Automatic Screening for Perturbations in Boolean Networks
A common approach to address biological questions in systems biology is to simulate regulatory mechanisms using dynamic models. Among others, Boolean networks can be used to model the dynamics of regulatory processes in biology. Boolean network models allow simulating the qualitative behavior of the...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-04-01
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| Series: | Frontiers in Physiology |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/fphys.2018.00431/full |
| _version_ | 1818992985128304640 |
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| author | Julian D. Schwab Julian D. Schwab Hans A. Kestler |
| author_facet | Julian D. Schwab Julian D. Schwab Hans A. Kestler |
| author_sort | Julian D. Schwab |
| collection | DOAJ |
| description | A common approach to address biological questions in systems biology is to simulate regulatory mechanisms using dynamic models. Among others, Boolean networks can be used to model the dynamics of regulatory processes in biology. Boolean network models allow simulating the qualitative behavior of the modeled processes. A central objective in the simulation of Boolean networks is the computation of their long-term behavior—so-called attractors. These attractors are of special interest as they can often be linked to biologically relevant behaviors. Changing internal and external conditions can influence the long-term behavior of the Boolean network model. Perturbation of a Boolean network by stripping a component of the system or simulating a surplus of another element can lead to different attractors. Apparently, the number of possible perturbations and combinations of perturbations increases exponentially with the size of the network. Manually screening a set of possible components for combinations that have a desired effect on the long-term behavior can be very time consuming if not impossible. We developed a method to automatically screen for perturbations that lead to a user-specified change in the network's functioning. This method is implemented in the visual simulation framework ViSiBool utilizing satisfiability (SAT) solvers for fast exhaustive attractor search. |
| first_indexed | 2024-12-20T20:34:51Z |
| format | Article |
| id | doaj.art-9f88d70d3e84403cab2872416492ba87 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-12-20T20:34:51Z |
| publishDate | 2018-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-9f88d70d3e84403cab2872416492ba872022-12-21T19:27:16ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2018-04-01910.3389/fphys.2018.00431359721Automatic Screening for Perturbations in Boolean NetworksJulian D. Schwab0Julian D. Schwab1Hans A. Kestler2Medical Faculty, Institute of Medical Systems Biology Ulm University, Ulm, GermanyInternational Graduate School of Molecular Medicine Ulm University, Ulm, GermanyMedical Faculty, Institute of Medical Systems Biology Ulm University, Ulm, GermanyA common approach to address biological questions in systems biology is to simulate regulatory mechanisms using dynamic models. Among others, Boolean networks can be used to model the dynamics of regulatory processes in biology. Boolean network models allow simulating the qualitative behavior of the modeled processes. A central objective in the simulation of Boolean networks is the computation of their long-term behavior—so-called attractors. These attractors are of special interest as they can often be linked to biologically relevant behaviors. Changing internal and external conditions can influence the long-term behavior of the Boolean network model. Perturbation of a Boolean network by stripping a component of the system or simulating a surplus of another element can lead to different attractors. Apparently, the number of possible perturbations and combinations of perturbations increases exponentially with the size of the network. Manually screening a set of possible components for combinations that have a desired effect on the long-term behavior can be very time consuming if not impossible. We developed a method to automatically screen for perturbations that lead to a user-specified change in the network's functioning. This method is implemented in the visual simulation framework ViSiBool utilizing satisfiability (SAT) solvers for fast exhaustive attractor search.http://journal.frontiersin.org/article/10.3389/fphys.2018.00431/fullsystems biologyregulatory networksBoolean networksdynamic modelsimulationperturbation studies |
| spellingShingle | Julian D. Schwab Julian D. Schwab Hans A. Kestler Automatic Screening for Perturbations in Boolean Networks Frontiers in Physiology systems biology regulatory networks Boolean networks dynamic model simulation perturbation studies |
| title | Automatic Screening for Perturbations in Boolean Networks |
| title_full | Automatic Screening for Perturbations in Boolean Networks |
| title_fullStr | Automatic Screening for Perturbations in Boolean Networks |
| title_full_unstemmed | Automatic Screening for Perturbations in Boolean Networks |
| title_short | Automatic Screening for Perturbations in Boolean Networks |
| title_sort | automatic screening for perturbations in boolean networks |
| topic | systems biology regulatory networks Boolean networks dynamic model simulation perturbation studies |
| url | http://journal.frontiersin.org/article/10.3389/fphys.2018.00431/full |
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