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From word models to executable models of signaling networks using automated assembly

Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to building computational models directly from natural...

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Autores principales: Gyori, Benjamin M, Bachman, John A, Subramanian, Kartik, Muhlich, Jeremy L, Galescu, Lucian, Sorger, Peter K
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5731347/
https://www.ncbi.nlm.nih.gov/pubmed/29175850
http://dx.doi.org/10.15252/msb.20177651
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author Gyori, Benjamin M
Bachman, John A
Subramanian, Kartik
Muhlich, Jeremy L
Galescu, Lucian
Sorger, Peter K
author_facet Gyori, Benjamin M
Bachman, John A
Subramanian, Kartik
Muhlich, Jeremy L
Galescu, Lucian
Sorger, Peter K
author_sort Gyori, Benjamin M
collection PubMed
description Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to building computational models directly from natural language using automated assembly. Molecular mechanisms described in simple English are read by natural language processing algorithms, converted into an intermediate representation, and assembled into executable or network models. We have implemented this approach in the Integrated Network and Dynamical Reasoning Assembler (INDRA), which draws on existing natural language processing systems as well as pathway information in Pathway Commons and other online resources. We demonstrate the use of INDRA and natural language to model three biological processes of increasing scope: (i) p53 dynamics in response to DNA damage, (ii) adaptive drug resistance in BRAF‐V600E‐mutant melanomas, and (iii) the RAS signaling pathway. The use of natural language makes the task of developing a model more efficient and it increases model transparency, thereby promoting collaboration with the broader biology community.
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spelling pubmed-57313472017-12-18 From word models to executable models of signaling networks using automated assembly Gyori, Benjamin M Bachman, John A Subramanian, Kartik Muhlich, Jeremy L Galescu, Lucian Sorger, Peter K Mol Syst Biol Articles Word models (natural language descriptions of molecular mechanisms) are a common currency in spoken and written communication in biomedicine but are of limited use in predicting the behavior of complex biological networks. We present an approach to building computational models directly from natural language using automated assembly. Molecular mechanisms described in simple English are read by natural language processing algorithms, converted into an intermediate representation, and assembled into executable or network models. We have implemented this approach in the Integrated Network and Dynamical Reasoning Assembler (INDRA), which draws on existing natural language processing systems as well as pathway information in Pathway Commons and other online resources. We demonstrate the use of INDRA and natural language to model three biological processes of increasing scope: (i) p53 dynamics in response to DNA damage, (ii) adaptive drug resistance in BRAF‐V600E‐mutant melanomas, and (iii) the RAS signaling pathway. The use of natural language makes the task of developing a model more efficient and it increases model transparency, thereby promoting collaboration with the broader biology community. John Wiley and Sons Inc. 2017-11-24 /pmc/articles/PMC5731347/ /pubmed/29175850 http://dx.doi.org/10.15252/msb.20177651 Text en © 2017 The Authors. Published under the terms of the CC BY 4.0 license This is an open access article under the terms of the Creative Commons Attribution 4.0 (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Gyori, Benjamin M
Bachman, John A
Subramanian, Kartik
Muhlich, Jeremy L
Galescu, Lucian
Sorger, Peter K
From word models to executable models of signaling networks using automated assembly
title From word models to executable models of signaling networks using automated assembly
title_full From word models to executable models of signaling networks using automated assembly
title_fullStr From word models to executable models of signaling networks using automated assembly
title_full_unstemmed From word models to executable models of signaling networks using automated assembly
title_short From word models to executable models of signaling networks using automated assembly
title_sort from word models to executable models of signaling networks using automated assembly
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5731347/
https://www.ncbi.nlm.nih.gov/pubmed/29175850
http://dx.doi.org/10.15252/msb.20177651
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