Reconstructing the hidden states in time course data of stochastic models

• Verfasst von: Zimmer, Christoph [VerfasserIn]
• Titel: Reconstructing the hidden states in time course data of stochastic models
• Verf.angabe: Christoph Zimmer
• E-Jahr: 2015
• Jahr: 9 September 2015
• Umfang: 13 S.
• Fussnoten: Gesehen am 12.10.2020
• Titel Quelle: Enthalten in: Mathematical biosciences
• Ort Quelle: New York, NY : American Elsevier, 1967
• Jahr Quelle: 2015
• Band/Heft Quelle: 269(2015), Seite 117-129
• ISSN Quelle: 1879-3134
• DOI: doi:10.1016/j.mbs.2015.08.015
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Parameter estimation
• Sach-SW: state estimation
• Sach-SW: stochastic models
• Sach-SW: systems biology
• K10plus-PPN: 1735395358

Abstract

Parameter estimation is central for analyzing models in Systems Biology. The relevance of stochastic modeling in the field is increasing. Therefore, the need for tailored parameter estimation techniques is increasing as well. Challenges for parameter estimation are partial observability, measurement noise, and the computational complexity arising from the dimension of the parameter space. This article extends the multiple shooting for stochastic systems’ method, developed for inference in intrinsic stochastic systems. The treatment of extrinsic noise and the estimation of the unobserved states is improved, by taking into account the correlation between unobserved and observed species. This article demonstrates the power of the method on different scenarios of a Lotka–Volterra model, including cases in which the prey population dies out or explodes, and a Calcium oscillation system. Besides showing how the new extension improves the accuracy of the parameter estimates, this article analyzes the accuracy of the state estimates. In contrast to previous approaches, the new approach is well able to estimate states and parameters for all the scenarios. As it does not need stochastic simulations, it is of the same order of speed as conventional least squares parameter estimation methods with respect to computational time.