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Loos, C. ; Fiedler, A. ; Hasenauer, J.

Parameter estimation for reaction rate equation constrained mixture models. 

Lect. Notes Comput. Sc. 9859, 186-200 (2016)
Postprint DOI
Open Access Green
The elucidation of sources of heterogeneity in cell populations is crucial to fully understand biological processes. A suitable method to identify causes of heterogeneity is reaction rate equation (RRE) constrained mixture modeling, which enables the analysis of subpopulation structures and dynamics. These mixture models are calibrated using single cell snapshot data to estimate model parameters which are not measured or which cannot be assessed experimentally. In this manuscript, we evaluate different optimization methods for estimating the parameters of RRE constrained mixture models under the normal distribution assumption. We compare gradient-based optimization using sensitivity analysis with two other optimization methods – gradient-based optimization with finite differences and a stochastic optimization method – for simulation examples with artificial data. Furthermore, we compare different numerical schemes for the evaluation of the log-likelihood function. We found that gradient-based optimization using sensitivity analysis outperforms the other optimization methods in terms of convergence and computation time.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Parameter estimation; Reaction rate equations; Mixture models; Sensitivity analysis
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Computational Methods in Systems Biology
Konferzenzdatum 21-23 September 2016
Konferenzort Cambridge, UK
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 9859, Heft: , Seiten: 186-200 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute of Computational Biology (ICB)