, M. Mateescu, and A. Sezgin, “Delayed
continuous time Markov chains for genetic regulatory circuits,” presented at the
CAV: Computer Aided Verification, Berkeley, CA, USA, 2012, vol. 7358, pp. 294–309.'
ista: 'Guet CC, Gupta A, Henzinger TA, Mateescu M, Sezgin A. 2012. Delayed continuous
time Markov chains for genetic regulatory circuits. CAV: Computer Aided Verification,
LNCS, vol. 7358, 294–309.'
mla: Guet, Calin C., et al. Delayed Continuous Time Markov Chains for Genetic
Regulatory Circuits. Vol. 7358, Springer, 2012, pp. 294–309, doi:10.1007/978-3-642-31424-7_24.
short: C.C. Guet, A. Gupta, T.A. Henzinger, M. Mateescu, A. Sezgin, in:, Springer,
2012, pp. 294–309.
conference:
end_date: 2012-07-13
location: Berkeley, CA, USA
name: 'CAV: Computer Aided Verification'
start_date: 2012-07-07
corr_author: '1'
date_created: 2018-12-11T12:01:36Z
date_published: 2012-07-01T00:00:00Z
date_updated: 2024-10-09T20:54:47Z
day: '01'
department:
- _id: CaGu
- _id: ToHe
doi: 10.1007/978-3-642-31424-7_24
ec_funded: 1
language:
- iso: eng
month: '07'
oa_version: None
page: 294 - 309
project:
- _id: 25EE3708-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '267989'
name: Quantitative Reactive Modeling
publication_status: published
publisher: Springer
publist_id: '3561'
quality_controlled: '1'
scopus_import: 1
status: public
title: Delayed continuous time Markov chains for genetic regulatory circuits
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: '7358 '
year: '2012'
...
---
_id: '2302'
abstract:
- lang: eng
text: 'We introduce propagation models (PMs), a formalism able to express several
kinds of equations that describe the behavior of biochemical reaction networks.
Furthermore, we introduce the propagation abstract data type (PADT), which separates
concerns regarding different numerical algorithms for the transient analysis of
biochemical reaction networks from concerns regarding their implementation, thus
allowing for portable and efficient solutions. The state of a propagation abstract
data type is given by a vector that assigns mass values to a set of nodes, and
its (next) operator propagates mass values through this set of nodes. We propose
an approximate implementation of the (next) operator, based on threshold abstraction,
which propagates only "significant" mass values and thus achieves a
compromise between efficiency and accuracy. Finally, we give three use cases for
propagation models: the chemical master equation (CME), the reaction rate equation
(RRE), and a hybrid method that combines these two equations. These three applications
use propagation models in order to propagate probabilities and/or expected values
and variances of the model''s variables.'
article_processing_charge: No
author:
- 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: Maria
full_name: Mateescu, Maria
id: 3B43276C-F248-11E8-B48F-1D18A9856A87
last_name: Mateescu
citation:
ama: Henzinger TA, Mateescu M. The propagation approach for computing biochemical
reaction networks. IEEE ACM Transactions on Computational Biology and Bioinformatics.
2012;10(2):310-322. doi:10.1109/TCBB.2012.91
apa: Henzinger, T. A., & Mateescu, M. (2012). The propagation approach for computing
biochemical reaction networks. IEEE ACM Transactions on Computational Biology
and Bioinformatics. IEEE. https://doi.org/10.1109/TCBB.2012.91
chicago: Henzinger, Thomas A, and Maria Mateescu. “The Propagation Approach for
Computing Biochemical Reaction Networks.” IEEE ACM Transactions on Computational
Biology and Bioinformatics. IEEE, 2012. https://doi.org/10.1109/TCBB.2012.91.
ieee: T. A. Henzinger and M. Mateescu, “The propagation approach for computing biochemical
reaction networks,” IEEE ACM Transactions on Computational Biology and Bioinformatics,
vol. 10, no. 2. IEEE, pp. 310–322, 2012.
ista: Henzinger TA, Mateescu M. 2012. The propagation approach for computing biochemical
reaction networks. IEEE ACM Transactions on Computational Biology and Bioinformatics.
