A hybrid approach for efficient and robust parameter estimation in biochemical pathways.

By M. Rodriguez-Fernandez, Pedro Mendes, Julio R. Banga. Bio Systems, 2006

346 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a0b7bdeae0f6737d4247027583f27f528757f511

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Maizar Raissi
2. G. Em Karniadakis
3. Hulin Wu

In Molecular Biology

1. Lu Lu
2. G. Em Karniadakis
3. Douglas A. Lauffenburger
4. Peter K. Sorger
5. Michael Hecker
6. Maizar Raissi
7. Susanne Töpfer
8. William W. Chen

Citations in the corpus

• ON IDENTIFIABILITY OF NONLINEAR ODE MODELS AND APPLICATIONS IN VIRAL DYNAMICS (2011, methodology): “[92, 93] proposed an alternative approach for practical identifiability analysis of ODE models by examining the correlations between model parameters.”
• Input–output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data (2009, methodology): “…precluded parameter optimization through local optimization methods such as Levenberg–Marquardt (Mar-quardt, 1963) or simple measures of convergence such as Fisher information (Rodriguez-Fernandez et al , 2006) and instead required broad and repeated searches through parameter space for good fits.”
• Universally Sloppy Parameter Sensitivities in Systems Biology Models (2007, background): “On the other hand, collectively fitting parameters [11, 12] by optimizing the agreement between the model and available data often yields large parameter uncertainties [13, 14, 15].”
• Gene regulatory network inference: Data integration in dynamic models - A review (2009, background): “Here, the focus lies on parameter estimation methods that surmount inherent ill-conditioning and multi-modality (Rodriguez-Fernandez et al., 2006; Moles et al., 2003).”
• Complex Parameter Landscape for a Complex Neuron Model (2006, background): “Such hybrid optimization has been proven to work very efficiently [14].”
• Systems biology informed deep learning for inferring parameters and hidden dynamics (2019, methodology): “( S1) and the correlation matrix R derived from the FIM.”