---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '19796'
abstract:
- lang: eng
text: "Motivation: Boolean networks are popular dynamical models of cellular processes
in systems biology. Their attractors model phenotypes that arise from the interplay
of key regulatory subcircuits. A succession diagram (SD) describes this interplay
in a discrete analog of Waddington’s epigenetic attractor landscape that allows
for fast identification of attractors and attractor control strategies. Efficient
computational tools for studying SDs are essential for the understanding of Boolean
attractor landscapes and connecting them to their biological functions.\r\nResults:
We present a new approach to SD construction for asynchronously updated Boolean
networks, implemented in the biologist’s Boolean attractor landscape mapper, biobalm.
We compare biobalm to similar tools and find a substantial performance increase
in SD construction, attractor identification, and attractor control. We perform
the most comprehensive comparative analysis to date of the SD structure in experimentally-validated
Boolean models of cell processes and random ensembles. We find that random models
(including critical Kauffman networks) have relatively small SDs, indicating simple
decision structures. In contrast, nonrandom models from the literature are enriched
in extremely large SDs, indicating an abundance of decision points and suggesting
the presence of complex Waddington landscapes in nature.\r\nAvailability and implementation:
The tool biobalm is available online at https://github.com/jcrozum/biobalm. Further
data, scripts for testing, analysis, and figure generation are available online
at https://github.com/jcrozum/biobalm-analysis and in the reproducibility artefact
at https://doi.org/10.5281/zenodo.13854760."
acknowledgement: V.-G.T. was supported by Institut Carnot STAR, Marseille, France.
K.H.P. was supported by NSF grant MCB1715826 to Réka Albert. S.P. has received funding
from the European Union’s Horizon 2020 Research and Innovation Programme under the
Marie Sklodowska-Curie Grant Agreement No. 101034413. J.C.R. was supported by internal
departmental funds provided by Luis M. Rocha. No funding bodies had any role in
study design, analysis, decision to publish, or preparation of the article.
article_number: btaf280
article_processing_charge: Yes
article_type: original
author:
- first_name: Van Giang
full_name: Trinh, Van Giang
last_name: Trinh
- first_name: Kyu Hyong
full_name: Park, Kyu Hyong
last_name: Park
- first_name: Samuel
full_name: Pastva, Samuel
id: 07c5ea74-f61c-11ec-a664-aa7c5d957b2b
last_name: Pastva
orcid: 0000-0003-1993-0331
- first_name: Jordan C.
full_name: Rozum, Jordan C.
last_name: Rozum
citation:
ama: Trinh VG, Park KH, Pastva S, Rozum JC. Mapping the attractor landscape of Boolean
networks with biobalm. Bioinformatics. 2025;41(5). doi:10.1093/bioinformatics/btaf280
apa: Trinh, V. G., Park, K. H., Pastva, S., & Rozum, J. C. (2025). Mapping the
attractor landscape of Boolean networks with biobalm. Bioinformatics. Oxford
University Press. https://doi.org/10.1093/bioinformatics/btaf280
chicago: Trinh, Van Giang, Kyu Hyong Park, Samuel Pastva, and Jordan C. Rozum. “Mapping
the Attractor Landscape of Boolean Networks with Biobalm.” Bioinformatics.
Oxford University Press, 2025. https://doi.org/10.1093/bioinformatics/btaf280.
ieee: V. G. Trinh, K. H. Park, S. Pastva, and J. C. Rozum, “Mapping the attractor
landscape of Boolean networks with biobalm,” Bioinformatics, vol. 41, no.
5. Oxford University Press, 2025.
ista: Trinh VG, Park KH, Pastva S, Rozum JC. 2025. Mapping the attractor landscape
of Boolean networks with biobalm. Bioinformatics. 41(5), btaf280.
mla: Trinh, Van Giang, et al. “Mapping the Attractor Landscape of Boolean Networks
with Biobalm.” Bioinformatics, vol. 41, no. 5, btaf280, Oxford University
Press, 2025, doi:10.1093/bioinformatics/btaf280.
short: V.G. Trinh, K.H. Park, S. Pastva, J.C. Rozum, Bioinformatics 41 (2025).
corr_author: '1'
date_created: 2025-06-08T22:01:22Z
date_published: 2025-05-01T00:00:00Z
date_updated: 2025-09-30T12:46:33Z
day: '01'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1093/bioinformatics/btaf280
ec_funded: 1
external_id:
isi:
- '001493400600001'
pmid:
- '40327535'
file:
- access_level: open_access
checksum: fa9d68aa0f5ce37598a623c9be936f09
content_type: application/pdf
creator: dernst
date_created: 2025-06-10T07:07:45Z
date_updated: 2025-06-10T07:07:45Z
file_id: '19801'
file_name: 2025_Bioinformatics_Trinh.pdf
file_size: 2695801
relation: main_file
success: 1
file_date_updated: 2025-06-10T07:07:45Z
has_accepted_license: '1'
intvolume: ' 41'
isi: 1
issue: '5'
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Bioinformatics
publication_identifier:
eissn:
- 1367-4811
publication_status: published
publisher: Oxford University Press
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/jcrozum/biobalm
record:
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relation: research_data
status: public
scopus_import: '1'
status: public
title: Mapping the attractor landscape of Boolean networks with biobalm
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: 41
year: '2025'
...