10(2), 310–322.
mla: Henzinger, Thomas A., and Maria Mateescu. “The Propagation Approach for Computing
Biochemical Reaction Networks.” IEEE ACM Transactions on Computational Biology
and Bioinformatics, vol. 10, no. 2, IEEE, 2012, pp. 310–22, doi:10.1109/TCBB.2012.91.
short: T.A. Henzinger, M. Mateescu, IEEE ACM Transactions on Computational Biology
and Bioinformatics 10 (2012) 310–322.
corr_author: '1'
date_created: 2018-12-11T11:56:52Z
date_published: 2012-07-03T00:00:00Z
date_updated: 2025-09-29T14:17:43Z
day: '03'
department:
- _id: ToHe
- _id: CaGu
doi: 10.1109/TCBB.2012.91
ec_funded: 1
external_id:
isi:
- '000323504100006'
pmid:
- '22778152'
intvolume: ' 10'
isi: 1
issue: '2'
language:
- iso: eng
month: '07'
oa_version: None
page: 310 - 322
pmid: 1
project:
- _id: 25EE3708-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '267989'
name: Quantitative Reactive Modeling
publication: IEEE ACM Transactions on Computational Biology and Bioinformatics
publication_status: published
publisher: IEEE
publist_id: '4625'
quality_controlled: '1'
scopus_import: '1'
status: public
title: The propagation approach for computing biochemical reaction networks
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 10
year: '2012'
...
---
_id: '3834'
abstract:
- lang: eng
text: "Background\r\n\r\nThe chemical master equation (CME) is a system of ordinary
differential equations that describes the evolution of a network of chemical reactions
as a stochastic process. Its solution yields the probability density vector of
the system at each point in time. Solving the CME numerically is in many cases
computationally expensive or even infeasible as the number of reachable states
can be very large or infinite. We introduce the sliding window method, which computes
an approximate solution of the CME by performing a sequence of local analysis
steps. In each step, only a manageable subset of states is considered, representing
a "window" into the state space. In subsequent steps, the window follows
the direction in which the probability mass moves, until the time period of interest
has elapsed. We construct the window based on a deterministic approximation of
the future behavior of the system by estimating upper and lower bounds on the
populations of the chemical species.\r\nResults\r\n\r\nIn order to show the effectiveness
of our approach, we apply it to several examples previously described in the literature.
The experimental results show that the proposed method speeds up the analysis
considerably, compared to a global analysis, while still providing high accuracy.\r\n\r\n\r\nConclusions\r\n\r\nThe
sliding window method is a novel approach to address the performance problems
of numerical algorithms for the solution of the chemical master equation. The
method efficiently approximates the probability distributions at the time points
of interest for a variety of chemically reacting systems, including systems for
which no upper bound on the population sizes of the chemical species is known
a priori."
acknowledgement: This research has been partially funded by the Swiss National Science
Foundation under grant 205321-111840 and by the Cluster of Excellence on Multimodal
Computing and Interaction at Saarland University.
article_processing_charge: No
author:
- first_name: Verena
full_name: Wolf, Verena
last_name: Wolf
- first_name: Rushil
full_name: Goel, Rushil
last_name: Goel
- first_name: Maria
full_name: Mateescu, Maria
id: 3B43276C-F248-11E8-B48F-1D18A9856A87
last_name: Mateescu
- first_name: Thomas A
full_name: Henzinger, Thomas A
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000−0002−2985−7724
citation:
ama: Wolf V, Goel R, Mateescu M, Henzinger TA. Solving the chemical master equation
using sliding windows. BMC Systems Biology. 2010;4(42):1-19. doi:10.1186/1752-0509-4-42
apa: Wolf, V., Goel, R., Mateescu, M., & Henzinger, T. A. (2010). Solving the
chemical master equation using sliding windows. BMC Systems Biology. BioMed
Central. https://doi.org/10.1186/1752-0509-4-42
chicago: Wolf, Verena, Rushil Goel, Maria Mateescu, and Thomas A Henzinger. “Solving
the Chemical Master Equation Using Sliding Windows.” BMC Systems Biology.
BioMed Central, 2010. https://doi.org/10.1186/1752-0509-4-42.
ieee: V. Wolf, R. Goel, M. Mateescu, and T. A. Henzinger, “Solving the chemical
master equation using sliding windows,” BMC Systems Biology, vol. 4, no.
42. BioMed Central, pp. 1–19, 2010.
ista: Wolf V, Goel R, Mateescu M, Henzinger TA. 2010. Solving the chemical master
equation using sliding windows. BMC Systems Biology. 4(42), 1–19.
mla: Wolf, Verena, et al. “Solving the Chemical Master Equation Using Sliding Windows.”
