Publikation: StochNetV2 : A Tool for Automated Deep Abstractions for Stochastic Reaction Networks
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GRIBAUDO, Marco, ed., David N. JANSEN, ed., Anne REMKE, ed.. Quantitative Evaluation of Systems : 17th International Conference, QEST 2020, Vienna, Austria, August 31 - September 3, 2020, Proceedings. Cham: Springer, 2020, pp. 27-32. Lecture Notes in Computer Science. 12289. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-030-59853-2. Available under: doi: 10.1007/978-3-030-59854-9_4
Zusammenfassung
We present a toolbox for stochastic simulations with CRN models and their (automated) deep abstractions: a mixture density deep neural network trained on time-series data produced by the CRN. The optimal neural network architecture is learnt 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. The tool was primarily designed to efficiently reproduce simulation traces of given complex stochastic reaction networks arising in systems biology research, possibly with multi-modal emergent phenotypes. It is at the same time applicable to any other application domain, where time-series measurements of a Markovian stochastic process are available by experiment or synthesised with simulation (e.g. are obtained from a rule-based description of the CRN).
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17th International Conference, QEST 2020, 31. Aug. 2020 - 3. Sept. 2020, Wien
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REPIN, Denis, Nhat-Huy PHUNG, Tatjana PETROV, 2020. StochNetV2 : A Tool for Automated Deep Abstractions for Stochastic Reaction Networks. 17th International Conference, QEST 2020. Wien, 31. Aug. 2020 - 3. Sept. 2020. In: GRIBAUDO, Marco, ed., David N. JANSEN, ed., Anne REMKE, ed.. Quantitative Evaluation of Systems : 17th International Conference, QEST 2020, Vienna, Austria, August 31 - September 3, 2020, Proceedings. Cham: Springer, 2020, pp. 27-32. Lecture Notes in Computer Science. 12289. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-030-59853-2. Available under: doi: 10.1007/978-3-030-59854-9_4BibTex
@inproceedings{Repin2020Stoch-51770,
year={2020},
doi={10.1007/978-3-030-59854-9_4},
title={StochNetV2 : A Tool for Automated Deep Abstractions for Stochastic Reaction Networks},
number={12289},
isbn={978-3-030-59853-2},
issn={0302-9743},
publisher={Springer},
address={Cham},
series={Lecture Notes in Computer Science},
booktitle={Quantitative Evaluation of Systems : 17th International Conference, QEST 2020, Vienna, Austria, August 31 - September 3, 2020, Proceedings},
pages={27--32},
editor={Gribaudo, Marco and Jansen, David N. and Remke, Anne},
author={Repin, Denis and Phung, Nhat-Huy and Petrov, Tatjana}
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<dcterms:abstract xml:lang="eng">We present a toolbox for stochastic simulations with CRN models and their (automated) deep abstractions: a mixture density deep neural network trained on time-series data produced by the CRN. The optimal neural network architecture is learnt 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. The tool was primarily designed to efficiently reproduce simulation traces of given complex stochastic reaction networks arising in systems biology research, possibly with multi-modal emergent phenotypes. It is at the same time applicable to any other application domain, where time-series measurements of a Markovian stochastic process are available by experiment or synthesised with simulation (e.g. are obtained from a rule-based description of the CRN).</dcterms:abstract>
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