---
OA_place: publisher
OA_type: hybrid
_id: '19854'
abstract:
- lang: eng
text: 'Asynchronous Boolean networks are a type of discrete dynamical system in
which each variable can take one of two states, and a single variable state is
updated in each time step according to pre-selected rules. Boolean networks are
popular in systems biology due to their ability to model long-term biological
phenotypes within a qualitative, predictive framework. Boolean networks model
phenotypes as attractors, which are closely linked to minimal trap spaces (inescapable
hypercubes in the system’s state space). In biological applications, attractors
and minimal trap spaces are typically in one-to-one correspondence. However, this
correspondence is not guaranteed: motif-avoidant attractors (MAAs) that lie outside
minimal trap spaces are possible. MAAs are rare and poorly understood, despite
recent efforts. In this contribution to the BMB & JMB Special Collection “Problems,
Progress and Perspectives in Mathematical and Computational Biology”, we summarize
the current state of knowledge regarding MAAs and present several novel observations
regarding their response to node deletion reductions and linear extensions of
edges. We conduct large-scale computational studies on an ensemble of 14 000 models
derived from published Boolean models of biological systems, and more than 100
million Random Boolean Networks. Our findings quantify the rarity of MAAs; in
particular, we only observed MAAs in biological models after applying standard
simplification methods, highlighting the role of network reduction in introducing
MAAs into the dynamics. We also show that MAAs are fragile to linear extensions:
in sparse networks, even a single linear node can disrupt virtually all MAAs.
Motivated by this observation, we improve the upper bound on the number of delays
needed to disrupt a motif-avoidant attractor.'
acknowledgement: Ondřej Huvar has been supported by the Czech Science Foundation grant
No. GA22-10845S. Samuel Pastva received funding from the European Union’s Horizon
2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement
No. 101034413. Kyu Hyong Park and Réka Albert have been supported by NSF grant MCB
1715826 and ARO grant 79961-SM-MUR. No funding bodies had any role in study design,
analysis, decision to publish, or preparation of the manuscript.
article_number: '11'
article_processing_charge: Yes (in subscription journal)
article_type: original
arxiv: 1
author:
- first_name: Samuel
full_name: Pastva, Samuel
id: 07c5ea74-f61c-11ec-a664-aa7c5d957b2b
last_name: Pastva
orcid: 0000-0003-1993-0331
- first_name: Kyu Hyong
full_name: Park, Kyu Hyong
last_name: Park
- first_name: Ondřej
full_name: Huvar, Ondřej
last_name: Huvar
- first_name: Jordan C.
full_name: Rozum, Jordan C.
last_name: Rozum
- first_name: Réka
full_name: Albert, Réka
last_name: Albert
citation:
ama: 'Pastva S, Park KH, Huvar O, Rozum JC, Albert R. An open problem: Why are motif-avoidant
attractors so rare in asynchronous Boolean networks? Journal of Mathematical
Biology. 2025;91. doi:10.1007/s00285-025-02235-8'
apa: 'Pastva, S., Park, K. H., Huvar, O., Rozum, J. C., & Albert, R. (2025).
An open problem: Why are motif-avoidant attractors so rare in asynchronous Boolean
networks? Journal of Mathematical Biology. Springer Nature. https://doi.org/10.1007/s00285-025-02235-8'
chicago: 'Pastva, Samuel, Kyu Hyong Park, Ondřej Huvar, Jordan C. Rozum, and Réka
Albert. “An Open Problem: Why Are Motif-Avoidant Attractors so Rare in Asynchronous
Boolean Networks?” Journal of Mathematical Biology. Springer Nature, 2025.
https://doi.org/10.1007/s00285-025-02235-8.'
ieee: 'S. Pastva, K. H. Park, O. Huvar, J. C. Rozum, and R. Albert, “An open problem:
Why are motif-avoidant attractors so rare in asynchronous Boolean networks?,”
Journal of Mathematical Biology, vol. 91. Springer Nature, 2025.'
ista: 'Pastva S, Park KH, Huvar O, Rozum JC, Albert R. 2025. An open problem: Why
are motif-avoidant attractors so rare in asynchronous Boolean networks? Journal
of Mathematical Biology. 91, 11.'
mla: 'Pastva, Samuel, et al. “An Open Problem: Why Are Motif-Avoidant Attractors
so Rare in Asynchronous Boolean Networks?” Journal of Mathematical Biology,
vol. 91, 11, Springer Nature, 2025, doi:10.1007/s00285-025-02235-8.'
short: S. Pastva, K.H. Park, O. Huvar, J.C. Rozum, R. Albert, Journal of Mathematical
Biology 91 (2025).
corr_author: '1'
date_created: 2025-06-22T22:02:05Z
date_published: 2025-06-12T00:00:00Z
date_updated: 2025-09-30T13:36:46Z
day: '12'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1007/s00285-025-02235-8
ec_funded: 1
external_id:
arxiv:
- '2410.03976'
isi:
- '001507009300001'
file:
- access_level: open_access
checksum: a385ef2662f1d0c3497ed3f2721fe594
content_type: application/pdf
creator: dernst
date_created: 2025-06-23T11:10:01Z
date_updated: 2025-06-23T11:10:01Z
file_id: '19871'
file_name: 2025_JourMathBiology_Pastva.pdf
file_size: 1243163
relation: main_file
success: 1
file_date_updated: 2025-06-23T11:10:01Z
has_accepted_license: '1'
intvolume: ' 91'
isi: 1
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Journal of Mathematical Biology
publication_identifier:
eissn:
- 1432-1416
issn:
- 0303-6812
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'An open problem: Why are motif-avoidant attractors so rare in asynchronous
Boolean networks?'