BMC Systems Biology, vol. 4, no. 42, BioMed Central, 2010, pp. 1–19, doi:10.1186/1752-0509-4-42.
short: V. Wolf, R. Goel, M. Mateescu, T.A. Henzinger, BMC Systems Biology 4 (2010)
1–19.
corr_author: '1'
date_created: 2018-12-11T12:05:25Z
date_published: 2010-04-08T00:00:00Z
date_updated: 2025-09-30T09:37:27Z
day: '08'
ddc:
- '005'
department:
- _id: ToHe
doi: 10.1186/1752-0509-4-42
external_id:
isi:
- '000277612700001'
file:
- access_level: open_access
checksum: 220239fae76f7b03c4d7f05d74ef426f
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:16:29Z
date_updated: 2020-07-14T12:46:16Z
file_id: '5217'
file_name: IST-2012-72-v1+1_Solving_the_chemical_master_equation_using_sliding_windows.pdf
file_size: 1919130
relation: main_file
file_date_updated: 2020-07-14T12:46:16Z
has_accepted_license: '1'
intvolume: ' 4'
isi: 1
issue: '42'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '04'
oa: 1
oa_version: Published Version
page: 1 - 19
publication: BMC Systems Biology
publication_status: published
publisher: BioMed Central
publist_id: '2374'
pubrep_id: '72'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Solving the chemical master equation using sliding windows
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 4
year: '2010'
...
---
_id: '3843'
abstract:
- lang: eng
text: "Within systems biology there is an increasing interest in the stochastic
behavior of biochemical reaction networks. An appropriate stochastic description
is provided by the chemical master equation, which represents a continuous- time
Markov chain (CTMC).\r\nStandard Uniformization (SU) is an efficient method for
the transient analysis of CTMCs. For systems with very different time scales,
such as biochemical reaction networks, SU is computationally expensive. In these
cases, a variant of SU, called adaptive uniformization (AU), is known to reduce
the large number of iterations needed by SU. The additional difficulty of AU is
that it requires the solution of a birth process.\r\nIn this paper we present
an on-the-fly variant of AU, where we improve the original algorithm for AU at
the cost of a small approximation error. By means of several examples, we show
that our approach is particularly well-suited for biochemical reaction networks."
acknowledgement: This research has been partially funded by the Swiss National Science
Foundation under grant 205321-111840 and by the Cluster of Excellence on Multimodal
Computing and Interaction at Saarland University.
article_processing_charge: No
author:
- first_name: Frédéric
full_name: Didier, Frédéric
last_name: Didier
- 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: Maria
full_name: Mateescu, Maria
id: 3B43276C-F248-11E8-B48F-1D18A9856A87
last_name: Mateescu
- first_name: Verena
full_name: Wolf, Verena
last_name: Wolf
citation:
ama: 'Didier F, Henzinger TA, Mateescu M, Wolf V. Fast adaptive uniformization of
the chemical master equation. In: Vol 4. IEEE; 2009:118-127. doi:10.1109/HiBi.2009.23'
apa: 'Didier, F., Henzinger, T. A., Mateescu, M., & Wolf, V. (2009). Fast adaptive
uniformization of the chemical master equation (Vol. 4, pp. 118–127). Presented
at the HIBI: High-Performance Computational Systems Biology, Trento, Italy: IEEE.
https://doi.org/10.1109/HiBi.2009.23'
chicago: Didier, Frédéric, Thomas A Henzinger, Maria Mateescu, and Verena Wolf.
“Fast Adaptive Uniformization of the Chemical Master Equation,” 4:118–27. IEEE,
2009. https://doi.org/10.1109/HiBi.2009.23.
ieee: 'F. Didier, T. A. Henzinger, M. Mateescu, and V. Wolf, “Fast adaptive uniformization
of the chemical master equation,” presented at the HIBI: High-Performance Computational
Systems Biology, Trento, Italy, 2009, vol. 4, no. 6, pp. 118–127.'
ista: 'Didier F, Henzinger TA, Mateescu M, Wolf V. 2009. Fast adaptive uniformization
of the chemical master equation. HIBI: High-Performance Computational Systems
Biology vol. 4, 118–127.'
mla: Didier, Frédéric, et al. Fast Adaptive Uniformization of the Chemical Master
Equation. Vol. 4, no. 6, IEEE, 2009, pp. 118–27, doi:10.1109/HiBi.2009.23.
short: F. Didier, T.A. Henzinger, M. Mateescu, V. Wolf, in:, IEEE, 2009, pp. 118–127.
