Inverse Modeling of the Drosophila Gap Gene System: Sparsity Promoting Bayesian Parameter Estimation and Uncertainty Quantication

Nikolaos Sfakianakis; Martin Simon

Inverse Modeling of the Drosophila Gap Gene System: Sparsity Promoting Bayesian Parameter Estimation and Uncertainty Quantication

Applied Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this work we propose a regularization strategy for the inverse parameter identification problem for the Drosophila gap gene circuit model in the framework of Bayesian inversion. In contrast to classical deterministic methods the proposed scheme aims not only to find approximate parameter values but also to estimate their reliability.

Original language	English
Title of host publication	The 10th International Workshop on Computational Systems Biology
Pages	86-90
Publication status	Published - 2013

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@inproceedings{305d319c7521402bbe5978944158df61,

title = "Inverse Modeling of the Drosophila Gap Gene System: Sparsity Promoting Bayesian Parameter Estimation and Uncertainty Quantication",

abstract = "In this work we propose a regularization strategy for the inverse parameter identification problem for the Drosophila gap gene circuit model in the framework of Bayesian inversion. In contrast to classical deterministic methods the proposed scheme aims not only to find approximate parameter values but also to estimate their reliability.",

author = "Nikolaos Sfakianakis and Martin Simon",

year = "2013",

language = "English",

pages = "86--90",

booktitle = "The 10th International Workshop on Computational Systems Biology",

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AU - Sfakianakis, Nikolaos

AU - Simon, Martin

PY - 2013

Y1 - 2013

N2 - In this work we propose a regularization strategy for the inverse parameter identification problem for the Drosophila gap gene circuit model in the framework of Bayesian inversion. In contrast to classical deterministic methods the proposed scheme aims not only to find approximate parameter values but also to estimate their reliability.

AB - In this work we propose a regularization strategy for the inverse parameter identification problem for the Drosophila gap gene circuit model in the framework of Bayesian inversion. In contrast to classical deterministic methods the proposed scheme aims not only to find approximate parameter values but also to estimate their reliability.

M3 - Conference contribution

SP - 86

EP - 90

BT - The 10th International Workshop on Computational Systems Biology

ER -

Inverse Modeling of the Drosophila Gap Gene System: Sparsity Promoting Bayesian Parameter Estimation and Uncertainty Quantication

Abstract

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