Model decomposition and reduction tools for large-scale networks in systems biology.

Model decomposition and reduction tools for large-scale networks in systems biology.

Biological system models are routinely developed in modern systems biology research following appropriate modelling/experiment design cycles. Frequently these take the form of high-dimensional nonlinear Ordinary Differential Equations that integrate information from several sources; they usually con...

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Bibliografiske detaljer
Main Authors:	Anderson, J, Chang, Y, Papachristodoulou, A
Format:	Journal article
Sprog:	English
Udgivet:	2011

Lignende værker

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Layered decomposition for the model order reduction of timescale separated biochemical reaction networks.
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Dynamical system decomposition for efficient, sparse analysis.
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Stability analysis of power systems using network decomposition and local gain analysis
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A Decomposition Technique for Nonlinear Dynamical System Analysis.
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A Network Decomposition Approach for Efficient Sum of Squares Programming Based Analysis
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Dynamical system decomposition using dissipation inequalities.
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Methodological frameworks for large-scale network analysis and design.
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On validation and invalidation of biological models
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Nonlinear Control of Large Scale complex Systems using Convex Control Design tools
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Analysis of non-polynomial systems using the sum of squares decomposition
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Analysis of nonlinear time-delay systems using the sum of squares decomposition
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Removal of Large-Scale Stripes Via Unidirectional Multiscale Decomposition
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Delay-dependent rendezvous and flocking of large scale multi-agent systems with communication delays.
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Analysis of Polynomial Systems With Time Delays via the Sum of Squares Decomposition.
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Structural identifiability of dynamic systems biology models
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Guaranteed error bounds for structured complexity reduction of biochemical networks.
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Web tools for large-scale 3D biological images and atlases
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Efficient, sparse biological network determination
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Model-constrained optimization methods for reduction of parameterized large-scale systems
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Model-Constrained Optimization Methods for Reduction of Parameterized Large-Scale Systems
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Chordal and factor-width decompositions for scalable semidefinite and polynomial optimization
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Tucker Decomposition-Based Network Compression for Anomaly Detection With Large-Scale Hyperspectral Images
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Decomposition techniques for large-scale optimization in the supply chain
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