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: 91
year: '2025'
...
---
_id: '18177'
abstract:
- lang: eng
text: Partially Specified Boolean Networks (PSBNs) represent a family of Boolean
models resulting from possible interpretations of unknown update logics. Hybrid
extension of CTL (HCTL) has the power to express complex dynamical phenomena,
such as oscillations or stability. We present BNClassifier to classify Boolean
Networks corresponding to a given PSBN according to criteria specified in HCTL.
The implementation of the tool is fully symbolic (based on BDDs). The results
are visualised using the machine-learning-based technology of decision trees.
acknowledgement: The work has been supported by the Czech Science Foundation grant
No. GA22-10845S. This project has received funding from the European Union’s Horizon
2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement
No. 101034413.
alternative_title:
- LNBI
article_processing_charge: No
author:
- first_name: Nikola
full_name: Beneš, Nikola
last_name: Beneš
- first_name: Luboš
full_name: Brim, Luboš
last_name: Brim
- first_name: Ondřej
full_name: Huvar, Ondřej
last_name: Huvar
- first_name: Samuel
full_name: Pastva, Samuel
id: 07c5ea74-f61c-11ec-a664-aa7c5d957b2b
last_name: Pastva
orcid: 0000-0003-1993-0331
- first_name: David
full_name: Šafránek, David
last_name: Šafránek
citation:
ama: 'Beneš N, Brim L, Huvar O, Pastva S, Šafránek D. BNClassifier: Classifying
boolean models by dynamic properties. In: Computational Methods in Systems
Biology. Vol 14971. Springer Nature; 2024:19-26. doi:10.1007/978-3-031-71671-3_2'
apa: 'Beneš, N., Brim, L., Huvar, O., Pastva, S., & Šafránek, D. (2024). BNClassifier:
Classifying boolean models by dynamic properties. In Computational Methods
in Systems Biology (Vol. 14971, pp. 19–26). Springer Nature. https://doi.org/10.1007/978-3-031-71671-3_2'
chicago: 'Beneš, Nikola, Luboš Brim, Ondřej Huvar, Samuel Pastva, and David Šafránek.
“BNClassifier: Classifying Boolean Models by Dynamic Properties.” In Computational
Methods in Systems Biology, 14971:19–26. Springer Nature, 2024. https://doi.org/10.1007/978-3-031-71671-3_2.'
ieee: 'N. Beneš, L. Brim, O. Huvar, S. Pastva, and D. Šafránek, “BNClassifier: Classifying
boolean models by dynamic properties,” in Computational Methods in Systems
Biology, 2024, vol. 14971, pp. 19–26.'
ista: 'Beneš N, Brim L, Huvar O, Pastva S, Šafránek D. 2024. BNClassifier: Classifying
boolean models by dynamic properties. Computational Methods in Systems Biology.
, LNBI, vol. 14971, 19–26.'
mla: 'Beneš, Nikola, et al. “BNClassifier: Classifying Boolean Models by Dynamic
Properties.” Computational Methods in Systems Biology, vol. 14971, Springer
Nature, 2024, pp. 19–26, doi:10.1007/978-3-031-71671-3_2.'
short: N. Beneš, L. Brim, O. Huvar, S. Pastva, D. Šafránek, in:, Computational Methods
in Systems Biology, Springer Nature, 2024, pp. 19–26.
date_created: 2024-10-06T22:01:12Z
date_published: 2024-09-19T00:00:00Z
date_updated: 2025-09-08T09:54:27Z
day: '19'
department:
- _id: ToHe
doi: 10.1007/978-3-031-71671-3_2
ec_funded: 1
external_id:
isi:
- '001333144400002'
intvolume: ' 14971'
isi: 1
language:
- iso: eng
month: '09'
oa_version: None
page: 19-26
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Computational Methods in Systems Biology
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783031716706'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'BNClassifier: Classifying boolean models by dynamic properties'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 14971
year: '2024'
...
