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I-Chun Chou
Researcher at The Wallace H. Coulter Department of Biomedical Engineering
Publications - 10
Citations - 882
I-Chun Chou is an academic researcher from The Wallace H. Coulter Department of Biomedical Engineering. The author has contributed to research in topics: Modelling biological systems & Estimation theory. The author has an hindex of 8, co-authored 10 publications receiving 854 citations. Previous affiliations of I-Chun Chou include Georgia Institute of Technology.
Papers
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Journal ArticleDOI
Recent Developments in Parameter Estimation and Structure Identification of Biochemical and Genomic Systems
I-Chun Chou,Eberhard O. Voit +1 more
TL;DR: The article presented here reviews the field of inverse modeling within BST and proposes an operational 'work-flow' that guides the user through the estimation process, identifies possibly problematic steps, and suggests corresponding solutions based on the specific characteristics of the various available algorithms.
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Parameter estimation in biochemical systems models with alternating regression.
TL;DR: It is shown here that alternating regression, applied to S-system models and combined with methods for decoupling systems of differential equations, provides a fast new tool for identifying parameter values from time series data.
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Parameter optimization in S-system models
Marco Vilela,Marco Vilela,I-Chun Chou,Susana Vinga,Ana Tereza Ribeiro de Vasconcelos,Eberhard O. Voit,Jonas S. Almeida,Jonas S. Almeida +7 more
TL;DR: The proposed method of eigenvector optimization constitutes an advancement over S-system parameter identification from time series using a recent method called Alternating Regression and overcomes convergence issues encountered in alternate regression by identifying nonlinear constraints that restrict the search space to computationally feasible solutions.
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System estimation from metabolic time-series data
TL;DR: The results suggest that the proposed approach is more effective and robust than presently available methods for deriving metabolic models from time-series data and its avoidance of error compensation among process descriptions promises significantly improved extrapolability toward new data or experimental conditions.
Journal Article
Biological systems modeling and analysis: a biomolecular technique of the twenty-first century.
TL;DR: It is proposed that computational systems biology should be considered a biomolecular technique of the twenty-first century, because it complements experimental biology and bioinformatics in unique ways that will eventually lead to insights and a depth of understanding not achievable without systems approaches.