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A deterministic method for estimating free energy genetic network landscapes with applications to cell commitment and reprogramming paths

Olariu, Victor LU ; Manesso, Erica LU and Peterson, Carsten LU (2017) In Royal Society Open Science 4(6).
Abstract

Depicting developmental processes as movements in free energy genetic landscapes is an illustrative tool. However, exploring such landscapes to obtain quantitative or even qualitative predictions is hampered by the lack of free energy functions corresponding to the biochemical Michaelis– Menten or Hill rate equations for the dynamics. Being armed with energy landscapes defined by a network and its interactions would open up the possibility of swiftly identifying cell states and computing optimal paths, including those of cell reprogramming, thereby avoiding exhaustive trial-and-error simulations with rate equations for different parameter sets. It turns out that sigmoidal rate equations do have approximate free energy associations. With... (More)

Depicting developmental processes as movements in free energy genetic landscapes is an illustrative tool. However, exploring such landscapes to obtain quantitative or even qualitative predictions is hampered by the lack of free energy functions corresponding to the biochemical Michaelis– Menten or Hill rate equations for the dynamics. Being armed with energy landscapes defined by a network and its interactions would open up the possibility of swiftly identifying cell states and computing optimal paths, including those of cell reprogramming, thereby avoiding exhaustive trial-and-error simulations with rate equations for different parameter sets. It turns out that sigmoidal rate equations do have approximate free energy associations. With this replacement of rate equations, we develop a deterministic method for estimating the free energy surfaces of systems of interacting genes at different noise levels or temperatures. Once such free energy landscape estimates have been established, we adapt a shortest path algorithm to determine optimal routes in the landscapes. We explore the method on three circuits for haematopoiesis and embryonic stem cell development for commitment and reprogramming scenarios and illustrate how the method can be used to determine sequential steps for onsets of external factors, essential for efficient reprogramming.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Deterministic models, Energy landscape, Reprogramming, Stem cell commitment
in
Royal Society Open Science
volume
4
issue
6
publisher
Royal Society
external identifiers
  • scopus:85020408874
  • wos:000404843200003
ISSN
2054-5703
DOI
10.1098/rsos.160765
language
English
LU publication?
yes
id
4649bfd4-5124-477f-9033-02e4f94ccc25
date added to LUP
2017-06-27 14:12:02
date last changed
2017-09-18 11:43:15
@article{4649bfd4-5124-477f-9033-02e4f94ccc25,
  abstract     = {<p>Depicting developmental processes as movements in free energy genetic landscapes is an illustrative tool. However, exploring such landscapes to obtain quantitative or even qualitative predictions is hampered by the lack of free energy functions corresponding to the biochemical Michaelis– Menten or Hill rate equations for the dynamics. Being armed with energy landscapes defined by a network and its interactions would open up the possibility of swiftly identifying cell states and computing optimal paths, including those of cell reprogramming, thereby avoiding exhaustive trial-and-error simulations with rate equations for different parameter sets. It turns out that sigmoidal rate equations do have approximate free energy associations. With this replacement of rate equations, we develop a deterministic method for estimating the free energy surfaces of systems of interacting genes at different noise levels or temperatures. Once such free energy landscape estimates have been established, we adapt a shortest path algorithm to determine optimal routes in the landscapes. We explore the method on three circuits for haematopoiesis and embryonic stem cell development for commitment and reprogramming scenarios and illustrate how the method can be used to determine sequential steps for onsets of external factors, essential for efficient reprogramming.</p>},
  articleno    = {160765},
  author       = {Olariu, Victor and Manesso, Erica and Peterson, Carsten},
  issn         = {2054-5703},
  keyword      = {Deterministic models,Energy landscape,Reprogramming,Stem cell commitment},
  language     = {eng},
  month        = {06},
  number       = {6},
  publisher    = {Royal Society},
  series       = {Royal Society Open Science},
  title        = {A deterministic method for estimating free energy genetic network landscapes with applications to cell commitment and reprogramming paths},
  url          = {http://dx.doi.org/10.1098/rsos.160765},
  volume       = {4},
  year         = {2017},
}