Cargando…
Inferring signaling pathways with probabilistic programming
MOTIVATION: Cells regulate themselves via dizzyingly complex biochemical processes called signaling pathways. These are usually depicted as a network, where nodes represent proteins and edges indicate their influence on each other. In order to understand diseases and therapies at the cellular level,...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773483/ https://www.ncbi.nlm.nih.gov/pubmed/33381832 http://dx.doi.org/10.1093/bioinformatics/btaa861 |
_version_ | 1783630054982418432 |
---|---|
author | Merrell, David Gitter, Anthony |
author_facet | Merrell, David Gitter, Anthony |
author_sort | Merrell, David |
collection | PubMed |
description | MOTIVATION: Cells regulate themselves via dizzyingly complex biochemical processes called signaling pathways. These are usually depicted as a network, where nodes represent proteins and edges indicate their influence on each other. In order to understand diseases and therapies at the cellular level, it is crucial to have an accurate understanding of the signaling pathways at work. Since signaling pathways can be modified by disease, the ability to infer signaling pathways from condition- or patient-specific data is highly valuable. A variety of techniques exist for inferring signaling pathways. We build on past works that formulate signaling pathway inference as a Dynamic Bayesian Network structure estimation problem on phosphoproteomic time course data. We take a Bayesian approach, using Markov Chain Monte Carlo to estimate a posterior distribution over possible Dynamic Bayesian Network structures. Our primary contributions are (i) a novel proposal distribution that efficiently samples sparse graphs and (ii) the relaxation of common restrictive modeling assumptions. RESULTS: We implement our method, named Sparse Signaling Pathway Sampling, in Julia using the Gen probabilistic programming language. Probabilistic programming is a powerful methodology for building statistical models. The resulting code is modular, extensible and legible. The Gen language, in particular, allows us to customize our inference procedure for biological graphs and ensure efficient sampling. We evaluate our algorithm on simulated data and the HPN-DREAM pathway reconstruction challenge, comparing our performance against a variety of baseline methods. Our results demonstrate the vast potential for probabilistic programming, and Gen specifically, for biological network inference. AVAILABILITY AND IMPLEMENTATION: Find the full codebase at https://github.com/gitter-lab/ssps. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-7773483 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-77734832021-01-05 Inferring signaling pathways with probabilistic programming Merrell, David Gitter, Anthony Bioinformatics Systems MOTIVATION: Cells regulate themselves via dizzyingly complex biochemical processes called signaling pathways. These are usually depicted as a network, where nodes represent proteins and edges indicate their influence on each other. In order to understand diseases and therapies at the cellular level, it is crucial to have an accurate understanding of the signaling pathways at work. Since signaling pathways can be modified by disease, the ability to infer signaling pathways from condition- or patient-specific data is highly valuable. A variety of techniques exist for inferring signaling pathways. We build on past works that formulate signaling pathway inference as a Dynamic Bayesian Network structure estimation problem on phosphoproteomic time course data. We take a Bayesian approach, using Markov Chain Monte Carlo to estimate a posterior distribution over possible Dynamic Bayesian Network structures. Our primary contributions are (i) a novel proposal distribution that efficiently samples sparse graphs and (ii) the relaxation of common restrictive modeling assumptions. RESULTS: We implement our method, named Sparse Signaling Pathway Sampling, in Julia using the Gen probabilistic programming language. Probabilistic programming is a powerful methodology for building statistical models. The resulting code is modular, extensible and legible. The Gen language, in particular, allows us to customize our inference procedure for biological graphs and ensure efficient sampling. We evaluate our algorithm on simulated data and the HPN-DREAM pathway reconstruction challenge, comparing our performance against a variety of baseline methods. Our results demonstrate the vast potential for probabilistic programming, and Gen specifically, for biological network inference. AVAILABILITY AND IMPLEMENTATION: Find the full codebase at https://github.com/gitter-lab/ssps. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-12-29 /pmc/articles/PMC7773483/ /pubmed/33381832 http://dx.doi.org/10.1093/bioinformatics/btaa861 Text en © The Author(s) 2020. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Systems Merrell, David Gitter, Anthony Inferring signaling pathways with probabilistic programming |
title | Inferring signaling pathways with probabilistic programming |
title_full | Inferring signaling pathways with probabilistic programming |
title_fullStr | Inferring signaling pathways with probabilistic programming |
title_full_unstemmed | Inferring signaling pathways with probabilistic programming |
title_short | Inferring signaling pathways with probabilistic programming |
title_sort | inferring signaling pathways with probabilistic programming |
topic | Systems |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773483/ https://www.ncbi.nlm.nih.gov/pubmed/33381832 http://dx.doi.org/10.1093/bioinformatics/btaa861 |
work_keys_str_mv | AT merrelldavid inferringsignalingpathwayswithprobabilisticprogramming AT gitteranthony inferringsignalingpathwayswithprobabilisticprogramming |