conference:
end_date: 2009-10-16
location: Trento, Italy
name: 'HIBI: High-Performance Computational Systems Biology'
start_date: 2009-10-14
date_created: 2018-12-11T12:05:28Z
date_published: 2009-10-30T00:00:00Z
date_updated: 2025-09-30T09:54:51Z
day: '30'
ddc:
- '000'
department:
- _id: ToHe
- _id: CaGu
doi: 10.1109/HiBi.2009.23
external_id:
isi:
- '000275038300017'
file:
- access_level: open_access
checksum: 9a3bde48f43203991a0b3c6a277c2f5b
content_type: application/pdf
creator: dernst
date_created: 2020-05-19T16:33:55Z
date_updated: 2020-07-14T12:46:17Z
file_id: '7874'
file_name: 2009_HIBI_Didier.pdf
file_size: 222890
relation: main_file
file_date_updated: 2020-07-14T12:46:17Z
has_accepted_license: '1'
intvolume: ' 4'
isi: 1
issue: '6'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Submitted Version
page: 118 - 127
publication_status: published
publisher: IEEE
publist_id: '2348'
quality_controlled: '1'
related_material:
record:
- id: '3842'
relation: later_version
status: public
scopus_import: '1'
status: public
title: Fast adaptive uniformization of the chemical master equation
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 4
year: '2009'
...
---
_id: '4453'
abstract:
- lang: eng
text: We present an on-the-fly abstraction technique for infinite-state continuous
-time Markov chains. We consider Markov chains that are specified by a finite
set of transition classes. Such models naturally represent biochemical reactions
and therefore play an important role in the stochastic modeling of biological
systems. We approximate the transient probability distributions at various time
instances by solving a sequence of dynamically constructed abstract models, each
depending on the previous one. Each abstract model is a finite Markov chain that
represents the behavior of the original, infinite chain during a specific time
interval. Our approach provides complete information about probability distributions,
not just about individual parameters like the mean. The error of each abstraction
can be computed, and the precision of the abstraction refined when desired. We
implemented the algorithm and demonstrate its usefulness and efficiency on several
case studies from systems biology.
acknowledgement: The research has been partially funded by the Swiss National Science
Foundation under grant 205321-111840.
alternative_title:
- LNCS
author:
- first_name: Thomas A
full_name: Thomas Henzinger
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000−0002−2985−7724
- first_name: Maria
full_name: Maria Mateescu
id: 3B43276C-F248-11E8-B48F-1D18A9856A87
last_name: Mateescu
- first_name: Verena
full_name: Wolf, Verena
last_name: Wolf
citation:
ama: 'Henzinger TA, Mateescu M, Wolf V. Sliding-window abstraction for infinite
Markov chains. In: Vol 5643. Springer; 2009:337-352. doi:10.1007/978-3-642-02658-4_27'
apa: 'Henzinger, T. A., Mateescu, M., & Wolf, V. (2009). Sliding-window abstraction
for infinite Markov chains (Vol. 5643, pp. 337–352). Presented at the CAV: Computer
Aided Verification, Springer. https://doi.org/10.1007/978-3-642-02658-4_27'
chicago: Henzinger, Thomas A, Maria Mateescu, and Verena Wolf. “Sliding-Window Abstraction
for Infinite Markov Chains,” 5643:337–52. Springer, 2009. https://doi.org/10.1007/978-3-642-02658-4_27.
ieee: 'T. A. Henzinger, M. Mateescu, and V. Wolf, “Sliding-window abstraction for
infinite Markov chains,” presented at the CAV: Computer Aided Verification, 2009,
vol. 5643, pp. 337–352.'
ista: 'Henzinger TA, Mateescu M, Wolf V. 2009. Sliding-window abstraction for infinite
Markov chains. CAV: Computer Aided Verification, LNCS, vol. 5643, 337–352.'
mla: Henzinger, Thomas A., et al. Sliding-Window Abstraction for Infinite Markov
Chains. Vol. 5643, Springer, 2009, pp. 337–52, doi:10.1007/978-3-642-02658-4_27.
short: T.A. Henzinger, M. Mateescu, V. Wolf, in:, Springer, 2009, pp. 337–352.
