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Benchmarking Optimization Methods For Parameter Estimation In Large Kinetic Models
[article]
2018
Zenodo
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Software to reproduce the results presented in the paper "Benchmarking optimization methods for parameter estimation in large kinetic models". ...
It includes two MATLAB toolboxes: AMICI and MEIGO, as well as MATLAB code to define the optimizations and to report the results as figures, tables, and summary files. ...
Running the benchmarks The inputs folder contains scripts that estimate the parameters of the models using the MEIGO toolbox. They are called launch_meigo_MODELNAME.m (e.g. launch_meigo_B2.m). ...
doi:10.5281/zenodo.1160342
fatcat:hdukqa55drg6nhsfl3q4jkxaoy
Benchmarking optimization methods for parameter estimation in large kinetic models
[article]
2018
bioRxiv
pre-print
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Motivation: Mechanistic kinetic models usually contain unknown parameters, which need to be estimated by optimizing the fit of the model to experimental data. ...
A systematic comparison of methods that are suited to parameter estimation problems of sizes ranging from tens to hundreds of optimization variables is currently missing, and smaller studies indeed provided ...
In this study, we evaluate the state-of-the-art in parameter estimation methodologies and provide guidelines for their application to large kinetic models in systems biology. ...
doi:10.1101/295006
fatcat:w3usnun4dzekhd64nd7f4ufjva
Benchmarking Optimization Methods For Parameter Estimation In Large Kinetic Models
[article]
2018
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Software to reproduce the results presented in the paper "Benchmarking optimization methods for parameter estimation in large kinetic models". ...
It includes two MATLAB toolboxes: AMICI and MEIGO, as well as MATLAB code to define the optimizations and to report the results as figures, tables, and summary files. ...
Running the benchmarks The inputs folder contains scripts that estimate the parameters of the models using the MEIGO toolbox. They are called launch_meigo_MODELNAME.m (e.g. launch_meigo_B2.m). ...
doi:10.5281/zenodo.1160343
fatcat:3stn6yhcb5aytmhyqdqjsnk7pe
Benchmarking optimization methods for parameter estimation in large kinetic models
2018
Bioinformatics
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The file type is application/pdf.
Kinetic models contain unknown parameters that are estimated by optimizing the fit to experimental data. ...
A systematic comparison of parameter estimation methods for problems with tens to hundreds of optimization variables is currently missing, and smaller studies provided contradictory findings. ...
In this study, we evaluate the state-of-the-art in parameter estimation methodologies and provide guidelines for their application to large kinetic models in systems biology. ...
doi:10.1093/bioinformatics/bty736
pmid:30816929
pmcid:PMC6394396
fatcat:nkoagacfqzd2pk5ltab57kwcbi
An enhanced scatter search with combined opposition-based learning for parameter estimation in large-scale kinetic models of biochemical systems
2017
Engineering applications of artificial intelligence
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For parameter estimation, around 116 kinetic parameters in Chinese hamster ovary (CHO) cells and central carbon metabolism of E. coli are estimated. ...
The proposed algorithm is tested using one set of benchmark function each from large-scale global optimization (LSGO) problem as well as parameter estimation problem. ...
Acknowledgement We would like to thank Malaysian Ministry of Higher Education and Universiti Teknologi Malaysia for supporting this research by a Fundamental Research Grant Scheme (grant number: R.J130000.7828.4F886 ...
doi:10.1016/j.engappai.2017.04.004
fatcat:md4zfa4rendtrbikfn3dg57yxu
libRCGA: a C library for real-coded genetic algorithms for rapid parameter estimation of kinetic models
2018
IPSJ Transactions on Bioinformatics
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However, parameter estimation remains a major bottleneck in kinetic modeling. To accelerate parameter estimation, we developed a C library for real-coded genetic algorithms (libRCGA). ...
In the present paper, we demonstrate the performance of libRCGA through benchmark problems and in realistic parameter estimation problems. libRCGA is freely available for academic usage at ...
In summary, libRCGA greatly accelerates parameter estimation in kinetic modeling. libRCGA is not restricted to application in kinetic modeling, but is also applicable to other global optimization problems ...
doi:10.2197/ipsjtbio.11.31
fatcat:ovyrttfyufa7lfakvvd4wj2x4m
Using Parallel Genetic Algorithms for Estimating Model Parameters in Complex Reactive Transport Problems
2019
Processes
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In this study, we present the details of an optimization method for parameter estimation of one-dimensional groundwater reactive transport problems using a parallel genetic algorithm (PGA). ...
The optimization model was provided with the published experimental results and reasonable bounds for the unknown kinetic reaction parameters as inputs. ...
The specific kinetic equations used for modeling these benchmark problems and their unknown parameters are presented in the following section. ...
doi:10.3390/pr7100640
fatcat:k3prwi5k5vbftloqrydi6ixsgm
libSRES: a C library for stochastic ranking evolution strategy for parameter estimation
2005
Bioinformatics
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Estimation of kinetic parameters in a biochemical pathway or network represents a common problem in systems studies of biological processes. ...
