scholarly journals Bayesian parameter estimation for biochemical reaction networks using region-based adaptive parallel tempering

2018 ◽  
Vol 34 (13) ◽  
pp. i494-i501 ◽  
Author(s):  
Benjamin Ballnus ◽  
Steffen Schaper ◽  
Fabian J Theis ◽  
Jan Hasenauer
Keyword(s):  
Parameter Estimation ◽  
Biochemical Reaction ◽  
Parallel Tempering ◽  
Reaction Networks ◽  
Biochemical Reaction Networks ◽  
Bayesian Parameter Estimation

scholarly journals Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks

2010 ◽  
Vol 4 (1) ◽  
pp. 69 ◽  
Author(s):  
Philipp Rumschinski ◽  
Steffen Borchers ◽  
Sandro Bosio ◽  
Robert Weismantel ◽  
Rolf Findeisen
Keyword(s):  
Parameter Estimation ◽  
Biochemical Reaction ◽  
Reaction Networks ◽  
Dynamical Parameter ◽  
Biochemical Reaction Networks ◽  
Model Invalidation

scholarly journals Model Discrimination and Parameter Estimation via Infeasibility Certificates for Dynamical Biochemical Reaction Networks

2009 ◽  
Vol 42 (10) ◽  
pp. 245-250 ◽  
Author(s):  
Steffen Borchers ◽  
Philipp Rumschinski ◽  
Sandro Bosio ◽  
Robert Weismantel ◽  
Rolf Findeisen
Keyword(s):  
Parameter Estimation ◽  
Biochemical Reaction ◽  
Reaction Networks ◽  
Model Discrimination ◽  
Biochemical Reaction Networks

scholarly journals Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks

2017 ◽  
Vol 13 (1) ◽  
pp. e1005331 ◽  
Author(s):  
Fabian Fröhlich ◽  
Barbara Kaltenbacher ◽  
Fabian J. Theis ◽  
Jan Hasenauer
Keyword(s):  
Parameter Estimation ◽  
Biochemical Reaction ◽  
Reaction Networks ◽  
Biochemical Reaction Networks ◽  
Genome Scale

scholarly journals Scalable parameter estimation for genome-scale biochemical reaction networks

2016 ◽  
Author(s):  
Fabian Fröhlich ◽  
Barbara Kaltenbacher ◽  
Fabian J. Theis ◽  
Jan Hasenauer
Keyword(s):  
Sensitivity Analysis ◽  
Parameter Estimation ◽  
Large Scale ◽  
Chemical Species ◽  
Biochemical Reaction ◽  
Reaction Networks ◽  
Adjoint Sensitivity Analysis ◽  
Biochemical Reaction Networks ◽  
Adjoint Sensitivity ◽  
Genome Scale

AbstractMechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small-and medium-scale biological processes. While the same should in principle hold for large-and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large-and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics.Author SummaryIn this manuscript, we introduce a scalable method for parameter estimation for genome-scale biochemical reaction networks. Mechanistic models for genome-scale biochemical reaction networks describe the behavior of thousands of chemical species using thousands of parameters. Standard methods for parameter estimation are usually computationally intractable at these scales. Adjoint sensitivity based approaches have been suggested to have superior scalability but any rigorous evaluation is lacking. We implement a toolbox for adjoint sensitivity analysis for biochemical reaction network which also supports the import of SBML models. We show by means of a set of benchmark models that adjoint sensitivity based approaches unequivocally outperform standard approaches for large-scale models and that the achieved speedup increases with respect to both the number of parameters and the number of chemical species in the model. This demonstrates the applicability of adjoint sensitivity based approaches to parameter estimation for genome-scale mechanistic model. The MATLAB toolbox implementing the developed methods is available from http://ICB-DCM.github.io/AMICI/.

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