Bayesian ranking of biochemical system models

By Vladislav Vyshemirsky, Mark A. Girolami. 2008

222 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Control functionals for Monte Carlo integration (2014, methodology): “and Girolami, 2009). Recent empirical investigations recommend thermodynamic integration (TI) as one of the best-performing approaches to the estimation of normalising constants in complex 18 models (Vyshemirsky and Girolami, 2008; Friel and Wyse, 2012; Hug et al., 2015). The control variate methodo”
Stochastic modelling for quantitative description of heterogeneous biological systems (2009, methodology): “All of the issues described here can be directly addressed using a Bayesian statistical approach using Markov chain Monte Carlo (MCMC) technique”
Automated adaptive inference of phenomenological dynamical models (2015, unknown): “…control methods are well established in statistical inference 26 and in choosing a systems biology model for data from a ﬁnite set of models 27–29 , we believe that they have not been used yet in the context of inferring complex, nonlinear dynamics from an inﬁnite, complete set of all…”
The Controlled Thermodynamic Integral for Bayesian Model Comparison (2014, methodology): “Recent empirical investigations, including Vyshemirsky and Girolami (2008); Friel and Wyse (2012), have revealed that TI is among the most promising approach to estimation of model evidence.”
Riemann manifold Langevin and Hamiltonian Monte Carlo methods (2011, background): “An important class of problems recently gaining attention is the statistical analysis of uncertainty in dynamical systems defined by a system of non-linear differential equations (Ramsay et al., 2007; Calderhead and Girolami, 2009; Vyshemirsky and Girolami, 2008).”
Estimating Bayes factors via thermodynamic integration and population MCMC (2009, methodology): “Firstly, it allows us to obtain samples from all the required thermodynamic distributions simultaneously, and secondly, the population structure enables sampling from the multimodal probability distributions that are induced by nonlinear ODEs.”
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs (2018, background): “The second system to be analyzed is called Protein Transduction and was originally proposed in Vyshemirsky and Girolami (2008).”
Simulation-based model selection for dynamical systems in systems and population biology (2009, methodology): “The present approach enables us to employ the whole formal apparatus to any system that can be (efficiently) simulated, even when exact likelihoods are computationally intractable.”
The Controlled Thermodynamic Integral for Bayesian Model Evidence Evaluation (2016, background): “Recent empirical investigations, including Vyshemirsky and Girolami (2008) and Friel and Wyse (2012), have revealed that TI is among the most promising approach to estimation of model evidence.”
Mechanistic Hierarchical Gaussian Processes (2014, background): “Parametric models based upon ODEs do exist but are known to be inadequate for characterizing muscle force tracings (Herzog 2004).”
Identifying Causal Structure in Large-Scale Kinetic Systems (2018, methodology): “ity regularization with a model selection step based on the information criteria AIC and BIC. Common alternatives are based on approximate Bayesian computation or likelihood ratio [Toni et al., 2009, Vyshemirsky and Girolami, 2007]. Procedures using a Gaussian process prior on the solution of the di”
Gradient Matching Methods for Computational Inference in Mechanistic Models for Systems Biology: A Review and Comparative Analysis (2015, background): “These equations model protein signaling transduction pathways in a signal transduction cascade, where the free parameters are kinetic parameters governing how quickly the proteins (“species”) convert to one another [Vyshemirsky and Girolami (2008)].”
Comparing Families of Dynamic Causal Models (2010, methodology): “In a comparison of sample-based methods using synthetic data from biochemical networks, AIS provided the best balance between accuracy and computation time [13].”
Towards Automatic Model Comparison: An Adaptive Sequential Monte Carlo Approach (2013, methodology): “The use of AIS for computing normalising constants directly and via path sampling dates back at least to Neal (2001); see Vyshemirsky and Girolami (2008) for a recent example of its use in the computation of model evidences.”
Bayesian inference of chemical kinetic models from proposed reactions (2015, methodology): “Applications of these methods to chemical kinetics have been pursued in combustion modeling and systems biology when the number of competing models is small (Braman et al., 2013; Vyshemirsky and Girolami, 2008; Vyshemirsky et al., 2006; Willkinson, 2011; Xu et al., 2010; Zechner et al., 2014).”
Input–output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data (2009, methodology): “and Markov chain Monte Carlo methods will be required (Vyshemirsky and Girolami, 2008).”