Decoupling dynamical systems for pathway identification from metabolic profiles

By Eberhard O. Voit, Jonas S. Almeida. Bioinform., 2004

321 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ace56d5c27c236bb2109ae7ce87e49c686ab77f5

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In Molecular Biology

Citations in the corpus

Parameter estimation of ODE’s via nonparametric estimators (2007, methodology): “In the same spirit, approximation of the solutions of the ODE are provided by smoothing methods such as local polynomial regression [22, 30, 11], or neural networks [42], within the same two-step estimation scheme.”
Recent Developments in Parameter Estimation and Structure Identification of Biochemical and Genomic Systems (2009, methodology): “Voit and Almeida [128] implemented the slope-estimation-decoupling strategy with an artificial neural network that simultaneously permitted data smoothing and the computer-algebraic computation of slopes from the smoothed time courses.”
√n-consistent parameter estimation for systems of ordinary differential equations : bypassing numerical integration via smoothing (2010, unknown): “For instance, a relatively simple example of a four-dimensional system considered in Appendix 1 of Voit and Almeida (2004) demonstrates that the need to repeat numerical integration multiple times might increase the computational time for numerical integration up to 95% of the total computational…”
Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling (2014, methodology): “This method also decouples the high-dimensional ODEs to allow us to perform variable selection and parameter estimation for one equation at a time (Voit and Almeida 2004; Jia, Stephanopoulos, and Gunawan 2011; Lu et al. 2011).”
Optimal rate of direct estimators in systems of ordinary differential equations linear in functions of the parameters (2013, unknown): “However, [45] demonstrate that the need to repeat numerical integration multiple times might increase the computational time for numerical integration up to 95% of the total computational time required for a gradient based optimization method (even in low dimensional systems).”