LAPSE:2019.0446
Published Article
LAPSE:2019.0446
Identifiability and Reconstruction of Biochemical Reaction Networks from Population Snapshot Data
Eugenio Cinquemani
April 8, 2019
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.
Keywords
flow-cytometry, moment equations, regulatory networks, reporter gene systems, statistical inference
Suggested Citation
Cinquemani E. Identifiability and Reconstruction of Biochemical Reaction Networks from Population Snapshot Data. (2019). LAPSE:2019.0446
Author Affiliations
Cinquemani E: Univ. Grenoble Alpes, Inria, 38000 Grenoble, France
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Journal Name
Processes
Volume
6
Issue
9
Article Number
E136
Year
2018
Publication Date
2018-08-22
Published Version
ISSN
2227-9717
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Original Submission
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PII: pr6090136, Publication Type: Journal Article
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LAPSE:2019.0446
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doi:10.3390/pr6090136
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Apr 8, 2019
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CC BY 4.0
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Original Submitter
Calvin Tsay
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