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CELLoGeNe - An energy landscape framework for logical networks controlling cell decisions

Andersson, Emil LU orcid ; Sjö, Mattias LU ; Kaji, Keisuke and Olariu, Victor LU (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)
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author
; ; and
organization
publishing date
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}},
}