Parameter Estimation for Reaction Rate Equation Constrained Mixture Models

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• 关键词：Parameter estimation ; Reaction rate equations ; Mixture models ; Sensitivity analysis
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9859
• 期：1
• 页码：186-200
• 全文大小：993 KB
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• 作者单位：Carolin Loos (16) (17)
  Anna Fiedler (16) (17)
  Jan Hasenauer (16) (17)
  
  16. Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764, Neuherberg, Germany
  17. Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Technische Universität München, 85748, Garching, Germany
  
• 丛书名：Computational Methods in Systems Biology
• ISBN：978-3-319-45177-0
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9859

文摘

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.