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SBML-PET: a Systems Biology Markup Language-based Parameter Estimation Tool
, 2006
"... Summary: The estimation of model parameters from experimental data remains a bottleneck for a major breakthrough in systems biology. We present a Systems Biology Markup Language (SBML) based Parameter Estimation Tool (SBML-PET). The tool is designed to enable parameter estimation for biological mode ..."
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Cited by 43 (4 self)
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Summary: The estimation of model parameters from experimental data remains a bottleneck for a major breakthrough in systems biology. We present a Systems Biology Markup Language (SBML) based Parameter Estimation Tool (SBML-PET). The tool is designed to enable parameter estimation for biological models including signaling pathways, gene regulation networks and metabolic pathways. SBML-PET supports import and export of the models in the SBML format. It can estimate the parameters by fitting a variety of experimental data from different experimental conditions. SBML-PET has a unique feature of supporting event definition in the SMBL model. SBML models can also be simulated in SBML-PET. Stochastic Ranking Evolution Strategy (SRES) is incorporated in SBML-PET for parameter estimation jobs. A classic ODE Solver called ODEPACK is used to solve the Ordinary Differential Equation (ODE) system.
Saskatoon By
"... uate degree from the University of Saskatchewan, I agree that the Libraries of this University may make it freely available for inspection. I further agree that permission for copying of this thesis in any manner, in whole or in part, for scholarly purposes may be granted by the professor or profess ..."
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uate degree from the University of Saskatchewan, I agree that the Libraries of this University may make it freely available for inspection. I further agree that permission for copying of this thesis in any manner, in whole or in part, for scholarly purposes may be granted by the professor or professors who supervised my thesis work or, in their absence, by the Head of the Department or the Dean of the College in which my thesis work was done. It is understood that any copying or publication or use of this thesis or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the University of Saskatchewan in any scholarly use which may be made of any material in my thesis.
Computational Analysis of S-type Biological Systems
"... Abstract – S-systems identify the direct interaction of genes and proteins in biological systems. Therefore, mathematical and computational analysis of the S-type models is important to achieve a true understanding of biological systems. However, theoretical approaches for S-systems are limited, and ..."
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Abstract – S-systems identify the direct interaction of genes and proteins in biological systems. Therefore, mathematical and computational analysis of the S-type models is important to achieve a true understanding of biological systems. However, theoretical approaches for S-systems are limited, and computational identification of S-systems ’ parameters is challenging. How to make a trade-off between accuracy and computational cost is in development. In this study, we first propose a memetic differential evolution scheme to identify the parameters of S-systems. The proposed scheme ameliorates the disadvantages in traditional gradient-based optimization methods, and solves the slow-convergence problem of stochastic algorithms. This scheme not only improves the global-search power of differential evolution (DE) but also largely increases the convergence speed. We then discuss and analyze the dynamic behavior of S-type biological systems. Four biological systems are used to demonstrate our approaches. Index terms: Inverse problem, parameter estimation, memetic computation, evolution algorithm. I.
Application Notes SBML-PET: A Systems Biology Markup Language based Parameter Estimation Tool
"... Summary: The estimation of model parameters from experimental data remains a bottleneck for a major breakthrough in systems biology. We present a Systems Biology Markup Language (SBML) based Parameter Estimation Tool (SBML-PET). The tool is designed to enable parameter estimation for biological mode ..."
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Summary: The estimation of model parameters from experimental data remains a bottleneck for a major breakthrough in systems biology. We present a Systems Biology Markup Language (SBML) based Parameter Estimation Tool (SBML-PET). The tool is designed to enable parameter estimation for biological models including signaling pathways, gene regulation networks, and metabolic pathways. SBML-PET supports import and export of the models in the SBML format. It can estimate the parameters by fitting a variety of experimental data from different experimental conditions. SBML-PET has a unique feature of supporting event definition in the SMBL model. SBML models can also be simulated in SBML-PET. Stochastic Ranking Evolution Strategy (SRES) is incorporated in SBML-PET for parameter estimation jobs. A classic ODE Solver called ODEPACK is used to solve the Ordinary Differential Equation (ODE) system.
BMC Systems Biology BioMed Central Research article Parameter optimization in S-system models
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doi:10.1093/bioinformatics/btl443BIOINFORMATICS APPLICATIONS NOTE Systems biology SBML-PET: a Systems Biology Markup Language-based
, 2006
"... Summary:Theestimationofmodelparameters fromexperimental data remains a bottleneck for a major breakthrough in systems biology. We present a Systems Biology Markup Language (SBML) based ParameterEstimationTool (SBML-PET). The tool is designed to enable parameter estimation for biological models inclu ..."
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Summary:Theestimationofmodelparameters fromexperimental data remains a bottleneck for a major breakthrough in systems biology. We present a Systems Biology Markup Language (SBML) based ParameterEstimationTool (SBML-PET). The tool is designed to enable parameter estimation for biological models including signaling path-ways, gene regulation networks and metabolic pathways. SBML-PET supports import and export of the models in the SBML format. It can estimate the parameters by fitting a variety of experimental data fromdifferentexperimental conditions.SBML-PEThasaunique feature of supporting event definition in the SMBL model. SBML models can alsobesimulated inSBML-PET.StochasticRankingEvolutionStrategy (SRES) is incorporated in SBML-PET for parameter estimation jobs. A classic ODE Solver called ODEPACK is used to solve the Ordinary Differential Equation (ODE) system.
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"... Mathematical modeling and kinetic analysis of cellular signaling pathways DISSERTATION zur Erlangung des akademischen Grades doctor rerum naturalium (Dr. rer. nat.) ..."
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Mathematical modeling and kinetic analysis of cellular signaling pathways DISSERTATION zur Erlangung des akademischen Grades doctor rerum naturalium (Dr. rer. nat.)
Fuzzy-Based Self-Interactive Multiobjective Evolution Optimization for Reverse Engineering of Biological Networks
, 2011
"... Abstract—S-system modeling from time series datasets can pro-vide us with an interactive network. However, system identifica-tion is difficult since an S-system is described as highly nonlin-ear differential equations. Much research adopts various evolution computation technologies to identify syste ..."
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Abstract—S-system modeling from time series datasets can pro-vide us with an interactive network. However, system identifica-tion is difficult since an S-system is described as highly nonlin-ear differential equations. Much research adopts various evolution computation technologies to identify system parameters, and some further achieve skeletal-network structure identification. However, the truncated redundant kinetic orders are not small enough as compared with the preserved terms. In this paper, we integrate quantitative genetics, bacterium movement, and fuzzy set theory into evolution computation to develop a new genetic algorithm to achieve convergence enhancement and diversity preservation. The proposed exploration and exploitation genetic algorithm (EEGA) can improve the best-so-far individual and ensure global optimal search at the same time. The EEGA enhances evolution conver-gence by golden section seed selection, normal-distribution repro-duction, mixed inbreeding and backcrossing, competition elitism, and acceleration operations. Search-then-conquer evolution direc-tion operations, eugenics-based screen-sifting mutation, eugenic self-mutation, and fuzzy-based tumble migration preserve popu-lation diversity to avoid premature convergence. Furthermore, to ensure that a reasonable gene regulation network is inferred, fuzzy composition is introduced to derive a reconstruction index. This performance index let EEGA possess self-interactive multiobjec-tive learning. The proposed fuzzy-reconstruction-based multiob-jective genetic algorithm is examined by three dry-lab biological systems. Simulation results show that a safety pruning action is guaranteed (the truncation threshold is set to be 10−15), and only one- or two-step pruning action is taken. Index Terms—Multiobjective, real-value coding, self-interactive, structure identification. I.