---
OA_place: repository
OA_type: green
_id: '19800'
abstract:
- lang: eng
text: "This archive contains all the code and data necessary to reproduce the results
presented in the \r\n\"Mapping the attractor landscape of Boolean networks\" paper."
article_processing_charge: No
author:
- first_name: Van Giang
full_name: trinh, Van Giang
last_name: trinh
- first_name: Kyu Hyong
full_name: Park, Kyu Hyong
last_name: Park
- first_name: Samuel
full_name: Pastva, Samuel
id: 07c5ea74-f61c-11ec-a664-aa7c5d957b2b
last_name: Pastva
orcid: 0000-0003-1993-0331
- first_name: Jordan
full_name: Rozum, Jordan
last_name: Rozum
citation:
ama: trinh VG, Park KH, Pastva S, Rozum J. Mapping the attractor landscape of Boolean
networks. 2024. doi:10.5281/ZENODO.13854759
apa: trinh, V. G., Park, K. H., Pastva, S., & Rozum, J. (2024). Mapping the
attractor landscape of Boolean networks. Zenodo. https://doi.org/10.5281/ZENODO.13854759
chicago: trinh, Van Giang, Kyu Hyong Park, Samuel Pastva, and Jordan Rozum. “Mapping
the Attractor Landscape of Boolean Networks.” Zenodo, 2024. https://doi.org/10.5281/ZENODO.13854759.
ieee: V. G. trinh, K. H. Park, S. Pastva, and J. Rozum, “Mapping the attractor landscape
of Boolean networks.” Zenodo, 2024.
ista: trinh VG, Park KH, Pastva S, Rozum J. 2024. Mapping the attractor landscape
of Boolean networks, Zenodo, 10.5281/ZENODO.13854759.
mla: trinh, Van Giang, et al. Mapping the Attractor Landscape of Boolean Networks.
Zenodo, 2024, doi:10.5281/ZENODO.13854759.
short: V.G. trinh, K.H. Park, S. Pastva, J. Rozum, (2024).
date_created: 2025-06-10T07:10:01Z
date_published: 2024-09-28T00:00:00Z
date_updated: 2025-09-30T12:46:33Z
day: '28'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.5281/ZENODO.13854759
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.13854760
month: '09'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '19796'
relation: used_in_publication
status: public
status: public
title: Mapping the attractor landscape of Boolean networks
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: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2024'
...
---
_id: '15321'
abstract:
- lang: eng
text: Boolean Networks (BNs) are widely used as a modeling formalism in several
domains, notably systems biology and computer science. A fundamental problem in
BN analysis is the enumeration of trap spaces, which are hypercubes in the state
space that cannot be escaped once entered. Several methods have been proposed
for enumerating trap spaces, however they often suffer from scalability and efficiency
issues, particularly for large and complex models. To our knowledge, the most
efficient and recent methods for the trap space enumeration all rely on Answer
Set Programming (ASP), which has been widely applied to the analysis of BNs. Motivated
by these considerations, our work proposes a new method for enumerating trap spaces
in BNs using ASP. We evaluate the method on a mix of 250+ real-world and 400+
randomly generated BNs, showing that it enables analysis of models beyond the
capabilities of existing tools (namely pyboolnet, mpbn, trappist, and trapmvn).
acknowledgement: This work was supported by Institut Carnot STAR, Marseille, France
and by the European Union’s Horizon 2020 research and innovation programme under
the Marie Skłodowska-Curie Grant Agreement No. 101034413.
article_processing_charge: No
author:
- first_name: Giang
full_name: Trinh, Giang
last_name: Trinh
- first_name: Belaid
full_name: Benhamou, Belaid
last_name: Benhamou
- first_name: Samuel
full_name: Pastva, Samuel
id: 07c5ea74-f61c-11ec-a664-aa7c5d957b2b
last_name: Pastva
orcid: 0000-0003-1993-0331
- first_name: Sylvain
full_name: Soliman, Sylvain
last_name: Soliman
citation:
ama: 'Trinh G, Benhamou B, Pastva S, Soliman S. Scalable enumeration of trap spaces
in boolean networks via answer set programming. In: Proceedings of the 38th
AAAI Conference on Artificial Intelligence. Vol 38. Association for the Advancement
of Artificial Intelligence; 2024:10714-10722. doi:10.1609/aaai.v38i9.28943'
apa: Trinh, G., Benhamou, B., Pastva, S., & Soliman, S. (2024). Scalable enumeration
of trap spaces in boolean networks via answer set programming. In Proceedings
of the 38th AAAI Conference on Artificial Intelligence (Vol. 38, pp. 10714–10722).
Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v38i9.28943
chicago: Trinh, Giang, Belaid Benhamou, Samuel Pastva, and Sylvain Soliman. “Scalable
Enumeration of Trap Spaces in Boolean Networks via Answer Set Programming.” In
Proceedings of the 38th AAAI Conference on Artificial Intelligence, 38:10714–22.
Association for the Advancement of Artificial Intelligence, 2024. https://doi.org/10.1609/aaai.v38i9.28943.
ieee: G. Trinh, B. Benhamou, S. Pastva, and S. Soliman, “Scalable enumeration of
trap spaces in boolean networks via answer set programming,” in Proceedings
of the 38th AAAI Conference on Artificial Intelligence, 2024, vol. 38, no.
