Automated deep abstractions for stochastic chemical reaction networks
Automated deep abstractions for stochastic chemical reaction networks
Loading...
Date
2021
Authors
Editors
Journal ISSN
Electronic ISSN
ISBN
Bibliographical data
Publisher
Series
URI (citable link)
DOI (citable link)
International patent number
Link to the license
EU project number
Project
Open Access publication
Title in another language
Publication type
Journal article
Publication status
Published
Published in
Information and Computation ; 281 (2021). - pp. 104788. - Elsevier. - ISSN 0890-5401. - eISSN 1090-2651
Abstract
Predicting stochastic cellular dynamics as emerging from the mechanistic models of molecular interactions is a long-standing challenge in systems biology: low-level chemical reaction network (CRN) models give rise to a highly-dimensional continuous-time Markov chain (CTMC) which is computationally demanding and often prohibitive to analyse in practice. A recently proposed abstraction method uses deep learning to replace this CTMC with a discrete-time continuous-space process, by training a mixture density deep neural network with traces sampled at regular time intervals (which can be obtained either by simulating a given CRN or as time-series data from experiment). The major advantage of such abstraction is that it produces a computational model that is dramatically cheaper to execute, while it preserves the statistical features of the training data. In general, the abstraction accuracy improves with the amount of training data. However, depending on the CRN, the overall quality of the method – the efficiency gain and abstraction accuracy – will also depend on the choice of neural network architecture given by hyper-parameters such as the layer types and connections between them. As a consequence, in practice, the modeller has to take care of finding the suitable architecture manually, for each given CRN, through a tedious and time-consuming trial-and-error cycle. In this paper, we propose to further automatise deep abstractions for stochastic CRNs, through learning the neural network architecture along with learning the transition kernel of the abstract process. Automated search of the architecture makes the method applicable directly to any given CRN, which is time-saving for deep learning experts and crucial for non-specialists. We implement the method and demonstrate its performance on a number of representative CRNs with multi-modal emergent phenotypes. Moreover, we showcase that deep abstractions can be used for efficient multi-scale simulations, which are otherwise computationally intractable. To this end, we define a scenario where multiple CRN instances interact across a spatial grid via shared species. Finally, we discuss the limitations and challenges arising when using deep abstractions.
Summary in another language
Subject (DDC)
004 Computer Science
Keywords
Model abstraction, Stochastic simulation, Chemical Reaction Networks, Deep learning, Neural architecture search
Conference
Review
undefined / . - undefined, undefined. - (undefined; undefined)
Cite This
ISO 690
REPIN, Denis, Tatjana PETROV, 2021. Automated deep abstractions for stochastic chemical reaction networks. In: Information and Computation. Elsevier. 281, pp. 104788. ISSN 0890-5401. eISSN 1090-2651. Available under: doi: 10.1016/j.ic.2021.104788BibTex
@article{Repin2021-12Autom-55058, year={2021}, doi={10.1016/j.ic.2021.104788}, title={Automated deep abstractions for stochastic chemical reaction networks}, volume={281}, issn={0890-5401}, journal={Information and Computation}, author={Repin, Denis and Petrov, Tatjana} }
RDF
<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/55058"> <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by-nc-nd/4.0/"/> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/55058/1/Repin_2-1wrre1cocr5tf0.pdf"/> <dcterms:abstract xml:lang="eng">Predicting stochastic cellular dynamics as emerging from the mechanistic models of molecular interactions is a long-standing challenge in systems biology: low-level chemical reaction network (CRN) models give rise to a highly-dimensional continuous-time Markov chain (CTMC) which is computationally demanding and often prohibitive to analyse in practice. A recently proposed abstraction method uses deep learning to replace this CTMC with a discrete-time continuous-space process, by training a mixture density deep neural network with traces sampled at regular time intervals (which can be obtained either by simulating a given CRN or as time-series data from experiment). The major advantage of such abstraction is that it produces a computational model that is dramatically cheaper to execute, while it preserves the statistical features of the training data. In general, the abstraction accuracy improves with the amount of training data. However, depending on the CRN, the overall quality of the method – the efficiency gain and abstraction accuracy – will also depend on the choice of neural network architecture given by hyper-parameters such as the layer types and connections between them. As a consequence, in practice, the modeller has to take care of finding the suitable architecture manually, for each given CRN, through a tedious and time-consuming trial-and-error cycle. In this paper, we propose to further automatise deep abstractions for stochastic CRNs, through learning the neural network architecture along with learning the transition kernel of the abstract process. Automated search of the architecture makes the method applicable directly to any given CRN, which is time-saving for deep learning experts and crucial for non-specialists. We implement the method and demonstrate its performance on a number of representative CRNs with multi-modal emergent phenotypes. Moreover, we showcase that deep abstractions can be used for efficient multi-scale simulations, which are otherwise computationally intractable. To this end, we define a scenario where multiple CRN instances interact across a spatial grid via shared species. Finally, we discuss the limitations and challenges arising when using deep abstractions.</dcterms:abstract> <dc:language>eng</dc:language> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/55058"/> <dcterms:issued>2021-12</dcterms:issued> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43615"/> <dc:rights>Attribution-NonCommercial-NoDerivatives 4.0 International</dc:rights> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43615"/> <dc:creator>Repin, Denis</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-09-28T09:54:52Z</dcterms:available> <dc:contributor>Petrov, Tatjana</dc:contributor> <dc:contributor>Repin, Denis</dc:contributor> <foaf:homepage rdf:resource="http://localhost:8080/"/> <dcterms:title>Automated deep abstractions for stochastic chemical reaction networks</dcterms:title> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/55058/1/Repin_2-1wrre1cocr5tf0.pdf"/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-09-28T09:54:52Z</dc:date> <dc:creator>Petrov, Tatjana</dc:creator> </rdf:Description> </rdf:RDF>
Internal note
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Examination date of dissertation
Method of financing
Comment on publication
Alliance license
Corresponding Authors der Uni Konstanz vorhanden
International Co-Authors
Bibliography of Konstanz
Yes
Refereed
Unknown