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Not just a colourful metaphor: modelling the landscape of cellular development using Hopfield networks
The epigenetic landscape was introduced by Conrad Waddington as a metaphor of cellular development. Like a ball rolling down a hillside is channelled through a succession of valleys until it reaches the bottom, cells follow specific trajectories from a pluripotent state to a committed state. Transcr...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516853/ https://www.ncbi.nlm.nih.gov/pubmed/28725466 http://dx.doi.org/10.1038/npjsba.2016.1 |
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author | Fard, Atefeh Taherian Srihari, Sriganesh Mar, Jessica C Ragan, Mark A |
author_facet | Fard, Atefeh Taherian Srihari, Sriganesh Mar, Jessica C Ragan, Mark A |
author_sort | Fard, Atefeh Taherian |
collection | PubMed |
description | The epigenetic landscape was introduced by Conrad Waddington as a metaphor of cellular development. Like a ball rolling down a hillside is channelled through a succession of valleys until it reaches the bottom, cells follow specific trajectories from a pluripotent state to a committed state. Transcription factors (TFs) interacting as a network (the gene regulatory network (GRN)) orchestrate this developmental process within each cell. Here, we quantitatively model the epigenetic landscape using a kind of artificial neural network called the Hopfield network (HN). An HN is composed of nodes (genes/TFs) and weighted undirected edges, resulting in a weight matrix (W) that stores interactions among the nodes over the entire network. We used gene co-expression to compute the edge weights. Through W, we then associate an energy score (E) to each input pattern (pattern of co-expression for a specific developmental stage) such that each pattern has a specific E. We propose that, based on the co-expression values stored in W, HN associates lower E values to stable phenotypic states and higher E to transient states. We validate our model using time course gene-expression data sets representing stages of development across 12 biological processes including differentiation of human embryonic stem cells into specialized cells, differentiation of THP1 monocytes to macrophages during immune response and trans-differentiation of epithelial to mesenchymal cells in cancer. We observe that transient states have higher energy than the stable phenotypic states, yielding an arc-shaped trajectory. This relationship was confirmed by perturbation analysis. HNs offer an attractive framework for quantitative modelling of cell differentiation (as a landscape) from empirical data. Using HNs, we identify genes and TFs that drive cell-fate transitions, and gain insight into the global dynamics of GRNs. |
format | Online Article Text |
id | pubmed-5516853 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-55168532017-07-19 Not just a colourful metaphor: modelling the landscape of cellular development using Hopfield networks Fard, Atefeh Taherian Srihari, Sriganesh Mar, Jessica C Ragan, Mark A NPJ Syst Biol Appl Article The epigenetic landscape was introduced by Conrad Waddington as a metaphor of cellular development. Like a ball rolling down a hillside is channelled through a succession of valleys until it reaches the bottom, cells follow specific trajectories from a pluripotent state to a committed state. Transcription factors (TFs) interacting as a network (the gene regulatory network (GRN)) orchestrate this developmental process within each cell. Here, we quantitatively model the epigenetic landscape using a kind of artificial neural network called the Hopfield network (HN). An HN is composed of nodes (genes/TFs) and weighted undirected edges, resulting in a weight matrix (W) that stores interactions among the nodes over the entire network. We used gene co-expression to compute the edge weights. Through W, we then associate an energy score (E) to each input pattern (pattern of co-expression for a specific developmental stage) such that each pattern has a specific E. We propose that, based on the co-expression values stored in W, HN associates lower E values to stable phenotypic states and higher E to transient states. We validate our model using time course gene-expression data sets representing stages of development across 12 biological processes including differentiation of human embryonic stem cells into specialized cells, differentiation of THP1 monocytes to macrophages during immune response and trans-differentiation of epithelial to mesenchymal cells in cancer. We observe that transient states have higher energy than the stable phenotypic states, yielding an arc-shaped trajectory. This relationship was confirmed by perturbation analysis. HNs offer an attractive framework for quantitative modelling of cell differentiation (as a landscape) from empirical data. Using HNs, we identify genes and TFs that drive cell-fate transitions, and gain insight into the global dynamics of GRNs. Nature Publishing Group 2016-02-18 /pmc/articles/PMC5516853/ /pubmed/28725466 http://dx.doi.org/10.1038/npjsba.2016.1 Text en Copyright © 2016 The Systems Biology Institute/Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Fard, Atefeh Taherian Srihari, Sriganesh Mar, Jessica C Ragan, Mark A Not just a colourful metaphor: modelling the landscape of cellular development using Hopfield networks |
title | Not just a colourful metaphor: modelling the landscape of cellular development using Hopfield networks |
title_full | Not just a colourful metaphor: modelling the landscape of cellular development using Hopfield networks |
title_fullStr | Not just a colourful metaphor: modelling the landscape of cellular development using Hopfield networks |
title_full_unstemmed | Not just a colourful metaphor: modelling the landscape of cellular development using Hopfield networks |
title_short | Not just a colourful metaphor: modelling the landscape of cellular development using Hopfield networks |
title_sort | not just a colourful metaphor: modelling the landscape of cellular development using hopfield networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516853/ https://www.ncbi.nlm.nih.gov/pubmed/28725466 http://dx.doi.org/10.1038/npjsba.2016.1 |
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