9, pp. 10714–10722.
ista: Trinh G, Benhamou B, Pastva S, Soliman S. 2024. Scalable enumeration of trap
spaces in boolean networks via answer set programming. Proceedings of the 38th
AAAI Conference on Artificial Intelligence. vol. 38, 10714–10722.
mla: Trinh, Giang, et al. “Scalable Enumeration of Trap Spaces in Boolean Networks
via Answer Set Programming.” Proceedings of the 38th AAAI Conference on Artificial
Intelligence, vol. 38, no. 9, Association for the Advancement of Artificial
Intelligence, 2024, pp. 10714–22, doi:10.1609/aaai.v38i9.28943.
short: G. Trinh, B. Benhamou, S. Pastva, S. Soliman, in:, Proceedings of the 38th
AAAI Conference on Artificial Intelligence, Association for the Advancement of
Artificial Intelligence, 2024, pp. 10714–10722.
date_created: 2024-04-14T22:01:02Z
date_published: 2024-03-25T00:00:00Z
date_updated: 2025-04-14T07:54:55Z
day: '25'
department:
- _id: ToHe
doi: 10.1609/aaai.v38i9.28943
ec_funded: 1
intvolume: ' 38'
issue: '9'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://amu.hal.science/hal-04523118/
month: '03'
oa: 1
oa_version: Published Version
page: 10714-10722
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Proceedings of the 38th AAAI Conference on Artificial Intelligence
publication_identifier:
eissn:
- 2374-3468
isbn:
- '1577358872'
issn:
- 2159-5399
publication_status: published
publisher: Association for the Advancement of Artificial Intelligence
quality_controlled: '1'
scopus_import: '1'
status: public
title: Scalable enumeration of trap spaces in boolean networks via answer set programming
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 38
year: '2024'
...
---
_id: '14411'
abstract:
- lang: eng
text: "Partially specified Boolean networks (PSBNs) represent a promising framework
for the qualitative modelling of biological systems in which the logic of interactions
is not completely known. Phenotype control aims to stabilise the network in states
exhibiting specific traits.\r\nIn this paper, we define the phenotype control
problem in the context of asynchronous PSBNs and propose a novel semi-symbolic
algorithm for solving this problem with permanent variable perturbations."
acknowledgement: This work was supported by the Czech Foundation grant No. GA22-10845S,
Grant Agency of Masaryk University grant No. MUNI/G/1771/2020, and the European
Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie
Grant Agreement No. 101034413.
alternative_title:
- LNBI
article_processing_charge: No
author:
- first_name: Nikola
full_name: Beneš, Nikola
last_name: Beneš
- first_name: Luboš
full_name: Brim, Luboš
last_name: Brim
- first_name: Samuel
full_name: Pastva, Samuel
id: 07c5ea74-f61c-11ec-a664-aa7c5d957b2b
last_name: Pastva
orcid: 0000-0003-1993-0331
- first_name: David
full_name: Šafránek, David
last_name: Šafránek
- first_name: Eva
full_name: Šmijáková, Eva
last_name: Šmijáková
citation:
ama: 'Beneš N, Brim L, Pastva S, Šafránek D, Šmijáková E. Phenotype control of partially
specified boolean networks. In: 21st International Conference on Computational
Methods in Systems Biology. Vol 14137. Springer Nature; 2023:18-35. doi:10.1007/978-3-031-42697-1_2'
apa: 'Beneš, N., Brim, L., Pastva, S., Šafránek, D., & Šmijáková, E. (2023).
Phenotype control of partially specified boolean networks. In 21st International
Conference on Computational Methods in Systems Biology (Vol. 14137, pp. 18–35).
Luxembourg City, Luxembourg: Springer Nature. https://doi.org/10.1007/978-3-031-42697-1_2'
chicago: Beneš, Nikola, Luboš Brim, Samuel Pastva, David Šafránek, and Eva Šmijáková.
“Phenotype Control of Partially Specified Boolean Networks.” In 21st International
Conference on Computational Methods in Systems Biology, 14137:18–35. Springer
Nature, 2023. https://doi.org/10.1007/978-3-031-42697-1_2.
ieee: N. Beneš, L. Brim, S. Pastva, D. Šafránek, and E. Šmijáková, “Phenotype control
of partially specified boolean networks,” in 21st International Conference
on Computational Methods in Systems Biology, Luxembourg City, Luxembourg,
2023, vol. 14137, pp. 18–35.
ista: 'Beneš N, Brim L, Pastva S, Šafránek D, Šmijáková E. 2023. Phenotype control
of partially specified boolean networks. 21st International Conference on Computational
Methods in Systems Biology. CMSB: Computational Methods in Systems Biology, LNBI,
vol. 14137, 18–35.'
mla: Beneš, Nikola, et al. “Phenotype Control of Partially Specified Boolean Networks.”
