scholarly journals SPSens: a software package for stochastic parameter sensitivity analysis of biochemical reaction networks

2012 ◽  
Vol 29 (1) ◽  
pp. 140-142 ◽  
Author(s):  
Patrick W. Sheppard ◽  
Muruhan Rathinam ◽  
Mustafa Khammash
Keyword(s):  
Sensitivity Analysis ◽  
Software Package ◽  
Parameter Sensitivity ◽  
Biochemical Reaction ◽  
Reaction Networks ◽  
Parameter Sensitivity Analysis ◽  
Biochemical Reaction Networks ◽  
Stochastic Parameter

scholarly journals Spatio-temporal modelling of Leishmania infantum infection among domestic dogs: a simulation study and sensitivity analysis applied to rural Brazil

2018 ◽  
Author(s):  
Elizabeth Buckingham-Jeffery ◽  
Edward M. Hill ◽  
Samik Datta ◽  
Erin Dilger ◽  
Orin Courtenay
Keyword(s):  
Sensitivity Analysis ◽  
Leishmania Infantum ◽  
Parameter Sensitivity ◽  
Domestic Dogs ◽  
Sand Fly ◽  
Model Parameters ◽  
Infection Prevalence ◽  
Parameter Sensitivity Analysis ◽  
Model Framework ◽  
Stochastic Parameter

AbstractBackgroundThe parasite Leishmania infantum causes zoonotic visceral leishmaniasis (VL), a potentially fatal vector-borne disease of canids and humans. Zoonotic VL poses a significant risk to public health, with regions of Latin America being particularly afflicted by the disease.Leishmania infantum parasites are transmitted between hosts during blood feeding by infected female phlebotomine sand flies. With a principal reservoir host of L. infantum being domestic dogs, limiting prevalence in this reservoir may result in a reduced risk of infection for the human population. To this end, a primary focus of research efforts has been to understand disease transmission dynamics among dogs. One way this can be achieved is through the use of mathematical models.MethodsWe have developed a stochastic, spatial, individual-based mechanistic model of L. infantum transmission in domestic dogs. The model framework was applied to a rural Brazilian village setting with parameter values informed by fieldwork and laboratory data. To ensure household and sand fly populations were realistic, we statistically fit distributions for these entities to existing survey data. To identify the model parameters of highest importance, we performed a stochastic parameter sensitivity analysis of the prevalence of infection among dogs to the model parameters.ResultsWe computed parametric distributions for the number of humans and animals per household and a non-parametric temporal profile for sand fly abundance. The stochastic parameter sensitivity analysis determined prevalence of L. infantum infection in dogs to be most strongly affected by the sand fly associated parameters and the proportion of immigrant dogs already infected with L. infantum parasites.ConclusionsEstablishing the model parameters with the highest sensitivity of average L. infantum infection prevalence in dogs to their variation helps motivate future data collection efforts focusing on these elements. Moreover, the proposed mechanistic modelling framework provides a foundation that can be expanded to explore spatial patterns of zoonotic VL in humans and to assess spatially targeted interventions.

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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