pyABC: distributed, likelihood-free inference

Klinger, Emmanuel; Rickert, Dennis; Hasenauer, Jan

doi:10.1093/bioinformatics/bty361

Cited by 92 publications

(95 citation statements)

References 9 publications

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“…To parameterize the motility of infected and uninfected cells in our CPM, we adjusted our model based on measurements from the time-lapse image analysis following both cell types for 1 h in loose collagen environments. We used the computational parallelization and high-performance approach “pyABC” 4,70 to automatically adjust simulation parameters to experimental data. The computational pipeline overcomes the problem of statistical interference for parameter fitting in stochastic multi-scale models by using a parallel approximate Bayesian computation sequential Monte Carlo (pABC-SMC) algorithm 4 .…”

Section: Methodsmentioning

confidence: 99%

Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures

et al. 2019

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Pathogens face varying microenvironments in vivo, but suitable experimental systems and analysis tools to dissect how three-dimensional (3D) tissue environments impact pathogen spread are lacking. Here we develop an Integrative method to Study Pathogen spread by Experiment and Computation within Tissue-like 3D cultures (INSPECT-3D), combining quantification of pathogen replication with imaging to study single-cell and cell population dynamics. We apply INSPECT-3D to analyze HIV-1 spread between primary human CD4 T-lymphocytes using collagen as tissue-like 3D-scaffold. Measurements of virus replication, infectivity, diffusion, cellular motility and interactions are combined by mathematical analyses into an integrated spatial infection model to estimate parameters governing HIV-1 spread. This reveals that environmental restrictions limit infection by cell-free virions but promote cell-associated HIV-1 transmission. Experimental validation identifies cell motility and density as essential determinants of efficacy and mode of HIV-1 spread in 3D. INSPECT-3D represents an adaptable method for quantitative time-resolved analyses of 3D pathogen spread.

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Section: Methodsmentioning

confidence: 99%

Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures

et al. 2019

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“…However, the implementation was problem-and architecture-specific and thus could not be applied more generally. In response, the Python package pyABC (Klinger, Rickert, & Hasenauer, 2018) was developed as a more flexible implementation of parallel ABC, supporting sharedand distributed-memory computing. In principle, pyABC can be used with any type of model, but its computational implementation must be expressed as a Python function.…”

Section: Discussionmentioning

confidence: 99%

Pakman: a modular, efficient and portable tool for approximate Bayesian inference

Pak¹,

Baker²,

Pitt-Francis³

2020

JOSS

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“…While the extension of our probabilistic towards symmetric cell divisions is straightforward, it is unclear whether one can find a tractable likelihood in this case. Although we did not give a tractable likelihood for this case, approximate Bayesian computation, probabilistic programming or other likelihood-free methods could be used for statistical inference in this case (Didelot et al, 2011; Klinger et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

How fast are cells dividing: Probabilistic model of continuous labeling assays

Rode

Goerke

Brusch

et al. 2019

Preprint

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Correct estimates of cell proliferation rates are crucial for quantitative models of the development, maintenance and regeneration of tissues. Continuous labeling assays are used to infer proliferation rates in vivo.So far, the experimental and theoretical study of continuous labeling assays focused on the dynamics of the mean labeling-fraction but neglected stochastic effects. To study the dynamics of the labeling-fraction in detail and fully exploit the information hidden in fluctuations, we developed a probabilistic model of continuous labeling assays which incorporates biological variability at different levels, between cells within a tissue sample but also between multiple tissue samples. Using stochastic simulations, we find systematic shifts of the mean-labeling fraction due to variability in cell cycle lengths. Using simulated data as ground truth, we show that current inference methods can give biased proliferation rate estimates with an error of up to 40 %. We derive the analytical solution for the Likelihood of our probabilistic model. We use this solution to infer unbiased proliferation rate estimates in a parameter recovery study. Furthermore, we show that the biological variability on different levels can be disentangled from the fluctuations in the labeling data. We implemented our model and the unbiased parameter estimation method as an open source Python tool and provide an easy to use web service for cell cycle length estimation from continuous labeling assays (https://imc.zih.tu-dresden.de/cellcycle).

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pyABC: distributed, likelihood-free inference

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 92 publications

References 9 publications

Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures

Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures

Pakman: a modular, efficient and portable tool for approximate Bayesian inference

How fast are cells dividing: Probabilistic model of continuous labeling assays

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