Identification of dynamic driver sets controlling phenotypical landscapes.
Boolean network
Cellular phenotype control
Dynamic driver
Implicants
Intervention targets
Network dynamics
Journal
Computational and structural biotechnology journal
ISSN: 2001-0370
Titre abrégé: Comput Struct Biotechnol J
Pays: Netherlands
ID NLM: 101585369
Informations de publication
Date de publication:
2022
2022
Historique:
received:
10
01
2022
revised:
30
03
2022
accepted:
30
03
2022
entrez:
25
4
2022
pubmed:
26
4
2022
medline:
26
4
2022
Statut:
epublish
Résumé
Controlling phenotypical landscapes is of vital interest to modern biology. This task becomes highly demanding because cellular decisions involve complex networks engaging in crosstalk interactions. Previous work on control theory indicates that small sets of compounds can control single phenotypes. However, a dynamic approach is missing to determine the drivers of the whole network dynamics. By analyzing 35 biologically motivated Boolean networks, we developed a method to identify small sets of compounds sufficient to decide on the entire phenotypical landscape. These compounds do not strictly prefer highly related compounds and show a smaller impact on the stability of the attractor landscape. The dynamic driver sets include many intervention targets and cellular reprogramming drivers in human networks. Finally, by using a new comprehensive model of colorectal cancer, we provide a complete workflow on how to implement our approach to shift from
Identifiants
pubmed: 35465155
doi: 10.1016/j.csbj.2022.03.034
pii: S2001-0370(22)00109-X
pmc: PMC9010550
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1603-1617Informations de copyright
© 2022 The Author(s).
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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