Unsupervised logic-based mechanism inference for network-driven biological processes.

Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Her...

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Autores principales:	Martina Prugger, Lukas Einkemmer, Samantha P Beik, Perry T Wasdin, Leonard A Harris, Carlos F Lopez
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/e3bf50ee75e745d68f3898b25e571aef
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spelling	oai:doaj.org-article:e3bf50ee75e745d68f3898b25e571aef2021-12-02T19:58:14ZUnsupervised logic-based mechanism inference for network-driven biological processes.1553-734X1553-735810.1371/journal.pcbi.1009035https://doaj.org/article/e3bf50ee75e745d68f3898b25e571aef2021-06-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009035https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Here we present a generalizable, unsupervised approach to generate parameter-free, logic-based models of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify optimized logic rules that link two states. Our approach comprises two steps. First, a model with no prior knowledge except for the mapping between initial and attractor states is built. We then employ biological constraints to improve model fidelity. Our algorithm automatically recovers the relevant dynamics for the explored models and recapitulates key aspects of the biochemical species concentration dynamics in the original model. We present the advantages and limitations of our work and discuss how our approach could be used to infer logic-based mechanisms of signaling, gene-regulatory, or other input-output processes describable by the Boolean formalism.Martina PruggerLukas EinkemmerSamantha P BeikPerry T WasdinLeonard A HarrisCarlos F LopezPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 6, p e1009035 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Martina Prugger Lukas Einkemmer Samantha P Beik Perry T Wasdin Leonard A Harris Carlos F Lopez Unsupervised logic-based mechanism inference for network-driven biological processes.
description	Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Here we present a generalizable, unsupervised approach to generate parameter-free, logic-based models of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify optimized logic rules that link two states. Our approach comprises two steps. First, a model with no prior knowledge except for the mapping between initial and attractor states is built. We then employ biological constraints to improve model fidelity. Our algorithm automatically recovers the relevant dynamics for the explored models and recapitulates key aspects of the biochemical species concentration dynamics in the original model. We present the advantages and limitations of our work and discuss how our approach could be used to infer logic-based mechanisms of signaling, gene-regulatory, or other input-output processes describable by the Boolean formalism.
format	article
author	Martina Prugger Lukas Einkemmer Samantha P Beik Perry T Wasdin Leonard A Harris Carlos F Lopez
author_facet	Martina Prugger Lukas Einkemmer Samantha P Beik Perry T Wasdin Leonard A Harris Carlos F Lopez
author_sort	Martina Prugger
title	Unsupervised logic-based mechanism inference for network-driven biological processes.
title_short	Unsupervised logic-based mechanism inference for network-driven biological processes.
title_full	Unsupervised logic-based mechanism inference for network-driven biological processes.
title_fullStr	Unsupervised logic-based mechanism inference for network-driven biological processes.
title_full_unstemmed	Unsupervised logic-based mechanism inference for network-driven biological processes.
title_sort	unsupervised logic-based mechanism inference for network-driven biological processes.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/e3bf50ee75e745d68f3898b25e571aef
work_keys_str_mv	AT martinaprugger unsupervisedlogicbasedmechanisminferencefornetworkdrivenbiologicalprocesses AT lukaseinkemmer unsupervisedlogicbasedmechanisminferencefornetworkdrivenbiologicalprocesses AT samanthapbeik unsupervisedlogicbasedmechanisminferencefornetworkdrivenbiologicalprocesses AT perrytwasdin unsupervisedlogicbasedmechanisminferencefornetworkdrivenbiologicalprocesses AT leonardaharris unsupervisedlogicbasedmechanisminferencefornetworkdrivenbiologicalprocesses AT carlosflopez unsupervisedlogicbasedmechanisminferencefornetworkdrivenbiologicalprocesses
_version_	1718375811758686208

Unsupervised logic-based mechanism inference for network-driven biological processes.

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