Computational procedures for optimal experimental design in biological systems.
about
New types of experimental data shape the use of enzyme kinetics for dynamic network modelingReverse engineering and identification in systems biology: strategies, perspectives and challengesOptimal experimental design for parameter estimation of a cell signaling modelAn improved swarm optimization for parameter estimation and biological model selectionBridging Mechanistic and Phenomenological Models of Complex Biological SystemsInferring human microbial dynamics from temporal metagenomics data: Pitfalls and lessons.Developing optimal input design strategies in cancer systems biology with applications to microfluidic device engineering.An iterative identification procedure for dynamic modeling of biochemical networksDiscriminating between rival biochemical network models: three approaches to optimal experiment design.Modeling and analysis of the dynamic behavior of the XlnR regulon in Aspergillus nigerStructural identifiability of systems biology models: a critical comparison of methodsSet membership experimental design for biological systems.Efficient reverse-engineering of a developmental gene regulatory network.Reverse-engineering post-transcriptional regulation of gap genes in Drosophila melanogasterA Bayesian active learning strategy for sequential experimental design in systems biology.The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems.Predicting network modules of cell cycle regulators using relative protein abundance statistics.Near-optimal experimental design for model selection in systems biologyModel-based design of experiments for cellular processes.Optimal design of stimulus experiments for robust discrimination of biochemical reaction networks.Experimental design schemes for learning Boolean network models.AMIGO, a toolbox for advanced model identification in systems biology using global optimization.A novel method for multifactorial bio-chemical experiments design based on combinational design theory.Selecting the best design for nonstandard toxicology experiments.Cause and cure of sloppiness in ordinary differential equation models.A global parallel model based design of experiments method to minimize model output uncertainty.A model-based strategy to investigate the role of microRNA regulation in cancer signalling networks.Integration of sensitivity and bifurcation analysis to detect critical processes in a model combining signalling and cell population dynamics
P2860
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P2860
Computational procedures for optimal experimental design in biological systems.
description
2008 nî lūn-bûn
@nan
2008年の論文
@ja
2008年学术文章
@wuu
2008年学术文章
@zh
2008年学术文章
@zh-cn
2008年学术文章
@zh-hans
2008年学术文章
@zh-my
2008年学术文章
@zh-sg
2008年學術文章
@yue
2008年學術文章
@zh-hant
name
Computational procedures for optimal experimental design in biological systems.
@en
Computational procedures for optimal experimental design in biological systems.
@nl
type
label
Computational procedures for optimal experimental design in biological systems.
@en
Computational procedures for optimal experimental design in biological systems.
@nl
prefLabel
Computational procedures for optimal experimental design in biological systems.
@en
Computational procedures for optimal experimental design in biological systems.
@nl
P356
P1433
P1476
Computational procedures for optimal experimental design in biological systems.
@en
P2093
P304
P356
10.1049/IET-SYB:20070069
P577
2008-07-01T00:00:00Z