conference:
name: 'CAV: Computer Aided Verification'
date_created: 2018-12-11T12:08:55Z
date_published: 2009-01-01T00:00:00Z
date_updated: 2021-01-12T07:57:04Z
day: '01'
doi: 10.1007/978-3-642-02658-4_27
extern: 1
file:
- access_level: open_access
checksum: 36b974111521ea534aae294166e93a63
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:20Z
date_updated: 2020-07-14T12:46:30Z
file_id: '4938'
file_name: IST-2012-40-v1+1_Sliding-window_abstraction_for_infinite_markov_chains.pdf
file_size: 804295
relation: main_file
file_date_updated: 2020-07-14T12:46:30Z
intvolume: ' 5643'
main_file_link:
- open_access: '0'
url: http://pub.ist.ac.at/%7Etah/Publications/sliding-window_abstraction_for_infinite_markov_chains.pdf
month: '01'
oa: 1
page: 337 - 352
publication_status: published
publisher: Springer
publist_id: '278'
pubrep_id: '40'
quality_controlled: 0
status: public
title: Sliding-window abstraction for infinite Markov chains
type: conference
volume: 5643
year: '2009'
...
---
_id: '4535'
abstract:
- lang: eng
text: |-
Molecular noise, which arises from the randomness of the discrete events in the cell, significantly influences fundamental biological processes. Discrete -state continuous-time stochastic models (CTMC) can be used to describe such effects, but the calculation of the probabilities of certain events is computationally expensive.
We present a comparison of two analysis approaches for CTMC. On one hand, we estimate the probabilities of interest using repeated Gillespie simulation and determine the statistical accuracy that we obtain. On the other hand, we apply a numerical reachability analysis that approximates the probability distributions of the system at several time instances. We use examples of cellular processes to demonstrate the superiority of the reachability analysis if accurate results are required.
acknowledgement: This research was supported in part by the Swiss National Science
Foundation under grant 205321-111840 and by the Excellence Cluster on Multimodal
Computing and Interaction.
alternative_title:
- LNCS
author:
- first_name: Frédéric
full_name: Didier, Frédéric
last_name: Didier
- first_name: Thomas A
full_name: Thomas Henzinger
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000−0002−2985−7724
- first_name: Maria
full_name: Maria Mateescu
id: 3B43276C-F248-11E8-B48F-1D18A9856A87
last_name: Mateescu
- first_name: Verena
full_name: Wolf, Verena
last_name: Wolf
citation:
ama: 'Didier F, Henzinger TA, Mateescu M, Wolf V. Approximation of event probabilities
in noisy cellular processes. In: Vol 5688. Springer; 2009:173-188. doi:10.1007/978-3-642-03845-7_12'
apa: 'Didier, F., Henzinger, T. A., Mateescu, M., & Wolf, V. (2009). Approximation
of event probabilities in noisy cellular processes (Vol. 5688, pp. 173–188). Presented
at the CMSB: Computational Methods in Systems Biology, Springer. https://doi.org/10.1007/978-3-642-03845-7_12'
chicago: Didier, Frédéric, Thomas A Henzinger, Maria Mateescu, and Verena Wolf.
“Approximation of Event Probabilities in Noisy Cellular Processes,” 5688:173–88.
Springer, 2009. https://doi.org/10.1007/978-3-642-03845-7_12.
ieee: 'F. Didier, T. A. Henzinger, M. Mateescu, and V. Wolf, “Approximation of event
probabilities in noisy cellular processes,” presented at the CMSB: Computational
Methods in Systems Biology, 2009, vol. 5688, pp. 173–188.'
ista: 'Didier F, Henzinger TA, Mateescu M, Wolf V. 2009. Approximation of event
probabilities in noisy cellular processes. CMSB: Computational Methods in Systems
Biology, LNCS, vol. 5688, 173–188.'
mla: Didier, Frédéric, et al. Approximation of Event Probabilities in Noisy Cellular
Processes. Vol. 5688, Springer, 2009, pp. 173–88, doi:10.1007/978-3-642-03845-7_12.
short: F. Didier, T.A. Henzinger, M. Mateescu, V. Wolf, in:, Springer, 2009, pp.
173–188.
conference:
name: 'CMSB: Computational Methods in Systems Biology'
date_created: 2018-12-11T12:09:21Z
date_published: 2009-08-17T00:00:00Z
date_updated: 2025-09-30T09:03:30Z
day: '17'
doi: 10.1007/978-3-642-03845-7_12
extern: 1
intvolume: ' 5688'
month: '08'
page: 173 - 188
publication_status: published
publisher: Springer
publist_id: '189'
quality_controlled: 0
related_material:
record:
- id: '3364'
relation: later_version
status: public
status: public
title: Approximation of event probabilities in noisy cellular processes
type: conference
volume: 5688
year: '2009'
...
