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Benchmarking Optimization Methods For Parameter Estimation In Large Kinetic Models [article]

Alejandro F Villaverde, Fabian Fröhlich, Daniel Weindl, Jan Hasenauer, Julio R. Banga
2018 Zenodo  
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]

Alejandro F. Villaverde, Fabian Froehlich, Daniel Weindl, Jan Hasenauer, Julio R Banga
2018 bioRxiv   pre-print
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]

Alejandro F Villaverde, Fabian Fröhlich, Daniel Weindl, Jan Hasenauer, Julio R. Banga
2018 Zenodo  
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

Alejandro F Villaverde, Fabian Fröhlich, Daniel Weindl, Jan Hasenauer, Julio R Banga, Oliver Stegle
2018 Bioinformatics  
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

Muhammad Akmal Remli, Safaai Deris, Mohd Saberi Mohamad, Sigeru Omatu, Juan Manuel Corchado
2017 Engineering applications of artificial intelligence  
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

Kazuhiro Maeda, Fred C. Boogerd, Hiroyuki Kurata
2018 IPSJ Transactions on Bioinformatics  
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

Torlapati, Clement
2019 Processes  
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

X. Ji, Y. Xu
2005 Bioinformatics  
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

Mohammed Adam Kunna, Tuty Asmawaty Abdul Kadir, Aqeel S. Jaber, Julius B. Odili
2015 Advances in Bioscience and Biotechnology  
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

LI-YU-XIN CHEN, DONG-XIANG ZHANG, LI-HUA WANG, JIAN-FENG SU
2020 DEStech Transactions on Computer Science and Engineering  
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]

Kazuhiro Maeda, Fred C. Boogerd, Hiroyuki Kurata
2021 bioRxiv   pre-print
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

A. Kremling
2004 Genome Research  
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]

Jeffrey Varner, Adithya Sagar, Rachel LeCover, Christine Shoemaker
2017 bioRxiv   pre-print
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

Derek C. Manheim, Russell Detwiler
2019 MethodsX  
•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

Mohammed Adam Kunna, Tuty Asmawaty Abdul Kadir, Muhammad Akmal Remli, Noorlin Binti Mohd Ali, Kohbalan Moorthy, Noryanti Muhammad
2020 Processes  
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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