CELLoGeNe - An energy landscape framework for logical networks controlling cell decisions
(2022) In iScience 25(8).- Abstract
- Experimental and computational efforts are constantly made to elucidate mechanisms controlling cell fate decisions during development and reprogramming. One powerful computational method is to consider cell commitment and reprogramming as movements in an energy landscape. Here, we develop Computation of Energy Landscapes of Logical Gene Networks (CELLoGeNe), which maps Boolean implementation of gene regulatory networks (GRNs) into energy landscapes. CELLoGeNe removes inadvertent symmetries in the energy landscapes normally arising from standard Boolean operators. Furthermore, CELLoGeNe provides tools to visualize and stochastically analyze the shapes of multi-dimensional energy landscapes corresponding to epigenetic landscapes for... (More)
- Experimental and computational efforts are constantly made to elucidate mechanisms controlling cell fate decisions during development and reprogramming. One powerful computational method is to consider cell commitment and reprogramming as movements in an energy landscape. Here, we develop Computation of Energy Landscapes of Logical Gene Networks (CELLoGeNe), which maps Boolean implementation of gene regulatory networks (GRNs) into energy landscapes. CELLoGeNe removes inadvertent symmetries in the energy landscapes normally arising from standard Boolean operators. Furthermore, CELLoGeNe provides tools to visualize and stochastically analyze the shapes of multi-dimensional energy landscapes corresponding to epigenetic landscapes for development and reprogramming. We demonstrate CELLoGeNe on two GRNs governing different aspects of induced pluripotent stem cells, identifying experimentally validated attractors and revealing potential reprogramming roadblocks. CELLoGeNe is a general framework that can be applied to various biological systems offering a broad picture of intracellular dynamics otherwise inaccessible with existing methods. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/feac332a-0e17-4499-931f-bcf7f0d0ad73
- author
- Andersson, Emil LU ; Sjö, Mattias LU ; Kaji, Keisuke and Olariu, Victor LU
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- in
- iScience
- volume
- 25
- issue
- 8
- article number
- 104743
- pages
- 28 pages
- publisher
- Elsevier
- external identifiers
-
- pmid:35942105
- ISSN
- 2589-0042
- DOI
- 10.1016/j.isci.2022.104743
- project
- Computational Science for Health and Environment
- language
- English
- LU publication?
- yes
- id
- feac332a-0e17-4499-931f-bcf7f0d0ad73
- date added to LUP
- 2022-11-30 13:14:07
- date last changed
- 2022-12-02 02:44:47
@article{feac332a-0e17-4499-931f-bcf7f0d0ad73, abstract = {{Experimental and computational efforts are constantly made to elucidate mechanisms controlling cell fate decisions during development and reprogramming. One powerful computational method is to consider cell commitment and reprogramming as movements in an energy landscape. Here, we develop Computation of Energy Landscapes of Logical Gene Networks (CELLoGeNe), which maps Boolean implementation of gene regulatory networks (GRNs) into energy landscapes. CELLoGeNe removes inadvertent symmetries in the energy landscapes normally arising from standard Boolean operators. Furthermore, CELLoGeNe provides tools to visualize and stochastically analyze the shapes of multi-dimensional energy landscapes corresponding to epigenetic landscapes for development and reprogramming. We demonstrate CELLoGeNe on two GRNs governing different aspects of induced pluripotent stem cells, identifying experimentally validated attractors and revealing potential reprogramming roadblocks. CELLoGeNe is a general framework that can be applied to various biological systems offering a broad picture of intracellular dynamics otherwise inaccessible with existing methods.}}, author = {{Andersson, Emil and Sjö, Mattias and Kaji, Keisuke and Olariu, Victor}}, issn = {{2589-0042}}, language = {{eng}}, number = {{8}}, publisher = {{Elsevier}}, series = {{iScience}}, title = {{CELLoGeNe - An energy landscape framework for logical networks controlling cell decisions}}, url = {{http://dx.doi.org/10.1016/j.isci.2022.104743}}, doi = {{10.1016/j.isci.2022.104743}}, volume = {{25}}, year = {{2022}}, }