21st International Conference on Computational Methods in Systems Biology,
vol. 14137, Springer Nature, 2023, pp. 18–35, doi:10.1007/978-3-031-42697-1_2.
short: N. Beneš, L. Brim, S. Pastva, D. Šafránek, E. Šmijáková, in:, 21st International
Conference on Computational Methods in Systems Biology, Springer Nature, 2023,
pp. 18–35.
conference:
end_date: 2023-09-15
location: Luxembourg City, Luxembourg
name: 'CMSB: Computational Methods in Systems Biology'
start_date: 2023-09-13
date_created: 2023-10-08T22:01:18Z
date_published: 2023-09-09T00:00:00Z
date_updated: 2025-09-09T14:25:46Z
day: '09'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1007/978-3-031-42697-1_2
ec_funded: 1
external_id:
isi:
- '001156280600002'
file:
- access_level: open_access
checksum: 6f71bdaedb770b52380222fd9f4d7937
content_type: application/pdf
creator: spastva
date_created: 2024-02-16T08:26:32Z
date_updated: 2024-02-16T08:26:32Z
file_id: '14997'
file_name: cmsb2023.pdf
file_size: 691582
relation: main_file
success: 1
file_date_updated: 2024-02-16T08:26:32Z
has_accepted_license: '1'
intvolume: ' 14137'
isi: 1
language:
- iso: eng
month: '09'
oa: 1
oa_version: Submitted Version
page: 18-35
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: 21st International Conference on Computational Methods in Systems Biology
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783031426964'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Phenotype control of partially specified boolean networks
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: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 14137
year: '2023'
...
---
_id: '14718'
abstract:
- lang: eng
text: 'Binary decision diagrams (BDDs) are one of the fundamental data structures
in formal methods and computer science in general. However, the performance of
BDD-based algorithms greatly depends on memory latency due to the reliance on
large hash tables and thus, by extension, on the speed of random memory access.
This hinders the full utilisation of resources available on modern CPUs, since
the absolute memory latency has not improved significantly for at least a decade.
In this paper, we explore several implementation techniques that improve the performance
of BDD manipulation either through enhanced memory locality or by partially eliminating
random memory access. On a benchmark suite of 600+ BDDs derived from real-world
applications, we demonstrate runtime that is comparable or better than parallelising
the same operations on eight CPU cores. '
acknowledgement: "This work was supported by the European Union’s Horizon 2020 research
and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101034413
and the\r\n“VAMOS” grant ERC-2020-AdG 101020093."
article_processing_charge: No
author:
- first_name: Samuel
full_name: Pastva, Samuel
id: 07c5ea74-f61c-11ec-a664-aa7c5d957b2b
last_name: Pastva
orcid: 0000-0003-1993-0331
- 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: 'Pastva S, Henzinger TA. Binary decision diagrams on modern hardware. In: Proceedings
of the 23rd Conference on Formal Methods in Computer-Aided Design. TU Vienna
Academic Press; 2023:122-131. doi:10.34727/2023/isbn.978-3-85448-060-0_20'
apa: 'Pastva, S., & Henzinger, T. A. (2023). Binary decision diagrams on modern
hardware. In Proceedings of the 23rd Conference on Formal Methods in Computer-Aided
Design (pp. 122–131). Ames, IA, United States: TU Vienna Academic Press. https://doi.org/10.34727/2023/isbn.978-3-85448-060-0_20'
chicago: Pastva, Samuel, and Thomas A Henzinger. “Binary Decision Diagrams on Modern
Hardware.” In Proceedings of the 23rd Conference on Formal Methods in Computer-Aided
Design, 122–31. TU Vienna Academic Press, 2023. https://doi.org/10.34727/2023/isbn.978-3-85448-060-0_20.
ieee: S. Pastva and T. A. Henzinger, “Binary decision diagrams on modern hardware,”
in Proceedings of the 23rd Conference on Formal Methods in Computer-Aided Design,
Ames, IA, United States, 2023, pp. 122–131.
ista: 'Pastva S, Henzinger TA. 2023. Binary decision diagrams on modern hardware.
Proceedings of the 23rd Conference on Formal Methods in Computer-Aided Design.
FMCAD: Formal Methods in Computer-Aided Design, 122–131.'
mla: Pastva, Samuel, and Thomas A. Henzinger. “Binary Decision Diagrams on Modern
Hardware.” Proceedings of the 23rd Conference on Formal Methods in Computer-Aided
Design, TU Vienna Academic Press, 2023, pp. 122–31, doi:10.34727/2023/isbn.978-3-85448-060-0_20.
short: S. Pastva, T.A. Henzinger, in:, Proceedings of the 23rd Conference on Formal
Methods in Computer-Aided Design, TU Vienna Academic Press, 2023, pp. 122–131.
conference:
end_date: 2023-10-27
location: Ames, IA, United States
name: 'FMCAD: Formal Methods in Computer-Aided Design'
start_date: 2023-10-25
corr_author: '1'
date_created: 2023-12-31T23:01:03Z
date_published: 2023-10-01T00:00:00Z
date_updated: 2025-09-09T14:04:14Z
day: '01'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.34727/2023/isbn.978-3-85448-060-0_20
ec_funded: 1
external_id:
isi:
- '001504402400020'
file:
- access_level: open_access
checksum: 818d6e13dd508f3a04f0941081022e5d
content_type: application/pdf
creator: dernst
date_created: 2024-01-02T08:14:23Z
date_updated: 2024-01-02T08:14:23Z
file_id: '14721'
file_name: 2023_FMCAD_Pastva.pdf
file_size: 524321
relation: main_file
success: 1
file_date_updated: 2024-01-02T08:14:23Z
has_accepted_license: '1'
isi: 1
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: 122-131
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
- _id: 62781420-2b32-11ec-9570-8d9b63373d4d
call_identifier: H2020
grant_number: '101020093'
name: Vigilant Algorithmic Monitoring of Software
publication: Proceedings of the 23rd Conference on Formal Methods in Computer-Aided
Design
publication_identifier:
isbn:
- '9783854480600'
publication_status: published
publisher: TU Vienna Academic Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Binary decision diagrams on modern hardware
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: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2023'
...
