Bayesian inference of dynamical model evaluating deltamethrin effect on daphnia survival

Abdoulaye Diouf*, Hela Toumi, Diene Ngom, Baba Issa Camara, Vincent Felten, Jean Francois Ferard

Abstract


The estimation of toxicokinetic and toxicodynamic (TK-TD) models pa- rameters is a real problem in research. These models highlight a dynamics of internalisation of a toxic compound and a dynamics of the damage that this contaminant will cause on an organism and of possible repairs on the latter. This coupling TK-TD makes it possible to connect these measure- ments at different times with the same set of parameters sometimes very important in number. In this paper, the focus is on assessing the long-term impact of deltamethrin effects on a sample of daphnia magna survival. We apply the Bayesian inference method to our survival data available in the Interdisciplinary Laboratory of Continental Environments (L.I.E.C) for es- timating parameters from a survival model developed in Soren Vogel et al 2016 [1]. As for the estimation of the survival model parameters applied to our data, we use the downloadable library deBInfer [2] in the Compre- hensive R Archive Network (CRAN), the directory deposite of R packages. It is a powerful approach offering a rigorous methodology for parameters inference well by modeling the existing links between unobservable states of a model, parameters and observable quantities. 


Full Text:

PDF

References


Albert C, Vogel S, Ashauer R (2016) Computationally Efficient Im- plementation of a Novel Algorithm for the General Unified Thresh- old Model of Survival (GUTS). PLoS Comput Biol 12(6): e1004978. doi:10.1371/journal.pcbi.1004978

Philipp H, Boersch-Supan, Sadie J Ryan, and Leah R Johnson (2016). deBInfer: Bayesian inference for dynamical models of biological systems in R. arXiv:1605.00021. URL https://arxiv.org/abs/1605.00021


Refbacks

  • There are currently no refbacks.