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Conference Paper: Optimal time points sampling in dynamic pathway modelling
Title | Optimal time points sampling in dynamic pathway modelling |
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Authors | |
Keywords | Computational system biology Dynamic pathway modelling Parameter estimation Quantum-inspired evolutionary algorithm Signal transduction |
Issue Date | 2004 |
Citation | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2004, v. 26 I, p. 671-674 How to Cite? |
Abstract | Modelling cellular dynamics based on experimental data is at the heart of System Biology. Considerable progress has been made to dynamic pathway modelling as well as the related parameter estimation. However, few of them gives consideration for the issue of optimal sampling time selection for parameter estimation. Time course experiments in molecular biology rarely produce large and accurate data sets and the experiments involved are usually time consuming and expensive. Therefore, to approximate parameters for models with only few available sampling data is of significant practical value. For signal transduction, the sampling intervals are usually not evenly distributed and are based on heuristics. In the paper, we investigate an approach to guide the process of selecting time points in an optimal way to minimize the variance of parameter estimates. In the method, we first formulate the problem to a nonlinear constrained optimization problem by maximum likelihood estimation. We then modify and apply a Quantum-inspired Evolutionary Algorithm, which combines the advantages of both quantum computing and evolutionary computing, to solve the optimization problem. The new algorithm does not suffer from the morass of selecting good initial values and being stuck into local optimum as usually accompanied with the conventional numerical optimization techniques. The simulation results indicate the soundness of the new method. |
Persistent Identifier | http://hdl.handle.net/10722/336019 |
ISSN | 2023 SCImago Journal Rankings: 0.340 |
DC Field | Value | Language |
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dc.contributor.author | Hu, Shiyan | - |
dc.date.accessioned | 2024-01-15T08:22:04Z | - |
dc.date.available | 2024-01-15T08:22:04Z | - |
dc.date.issued | 2004 | - |
dc.identifier.citation | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2004, v. 26 I, p. 671-674 | - |
dc.identifier.issn | 0589-1019 | - |
dc.identifier.uri | http://hdl.handle.net/10722/336019 | - |
dc.description.abstract | Modelling cellular dynamics based on experimental data is at the heart of System Biology. Considerable progress has been made to dynamic pathway modelling as well as the related parameter estimation. However, few of them gives consideration for the issue of optimal sampling time selection for parameter estimation. Time course experiments in molecular biology rarely produce large and accurate data sets and the experiments involved are usually time consuming and expensive. Therefore, to approximate parameters for models with only few available sampling data is of significant practical value. For signal transduction, the sampling intervals are usually not evenly distributed and are based on heuristics. In the paper, we investigate an approach to guide the process of selecting time points in an optimal way to minimize the variance of parameter estimates. In the method, we first formulate the problem to a nonlinear constrained optimization problem by maximum likelihood estimation. We then modify and apply a Quantum-inspired Evolutionary Algorithm, which combines the advantages of both quantum computing and evolutionary computing, to solve the optimization problem. The new algorithm does not suffer from the morass of selecting good initial values and being stuck into local optimum as usually accompanied with the conventional numerical optimization techniques. The simulation results indicate the soundness of the new method. | - |
dc.language | eng | - |
dc.relation.ispartof | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings | - |
dc.subject | Computational system biology | - |
dc.subject | Dynamic pathway modelling | - |
dc.subject | Parameter estimation | - |
dc.subject | Quantum-inspired evolutionary algorithm | - |
dc.subject | Signal transduction | - |
dc.title | Optimal time points sampling in dynamic pathway modelling | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-11044232029 | - |
dc.identifier.volume | 26 I | - |
dc.identifier.spage | 671 | - |
dc.identifier.epage | 674 | - |