Protocol for stratification of triple-negative breast cancer patients using in silico signaling dynamics

Personalized kinetic models can predict potential biomarkers and drug targets. Here, we provide a step-by-step approach for building an executable mathematical model from text and integrating transcriptomic datasets. We additionally describe the steps to personalize the mechanistic model and to stra...

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Detalles Bibliográficos
Autores principales: Imoto, Hiroaki, Yamashiro, Sawa, Murakami, Ken, Okada, Mariko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Protocol
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9389415/
https://www.ncbi.nlm.nih.gov/pubmed/35990741
http://dx.doi.org/10.1016/j.xpro.2022.101619
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author Imoto, Hiroaki
Yamashiro, Sawa
Murakami, Ken
Okada, Mariko
author_facet Imoto, Hiroaki
Yamashiro, Sawa
Murakami, Ken
Okada, Mariko
author_sort Imoto, Hiroaki
collection PubMed
description Personalized kinetic models can predict potential biomarkers and drug targets. Here, we provide a step-by-step approach for building an executable mathematical model from text and integrating transcriptomic datasets. We additionally describe the steps to personalize the mechanistic model and to stratify patients with triple-negative breast cancer (TNBC) based on in silico signaling dynamics. This protocol can also be applied to any signaling pathway for patient-specific modeling. For complete details on the use and execution of this protocol, please refer to Imoto et al. (2022).
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spelling pubmed-93894152022-08-20 Protocol for stratification of triple-negative breast cancer patients using in silico signaling dynamics Imoto, Hiroaki Yamashiro, Sawa Murakami, Ken Okada, Mariko STAR Protoc Protocol Personalized kinetic models can predict potential biomarkers and drug targets. Here, we provide a step-by-step approach for building an executable mathematical model from text and integrating transcriptomic datasets. We additionally describe the steps to personalize the mechanistic model and to stratify patients with triple-negative breast cancer (TNBC) based on in silico signaling dynamics. This protocol can also be applied to any signaling pathway for patient-specific modeling. For complete details on the use and execution of this protocol, please refer to Imoto et al. (2022). Elsevier 2022-08-11 /pmc/articles/PMC9389415/ /pubmed/35990741 http://dx.doi.org/10.1016/j.xpro.2022.101619 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Imoto, Hiroaki
Yamashiro, Sawa
Murakami, Ken
Okada, Mariko
Protocol for stratification of triple-negative breast cancer patients using in silico signaling dynamics
title Protocol for stratification of triple-negative breast cancer patients using in silico signaling dynamics
title_full Protocol for stratification of triple-negative breast cancer patients using in silico signaling dynamics
title_fullStr Protocol for stratification of triple-negative breast cancer patients using in silico signaling dynamics
title_full_unstemmed Protocol for stratification of triple-negative breast cancer patients using in silico signaling dynamics
title_short Protocol for stratification of triple-negative breast cancer patients using in silico signaling dynamics
title_sort protocol for stratification of triple-negative breast cancer patients using in silico signaling dynamics
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9389415/
https://www.ncbi.nlm.nih.gov/pubmed/35990741
http://dx.doi.org/10.1016/j.xpro.2022.101619
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