Bayesian Model Selection Methods and Their Application to Biological ODE Systems

Hug, Sabine; Schmidl, Daniel; Li, Wei Bo; Greiter, Matthias; Theis, Fabian J.

doi:10.1007/978-3-319-21296-8_10

Cited by 10 publications

(5 citation statements)

References 30 publications

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“…posterior ratios for relative model probabilities from Bayes factors and the approximate Bayesian information criterion) as well as techniques such as cross-validation and bootstrapping. Bayesian model selection for biological dynamical systems is further elaborated by Hug et al [14], working with the Bayes factor computed by Thermodynamic Integration. Fundamentally different approaches to model selection (as compared to Bayesian approaches) are also treated, e.g.…”

Section: Model Selection and Parameter Optimisationmentioning

confidence: 99%

“…All techniques are illustrated with examples ranging from simple, and sometimes analytically tractable problems, to medium sized models composed of ordinary differential equations. Information on how the most important results can be derived is provided in [7], alongside with a discussion on differences between methods ( [7] and [14]) and how these methods can be employed in practice as there is no generally applicable method for model assessment that is valid in all situations.…”

Section: Model Selection and Parameter Optimisationmentioning

confidence: 99%

“…The structure of this chapter (and the whole book) follows that of the model development life cycle, starting with model establishment over parameter optimisation and model adaptation and ending with model prediction. [6]; C3, F2 [8]; C4, F1 [9]; C6, F4 [11]; C9, F3 [7]; C10, F5 [14]; C11, F5 [15]; C12, F1 [17]; C13, F16 [16]; C16, F3 [20]; C16, F3 [20]; C17, F3 [21].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Introduction to Uncertainty in the Development of Computational Models of Biological Processes

Geris

Gomez-Cabrero

2015

Uncertainty in Biology

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This chapter aims to provide an introduction to the different ways in which uncertainty can be dealt with computational modelling of biological processes. The first step is model establishment under uncertainty. Once models have been established, data can further be used to select which of the proposed models best meets the predefined criteria. Subsequently, parameter values can be optimized for a specific model configuration. Sensitivity analyses allow to assess the influence of the previous choices on the model output. Additionally, model adaptation permits to focus on specific aspects of the model without losing its global predictive capacity. Finally, predictions with the established models should also consider the effect of uncertainty in the model development process.

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Section: Model Selection and Parameter Optimisationmentioning

confidence: 99%

Section: Model Selection and Parameter Optimisationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Introduction to Uncertainty in the Development of Computational Models of Biological Processes

Geris

Gomez-Cabrero

2015

Uncertainty in Biology

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“…However, the efficiency of Chib's method depends on how close an arbitrary value is to the posterior mode (Dai and Liu, 2022). Hug et al (2016) illustrated that Chib's method significantly underestimates the marginal likelihood of a bimodal Gaussian mixture model.…”

Section: Introductionmentioning

confidence: 99%

Selecting a conceptual hydrological model using Bayes' factors computed with Replica Exchange Hamiltonian Monte Carlo and Thermodynamic Integration

Mingo,

Nijzink,

Ley

et al. 2024

Preprint

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Abstract. We develop a method for computing Bayes’ factors of conceptual rainfall-runoff models based on thermodynamic integration, gradient-based replica-exchange Markov Chain Monte Carlo algorithms and modern differentiable programming languages. We apply our approach to the problem of choosing from a set of conceptual bucket-type models with increasing dynamical complexity calibrated against both synthetically generated and real runoff data from Magela Creek, Australia. We show that using the proposed methodology the Bayes factor can be used to select a parsimonious model and can be computed robustly in a few hours on modern computing hardware. We introduce formal posterior predictive checks for the selected model. The prior calibrated posterior predictive p-value, which also tests for prior data conflict, is used for the posterior predictive checks. Prior data conflict is when the prior favours parameter values that are less likely given the data.

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“…This is a pertinent problem, for instance, for cell signaling studies [13]. If there are few competing hypotheses on mechanismsleading to few possible model topologies-they can be enumerated and, for example, one can apply ABC to each model topology to select the topology that is most consistent with the data [10,[14][15][16][17]. Such Bayesian model selection has been successful for elucidating mechanisms of mammalian epidermal growth factor (EGF) [18] and target of rapamycin (TOR) [19] signaling and of gene regulatory networks in yeast nutrient sensing [20].…”

Section: Introductionmentioning

confidence: 99%

TopoFilter: a MATLAB package for mechanistic model identification in systems biology

et al. 2020

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Background: To develop mechanistic dynamic models in systems biology, one often needs to identify all (or minimal) representations of the biological processes that are consistent with experimental data, out of a potentially large set of hypothetical mechanisms. However, a simple enumeration of all alternatives becomes quickly intractable when the number of model parameters grows. Selecting appropriate dynamic models out of a large ensemble of models, taking the uncertainty in our biological knowledge and in the experimental data into account, is therefore a key current problem in systems biology. Results: The TopoFilter package addresses this problem in a heuristic and automated fashion by implementing the previously described topological filtering method for Bayesian model selection. It includes a core heuristic for searching the space of submodels of a parametrized model, coupled with a sampling-based exploration of the parameter space. Recent developments of the method allow to balance exhaustiveness and speed of the model space search, to efficiently re-sample parameters, to parallelize the search, and to use custom scoring functions. We use a theoretical example to motivate these features and then demonstrate TopoFilter's applicability for a yeast signaling network with more than 250'000 possible model structures. Conclusions: TopoFilter is a flexible software framework that makes Bayesian model selection and reduction efficient and scalable to network models of a complexity that represents contemporary problems in, for example, cell signaling. TopoFilter is open-source, available under the GPL-3.0 license at https://gitlab.com/csb.ethz/TopoFilter. It includes installation instructions, a quickstart guide, a description of all package options, and multiple examples.

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Bayesian Model Selection Methods and Their Application to Biological ODE Systems

Cited by 10 publications

References 30 publications

An Introduction to Uncertainty in the Development of Computational Models of Biological Processes

An Introduction to Uncertainty in the Development of Computational Models of Biological Processes

Selecting a conceptual hydrological model using Bayes' factors computed with Replica Exchange Hamiltonian Monte Carlo and Thermodynamic Integration

TopoFilter: a MATLAB package for mechanistic model identification in systems biology

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