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Identifiability of stochastically modelled reaction networks

Published online by Cambridge University Press:  15 February 2021

GERMAN ENCISO
Affiliation:
Department of Mathematics, University of California, Irvine, CA92697, USA e-mail: [email protected]
RADEK ERBAN
Affiliation:
Mathematical Institute, University of Oxford, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, UK e-mail: [email protected]
JINSU KIM
Affiliation:
NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA92697, USA e-mail: [email protected]

Abstract

Chemical reaction networks describe interactions between biochemical species. Once an underlying reaction network is given for a biochemical system, the system dynamics can be modelled with various mathematical frameworks such as continuous-time Markov processes. In this manuscript, the identifiability of the underlying network structure with a given stochastic system dynamics is studied. It is shown that some data types related to the associated stochastic dynamics can uniquely identify the underlying network structure as well as the system parameters. The accuracy of the presented network inference is investigated when given dynamical data are obtained via stochastic simulations.

Type
Papers
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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