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Biologically informed NeuralODEs for genome-wide regulatory dynamics
Models that are formulated as ordinary differential equations (ODEs) can accurately explain temporal gene expression patterns and promise to yield new insights into important cellular processes, disease progression, and intervention design. Learning such ODEs is challenging, since we want to predict...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Journal Experts
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055646/ https://www.ncbi.nlm.nih.gov/pubmed/36993392 http://dx.doi.org/10.21203/rs.3.rs-2675584/v1 |
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author | Hossain, Intekhab Fanfani, Viola Quackenbush, John Burkholz, Rebekka |
author_facet | Hossain, Intekhab Fanfani, Viola Quackenbush, John Burkholz, Rebekka |
author_sort | Hossain, Intekhab |
collection | PubMed |
description | Models that are formulated as ordinary differential equations (ODEs) can accurately explain temporal gene expression patterns and promise to yield new insights into important cellular processes, disease progression, and intervention design. Learning such ODEs is challenging, since we want to predict the evolution of gene expression in a way that accurately encodes the causal gene-regulatory network (GRN) governing the dynamics and the nonlinear functional relationships between genes. Most widely used ODE estimation methods either impose too many parametric restrictions or are not guided by meaningful biological insights, both of which impedes scalability and/or explainability. To overcome these limitations, we developed PHOENIX, a modeling framework based on neural ordinary differential equations (NeuralODEs) and Hill-Langmuir kinetics, that can flexibly incorporate prior domain knowledge and biological constraints to promote sparse, biologically interpretable representations of ODEs. We test accuracy of PHOENIX in a series of in silico experiments benchmarking it against several currently used tools for ODE estimation. We also demonstrate PHOENIX’s flexibility by studying oscillating expression data from synchronized yeast cells and assess its scalability by modelling genome-scale breast cancer expression for samples ordered in pseudotime. Finally, we show how the combination of user-defined prior knowledge and functional forms from systems biology allows PHOENIX to encode key properties of the underlying GRN, and subsequently predict expression patterns in a biologically explainable way. |
format | Online Article Text |
id | pubmed-10055646 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Journal Experts |
record_format | MEDLINE/PubMed |
spelling | pubmed-100556462023-03-30 Biologically informed NeuralODEs for genome-wide regulatory dynamics Hossain, Intekhab Fanfani, Viola Quackenbush, John Burkholz, Rebekka Res Sq Article Models that are formulated as ordinary differential equations (ODEs) can accurately explain temporal gene expression patterns and promise to yield new insights into important cellular processes, disease progression, and intervention design. Learning such ODEs is challenging, since we want to predict the evolution of gene expression in a way that accurately encodes the causal gene-regulatory network (GRN) governing the dynamics and the nonlinear functional relationships between genes. Most widely used ODE estimation methods either impose too many parametric restrictions or are not guided by meaningful biological insights, both of which impedes scalability and/or explainability. To overcome these limitations, we developed PHOENIX, a modeling framework based on neural ordinary differential equations (NeuralODEs) and Hill-Langmuir kinetics, that can flexibly incorporate prior domain knowledge and biological constraints to promote sparse, biologically interpretable representations of ODEs. We test accuracy of PHOENIX in a series of in silico experiments benchmarking it against several currently used tools for ODE estimation. We also demonstrate PHOENIX’s flexibility by studying oscillating expression data from synchronized yeast cells and assess its scalability by modelling genome-scale breast cancer expression for samples ordered in pseudotime. Finally, we show how the combination of user-defined prior knowledge and functional forms from systems biology allows PHOENIX to encode key properties of the underlying GRN, and subsequently predict expression patterns in a biologically explainable way. American Journal Experts 2023-03-14 /pmc/articles/PMC10055646/ /pubmed/36993392 http://dx.doi.org/10.21203/rs.3.rs-2675584/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Hossain, Intekhab Fanfani, Viola Quackenbush, John Burkholz, Rebekka Biologically informed NeuralODEs for genome-wide regulatory dynamics |
title | Biologically informed NeuralODEs for genome-wide regulatory dynamics |
title_full | Biologically informed NeuralODEs for genome-wide regulatory dynamics |
title_fullStr | Biologically informed NeuralODEs for genome-wide regulatory dynamics |
title_full_unstemmed | Biologically informed NeuralODEs for genome-wide regulatory dynamics |
title_short | Biologically informed NeuralODEs for genome-wide regulatory dynamics |
title_sort | biologically informed neuralodes for genome-wide regulatory dynamics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055646/ https://www.ncbi.nlm.nih.gov/pubmed/36993392 http://dx.doi.org/10.21203/rs.3.rs-2675584/v1 |
work_keys_str_mv | AT hossainintekhab biologicallyinformedneuralodesforgenomewideregulatorydynamics AT fanfaniviola biologicallyinformedneuralodesforgenomewideregulatorydynamics AT quackenbushjohn biologicallyinformedneuralodesforgenomewideregulatorydynamics AT burkholzrebekka biologicallyinformedneuralodesforgenomewideregulatorydynamics |