Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data.
Variability is an intrinsic property of biological systems and is often at the heart of their complex behaviour. Examples range from cell-to-cell variability in cell signalling pathways to variability in the response to treatment across patients. A popular approach to model and understand this varia...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-05-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1011135 |
| _version_ | 1797802663101857792 |
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| author | David Augustin Ben Lambert Ken Wang Antje-Christine Walz Martin Robinson David Gavaghan |
| author_facet | David Augustin Ben Lambert Ken Wang Antje-Christine Walz Martin Robinson David Gavaghan |
| author_sort | David Augustin |
| collection | DOAJ |
| description | Variability is an intrinsic property of biological systems and is often at the heart of their complex behaviour. Examples range from cell-to-cell variability in cell signalling pathways to variability in the response to treatment across patients. A popular approach to model and understand this variability is nonlinear mixed effects (NLME) modelling. However, estimating the parameters of NLME models from measurements quickly becomes computationally expensive as the number of measured individuals grows, making NLME inference intractable for datasets with thousands of measured individuals. This shortcoming is particularly limiting for snapshot datasets, common e.g. in cell biology, where high-throughput measurement techniques provide large numbers of single cell measurements. We introduce a novel approach for the estimation of NLME model parameters from snapshot measurements, which we call filter inference. Filter inference uses measurements of simulated individuals to define an approximate likelihood for the model parameters, avoiding the computational limitations of traditional NLME inference approaches and making efficient inferences from snapshot measurements possible. Filter inference also scales well with the number of model parameters, using state-of-the-art gradient-based MCMC algorithms such as the No-U-Turn Sampler (NUTS). We demonstrate the properties of filter inference using examples from early cancer growth modelling and from epidermal growth factor signalling pathway modelling. |
| first_indexed | 2024-03-13T05:10:08Z |
| format | Article |
| id | doaj.art-984f3332f1044c98ae97de2b067c856a |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-13T05:10:08Z |
| publishDate | 2023-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-984f3332f1044c98ae97de2b067c856a2023-06-16T05:30:40ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582023-05-01195e101113510.1371/journal.pcbi.1011135Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data.David AugustinBen LambertKen WangAntje-Christine WalzMartin RobinsonDavid GavaghanVariability is an intrinsic property of biological systems and is often at the heart of their complex behaviour. Examples range from cell-to-cell variability in cell signalling pathways to variability in the response to treatment across patients. A popular approach to model and understand this variability is nonlinear mixed effects (NLME) modelling. However, estimating the parameters of NLME models from measurements quickly becomes computationally expensive as the number of measured individuals grows, making NLME inference intractable for datasets with thousands of measured individuals. This shortcoming is particularly limiting for snapshot datasets, common e.g. in cell biology, where high-throughput measurement techniques provide large numbers of single cell measurements. We introduce a novel approach for the estimation of NLME model parameters from snapshot measurements, which we call filter inference. Filter inference uses measurements of simulated individuals to define an approximate likelihood for the model parameters, avoiding the computational limitations of traditional NLME inference approaches and making efficient inferences from snapshot measurements possible. Filter inference also scales well with the number of model parameters, using state-of-the-art gradient-based MCMC algorithms such as the No-U-Turn Sampler (NUTS). We demonstrate the properties of filter inference using examples from early cancer growth modelling and from epidermal growth factor signalling pathway modelling.https://doi.org/10.1371/journal.pcbi.1011135 |
| spellingShingle | David Augustin Ben Lambert Ken Wang Antje-Christine Walz Martin Robinson David Gavaghan Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data. PLoS Computational Biology |
| title | Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data. |
| title_full | Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data. |
| title_fullStr | Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data. |
| title_full_unstemmed | Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data. |
| title_short | Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data. |
| title_sort | filter inference a scalable nonlinear mixed effects inference approach for snapshot time series data |
| url | https://doi.org/10.1371/journal.pcbi.1011135 |
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