---
_id: '12876'
abstract:
- lang: eng
text: "Motivation: The problem of model inference is of fundamental importance to
systems biology. Logical models (e.g. Boolean networks; BNs) represent a computationally
attractive approach capable of handling large biological networks. The models
are typically inferred from experimental data. However, even with a substantial
amount of experimental data supported by some prior knowledge, existing inference
methods often focus on a small sample of admissible candidate models only.\r\n\r\nResults:
We propose Boolean network sketches as a new formal instrument for the inference
of Boolean networks. A sketch integrates (typically partial) knowledge about the
network’s topology and the update logic (obtained through, e.g. a biological knowledge
base or a literature search), as well as further assumptions about the properties
of the network’s transitions (e.g. the form of its attractor landscape), and additional
restrictions on the model dynamics given by the measured experimental data. Our
new BNs inference algorithm starts with an ‘initial’ sketch, which is extended
by adding restrictions representing experimental data to a ‘data-informed’ sketch
and subsequently computes all BNs consistent with the data-informed sketch. Our
algorithm is based on a symbolic representation and coloured model-checking. Our
approach is unique in its ability to cover a broad spectrum of knowledge and efficiently
produce a compact representation of all inferred BNs. We evaluate the method on
a non-trivial collection of real-world and simulated data."
acknowledgement: This work was partially supported by GACR [grant No. GA22-10845S];
and Grant Agency of Masaryk University [grant No. MUNI/G/1771/2020]. This work was
partially supported by European Union’s Horizon 2020 research and innovation programme
under the Marie Skłodowska-Curie [Grant Agreement No. 101034413 to S.P.].
article_number: btad158
article_processing_charge: No
article_type: original
author:
- first_name: Nikola
full_name: Beneš, Nikola
last_name: Beneš
- first_name: Luboš
full_name: Brim, Luboš
last_name: Brim
- first_name: Ondřej
full_name: Huvar, Ondřej
last_name: Huvar
- first_name: Samuel
full_name: Pastva, Samuel
id: 07c5ea74-f61c-11ec-a664-aa7c5d957b2b
last_name: Pastva
orcid: 0000-0003-1993-0331
- first_name: David
full_name: Šafránek, David
last_name: Šafránek
citation:
ama: 'Beneš N, Brim L, Huvar O, Pastva S, Šafránek D. Boolean network sketches:
A unifying framework for logical model inference. Bioinformatics. 2023;39(4).
doi:10.1093/bioinformatics/btad158'
apa: 'Beneš, N., Brim, L., Huvar, O., Pastva, S., & Šafránek, D. (2023). Boolean
network sketches: A unifying framework for logical model inference. Bioinformatics.
Oxford University Press. https://doi.org/10.1093/bioinformatics/btad158'
chicago: 'Beneš, Nikola, Luboš Brim, Ondřej Huvar, Samuel Pastva, and David Šafránek.
“Boolean Network Sketches: A Unifying Framework for Logical Model Inference.”
Bioinformatics. Oxford University Press, 2023. https://doi.org/10.1093/bioinformatics/btad158.'
ieee: 'N. Beneš, L. Brim, O. Huvar, S. Pastva, and D. Šafránek, “Boolean network
sketches: A unifying framework for logical model inference,” Bioinformatics,
vol. 39, no. 4. Oxford University Press, 2023.'
ista: 'Beneš N, Brim L, Huvar O, Pastva S, Šafránek D. 2023. Boolean network sketches:
A unifying framework for logical model inference. Bioinformatics. 39(4), btad158.'
mla: 'Beneš, Nikola, et al. “Boolean Network Sketches: A Unifying Framework for
Logical Model Inference.” Bioinformatics, vol. 39, no. 4, btad158, Oxford
University Press, 2023, doi:10.1093/bioinformatics/btad158.'
short: N. Beneš, L. Brim, O. Huvar, S. Pastva, D. Šafránek, Bioinformatics 39 (2023).
date_created: 2023-04-30T22:01:05Z
date_published: 2023-04-03T00:00:00Z
date_updated: 2025-05-14T11:06:50Z
day: '03'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1093/bioinformatics/btad158
ec_funded: 1
external_id:
isi:
- '000976610800001'
pmid:
- '37004199'
file:
- access_level: open_access
checksum: 2cb90ddf781baefddf47eac4b54e2a03
content_type: application/pdf
creator: dernst
date_created: 2023-05-02T07:39:04Z
date_updated: 2023-05-02T07:39:04Z
file_id: '12886'
file_name: 2023_Bioinformatics_Benes.pdf
file_size: 478740
relation: main_file
success: 1
file_date_updated: 2023-05-02T07:39:04Z
has_accepted_license: '1'
intvolume: ' 39'
isi: 1
issue: '4'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Bioinformatics
publication_identifier:
eissn:
- 1367-4811
publication_status: published
publisher: Oxford University Press
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://doi.org/10.5281/zenodo.7688740
scopus_import: '1'
status: public
title: 'Boolean network sketches: A unifying framework for logical model inference'
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 39
year: '2023'
...
