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Processes 2018, 6(9), 136; https://doi.org/10.3390/pr6090136

Identifiability and Reconstruction of Biochemical Reaction Networks from Population Snapshot Data

Univ. Grenoble Alpes, Inria, 38000 Grenoble, France
Received: 28 June 2018 / Revised: 31 July 2018 / Accepted: 15 August 2018 / Published: 22 August 2018
(This article belongs to the Special Issue Computational Synthetic Biology)
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Abstract

Inference of biochemical network models from experimental data is a crucial problem in systems and synthetic biology that includes parameter calibration but also identification of unknown interactions. Stochastic modelling from single-cell data is known to improve identifiability of reaction network parameters for specific systems. However, general results are lacking, and the advantage over deterministic, population-average approaches has not been explored for network reconstruction. In this work, we study identifiability and propose new reconstruction methods for biochemical interaction networks. Focusing on population-snapshot data and networks with reaction rates affine in the state, for parameter estimation, we derive general methods to test structural identifiability and demonstrate them in connection with practical identifiability for a reporter gene in silico case study. In the same framework, we next develop a two-step approach to the reconstruction of unknown networks of interactions. We apply it to compare the achievable network reconstruction performance in a deterministic and a stochastic setting, showing the advantage of the latter, and demonstrate it on population-snapshot data from a simulated example. View Full-Text
Keywords: moment equations; regulatory networks; statistical inference; reporter gene systems; flow-cytometry moment equations; regulatory networks; statistical inference; reporter gene systems; flow-cytometry
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Cinquemani, E. Identifiability and Reconstruction of Biochemical Reaction Networks from Population Snapshot Data. Processes 2018, 6, 136.

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