---
_id: '4527'
abstract:
- lang: eng
text: |-
We introduce bounded asynchrony, a notion of concurrency tailored to the modeling of biological cell-cell interactions. Bounded asynchrony is the result of a scheduler that bounds the number of steps that one process gets ahead of other processes; this allows the components of a system to move independently while keeping them coupled. Bounded asynchrony accurately reproduces the experimental observations made about certain cell-cell interactions: its constrained nondeterminism captures the variability observed in cells that, although equally potent, assume distinct fates. Real-life cells are not “scheduled”, but we show that distributed real-time behavior can lead to component interactions that are observationally equivalent to bounded asynchrony; this provides a possible mechanistic explanation for the phenomena observed during cell fate specification.
We use model checking to determine cell fates. The nondeterminism of bounded asynchrony causes state explosion during model checking, but partial-order methods are not directly applicable. We present a new algorithm that reduces the number of states that need to be explored: our optimization takes advantage of the bounded-asynchronous progress and the spatially local interactions of components that model cells. We compare our own communication-based reduction with partial-order reduction (on a restricted form of bounded asynchrony) and experiments illustrate that our algorithm leads to significant savings.
acknowledgement: Supported in part by the Swiss National Science Foundation (grant
205321-111840).
alternative_title:
- LNCS
author:
- first_name: Jasmin
full_name: Fisher, Jasmin
last_name: Fisher
- first_name: Thomas A
full_name: Thomas Henzinger
id: 40876CD8-F248-11E8-B48F-1D18A9856A87
last_name: Henzinger
orcid: 0000−0002−2985−7724
- first_name: Maria
full_name: Maria Mateescu
id: 3B43276C-F248-11E8-B48F-1D18A9856A87
last_name: Mateescu
- first_name: Nir
full_name: Piterman, Nir
last_name: Piterman
citation:
ama: 'Fisher J, Henzinger TA, Mateescu M, Piterman N. Bounded asynchrony: Concurrency
for modeling cell-cell interactions. In: Vol 5054. Springer; 2008:17-32. doi:10.1007/978-3-540-68413-8_2'
apa: 'Fisher, J., Henzinger, T. A., Mateescu, M., & Piterman, N. (2008). Bounded
asynchrony: Concurrency for modeling cell-cell interactions (Vol. 5054, pp. 17–32).
Presented at the FMSB: Formal Methods in Systems Biology, Springer. https://doi.org/10.1007/978-3-540-68413-8_2'
chicago: 'Fisher, Jasmin, Thomas A Henzinger, Maria Mateescu, and Nir Piterman.
“Bounded Asynchrony: Concurrency for Modeling Cell-Cell Interactions,” 5054:17–32.
Springer, 2008. https://doi.org/10.1007/978-3-540-68413-8_2.'
ieee: 'J. Fisher, T. A. Henzinger, M. Mateescu, and N. Piterman, “Bounded asynchrony:
Concurrency for modeling cell-cell interactions,” presented at the FMSB: Formal
Methods in Systems Biology, 2008, vol. 5054, pp. 17–32.'
ista: 'Fisher J, Henzinger TA, Mateescu M, Piterman N. 2008. Bounded asynchrony:
Concurrency for modeling cell-cell interactions. FMSB: Formal Methods in Systems
Biology, LNCS, vol. 5054, 17–32.'
mla: 'Fisher, Jasmin, et al. Bounded Asynchrony: Concurrency for Modeling Cell-Cell
Interactions. Vol. 5054, Springer, 2008, pp. 17–32, doi:10.1007/978-3-540-68413-8_2.'
short: J. Fisher, T.A. Henzinger, M. Mateescu, N. Piterman, in:, Springer, 2008,
pp. 17–32.
conference:
name: 'FMSB: Formal Methods in Systems Biology'
date_created: 2018-12-11T12:09:19Z
date_published: 2008-05-26T00:00:00Z
date_updated: 2021-01-12T07:59:27Z
day: '26'
doi: 10.1007/978-3-540-68413-8_2
extern: 1
intvolume: ' 5054'
main_file_link:
- open_access: '0'
url: http://pub.ist.ac.at/%7Etah/Publications/bounded_asynchrony.pdf
month: '05'
page: 17 - 32
publication_status: published
publisher: Springer
publist_id: '196'
quality_controlled: 0
status: public
title: 'Bounded asynchrony: Concurrency for modeling cell-cell interactions'
type: conference
volume: 5054
year: '2008'
...