---
_id: '13263'
abstract:
- lang: eng
text: "Motivation: Boolean networks are simple but efficient mathematical formalism
for modelling complex biological systems. However, having only two levels of activation
is sometimes not enough to fully capture the dynamics of real-world biological
systems. Hence, the need for multi-valued networks (MVNs), a generalization of
Boolean networks. Despite the importance of MVNs for modelling biological systems,
only limited progress has been made on developing theories, analysis methods,
and tools that can support them. In particular, the recent use of trap spaces
in Boolean networks made a great impact on the field of systems biology, but there
has been no similar concept defined and studied for MVNs to date.\r\n\r\nResults:
In this work, we generalize the concept of trap spaces in Boolean networks to
that in MVNs. We then develop the theory and the analysis methods for trap spaces
in MVNs. In particular, we implement all proposed methods in a Python package
called trapmvn. Not only showing the applicability of our approach via a realistic
case study, we also evaluate the time efficiency of the method on a large collection
of real-world models. The experimental results confirm the time efficiency, which
we believe enables more accurate analysis on larger and more complex multi-valued
models."
acknowledgement: This work was supported by L’Institut Carnot STAR, Marseille, France,
and by the European Union’s Horizon 2020 research and innovation programme under
the Marie Skłodowska-Curie Grant Agreement No. [101034413].
article_processing_charge: Yes
article_type: original
author:
- first_name: Van Giang
full_name: Trinh, Van Giang
last_name: Trinh
- first_name: Belaid
full_name: Benhamou, Belaid
last_name: Benhamou
- 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: Samuel
full_name: Pastva, Samuel
id: 07c5ea74-f61c-11ec-a664-aa7c5d957b2b
last_name: Pastva
orcid: 0000-0003-1993-0331
citation:
ama: 'Trinh VG, Benhamou B, Henzinger TA, Pastva S. Trap spaces of multi-valued
networks: Definition, computation, and applications. Bioinformatics. 2023;39(Supplement_1):i513-i522.
doi:10.1093/bioinformatics/btad262'
apa: 'Trinh, V. G., Benhamou, B., Henzinger, T. A., & Pastva, S. (2023). Trap
spaces of multi-valued networks: Definition, computation, and applications. Bioinformatics.
Oxford University Press. https://doi.org/10.1093/bioinformatics/btad262'
chicago: 'Trinh, Van Giang, Belaid Benhamou, Thomas A Henzinger, and Samuel Pastva.
“Trap Spaces of Multi-Valued Networks: Definition, Computation, and Applications.”
Bioinformatics. Oxford University Press, 2023. https://doi.org/10.1093/bioinformatics/btad262.'
ieee: 'V. G. Trinh, B. Benhamou, T. A. Henzinger, and S. Pastva, “Trap spaces of
multi-valued networks: Definition, computation, and applications,” Bioinformatics,
vol. 39, no. Supplement_1. Oxford University Press, pp. i513–i522, 2023.'
ista: 'Trinh VG, Benhamou B, Henzinger TA, Pastva S. 2023. Trap spaces of multi-valued
networks: Definition, computation, and applications. Bioinformatics. 39(Supplement_1),
i513–i522.'
mla: 'Trinh, Van Giang, et al. “Trap Spaces of Multi-Valued Networks: Definition,
Computation, and Applications.” Bioinformatics, vol. 39, no. Supplement_1,
Oxford University Press, 2023, pp. i513–22, doi:10.1093/bioinformatics/btad262.'
short: V.G. Trinh, B. Benhamou, T.A. Henzinger, S. Pastva, Bioinformatics 39 (2023)
i513–i522.
corr_author: '1'
date_created: 2023-07-23T22:01:12Z
date_published: 2023-06-30T00:00:00Z
date_updated: 2025-05-14T11:07:28Z
day: '30'
ddc:
- '000'
department:
- _id: ToHe
doi: 10.1093/bioinformatics/btad262
ec_funded: 1
external_id:
isi:
- '001027457000060'
pmid:
- '37387165'
file:
- access_level: open_access
checksum: ba3abe1171df1958413b7c7f957f5486
content_type: application/pdf
creator: dernst
date_created: 2023-07-31T11:09:05Z
date_updated: 2023-07-31T11:09:05Z
file_id: '13335'
file_name: 2023_Bioinformatics_Trinh.pdf
file_size: 641736
relation: main_file
success: 1
file_date_updated: 2023-07-31T11:09:05Z
has_accepted_license: '1'
intvolume: ' 39'
isi: 1
issue: Supplement_1
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
page: i513-i522
pmid: 1
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Bioinformatics
publication_identifier:
eissn:
- 1367-4811
issn:
- 1367-4803
publication_status: published
publisher: Oxford University Press
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/giang-trinh/trap-mvn
scopus_import: '1'
status: public
title: 'Trap spaces of multi-valued networks: Definition, computation, and applications'
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: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 39
year: '2023'
...