Considering the amount of computing time it might require to solve a parameter-estimation problem, an MPI version of libSRES is provided for parallel implementation, as well as a simple user interface. ...
ACKNOWLEDGEMENTS This research was supported in part by US Department of Energy's Genomes to Life program under project 'Carbon Sequestration in Synechococcus sp: From Molecular Machines to hierarchical ...
doi:10.1093/bioinformatics/bti753
pmid:16267082
fatcat:zmdi3ecebzhedn5sxprgpdijea
Large-Scale Kinetic Parameter Identification of Metabolic Network Model of E. coli Using PSO
2015
Advances in Bioscience and Biotechnology
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Seven kinetic parameters were found to be highly affected in the model response; secondly, particle swarm optimization was applied for kinetic parameter identification of a metabolic network model. ...
In metabolic network modelling, the accuracy of kinetic parameters has become more important over the last two decades. ...
Tuty Asmawaty Abdul Kadir, who provided the model under study and Dr. Md. Aminul Hoquea for providing the experimental data set. ...
doi:10.4236/abb.2015.62012
fatcat:ijocfiwg45gcxbqmixcto5474u
Using Modified Self-adaptive Differential Evolution for Estimation of Chemical Reaction Kinetic Parameters
2020
DEStech Transactions on Computer Science and Engineering
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In addition, MSaDE is applied to estimate the kinetic parameters of ammonium perchlorate (AP) thermal decomposition reaction models, satisfactory results are obtained. ...
In order to accurately estimate the reaction kinetic parameters, a novel modified self-adaptive differential evolution (MSaDE) algorithm is proposed in this paper, aiming at the problems of premature or ...
The problem of kinetic parameter estimation is usually more complex, and has larger dimensions, with a large number of local optimal solutions. ...
doi:10.12783/dtcse/cmso2019/33606
fatcat:zuqdyvcewneold5elvuaxzzt2y
RCGAToolbox: A real-coded genetic algorithm software for parameter estimation of kinetic models
[article]
2021
bioRxiv
pre-print
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However, parameter estimation remains a major bottleneck in the development of kinetic models. ...
We present RCGAToolbox, software for real-coded genetic algorithms (RCGAs), which accelerates the parameter estimation of kinetic models. ...
Acknowledgements The authors thank Editage for reviewing and editing the manuscript. ...
doi:10.1101/2021.02.15.431062
fatcat:gzginrpdnjbufmqoxwpzg5dydu
A Benchmark for Methods in Reverse Engineering and Model Discrimination: Problem Formulation and Solutions
2004
Genome Research
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Measurements for some intracellular components are provided representing a small biochemical network. Problems of reverse engineering, parameter estimation, and identifiability are addressed. ...
A benchmark problem is described for the reconstruction and analysis of biochemical networks given sampled experimental data. ...
is very sensitive to parameters that could be estimated only with large errors using the experiment(s) carried out so far (here the benchmark experiment). ...
doi:10.1101/gr.1226004
pmid:15342560
pmcid:PMC515324
fatcat:ceptheigfvekndnnhyes6lwqeq
Dynamic Optimization with Particle Swarms (DOPS): A meta-heuristic for parameter estimation in biochemical models
[article]
2017
bioRxiv
pre-print
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Conclusions: DOPS is a promising meta-heuristic approach for the estimation of biochemical model parameters in relatively few function evaluations. ...
However, the estimation of the parameters that appear in biochemical models is a significant challenge. ...
Taken together, DOPS is a promising meta-heuristic for the estimation of parameters in large biochemical models. ...
doi:10.1101/240580
fatcat:xnvtl22j75b4jpfv3ocbdhehje
Accurate and Reliable Estimation of Kinetic Parameters for Environmental Engineering Applications: A Global, Multi Objective, Bayesian Optimization Approach
2019
MethodsX
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•A sequential single, multi-objective, and Bayesian optimization workflow was developed to accurately and reliably estimate unstructured kinetic model parameters. ...
Among the main issues identified with parameter estimation, the model-data calibration approach is a crucial, yet an often overlooked and difficult optimization problem. ...
Jasper Vrugt for providing the MATLAB codes and assistance with the AMALGAM-SO, AMALGAM-MO, and DREAM-ZS algorithms.
Appendix A. ...
doi:10.1016/j.mex.2019.05.035
pmid:31245280
pmcid:PMC6582191
fatcat:friyyapwyfbefgi4d7cwwdjari
An Enhanced Segment Particle Swarm Optimization Algorithm for Kinetic Parameters Estimation of the Main Metabolic Model of Escherichia Coli
2020
Processes
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In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. ...
In addition, building a kinetic model requires the estimation of the kinetic parameters, but kinetic parameters estimation in kinetic modeling is a difficult task due to the nonlinearity of the model. ...
Therefore, because of the difficulty in estimating kinetic parameters, many researchers lately have used the metaheuristic optimization algorithms' methods to estimate the kinetics of the E. coli model ...
doi:10.3390/pr8080963
fatcat:cjuld25hprfojlul3hzw4mvzae
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