Treffer: Modeling gene regulatory networks with piecewise linear differential equations : Challenges of Continous Optimization in Theory and Applications
Title:
Modeling gene regulatory networks with piecewise linear differential equations : Challenges of Continous Optimization in Theory and Applications
Authors:
Source:
European journal of operational research. 181(3):1148-1165
Publisher Information:
Amsterdam: Elsevier, 2007.
Publication Year:
2007
Physical Description:
print, 29 ref
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Inférence à partir de processus stochastiques; analyse des séries temporelles, Inference from stochastic processes; time series analysis, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Ajustement courbe, Curve fitting, Ajustamiento curva, Bioinformatique, Bioinformatics, Bioinformática, Equation différentielle linéaire, Linear differential equations, Linéarisation morceau, Piecewise linearization, Linearización trozo, Modèle donnée, Data models, Modélisation, Modeling, Modelización, Méthode approximation, Approximation method, Méthode moindre carré, Least squares method, Método cuadrado menor, Optimisation, Optimization, Optimización, Réglementation, Regulation, Reglamentación, Système dynamique, Dynamical system, Sistema dinámico, Système hybride, Hybrid system, Sistema híbrido, Série temporelle, Time series, Serie temporal, Technique linéaire par morceau, Piecewise-linear techniques, Computational biology, Gene regulation, Optimization problem, System of piecewise linear differential equations
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Center for Applied Computer Science, University of Cologne, Weyertal 80, 50931 Cologne, Germany
Institute of Applied Mathematics, Middle East Technical University (METU), 06531 Ankara, Turkey
Institute of Applied Mathematics, Middle East Technical University (METU), 06531 Ankara, Turkey
ISSN:
0377-2217
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Operational research. Management
Operational research. Management
Accession Number:
edscal.18674431
Database:
PASCAL Archive
Weitere Informationen
Microarray chips generate large amounts of data about a cell's state. In our work we want to analyze these data in order to describe the regulation processes within a cell. Therefore, we build a model which is capable of capturing the most relevant regulating interactions and present an approach how to calculate the parameters for the model from time-series data. This approach uses the discrete approximation method of least squares to solve a data fitting modeling problem. Furthermore, we analyze the features of our proposed system, i.e., which kinds of dynamical behaviors the system is able to show.