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).

StochNetV2: A Tool for Automated Deep Abstractions for Stochastic Reaction Networks

Petrov T.
2020-01-01

Abstract

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).
2020
978-3-030-59853-2
978-3-030-59854-9
https://link.springer.com/chapter/10.1007/978-3-030-59854-9_4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3070281
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