DEBtox information
Making sense of ecotoxicity test results



Main menu


Submenu

DEBtox information

DEBtox information

Software / Links to other software

Miscellaneous software


On this page, I will collect links to freely-available software packages provided by other parties to perform calculations on GUTS and DEB(tox). I have not been involved in the production of these packages (apart from openGUTS), so I cannot provide any assistance or support for them. I am responsible for the Matlab implementations of GUTS, DEBtox and DEBkiss as part of the BYOM platform. Several more implementations for GUTS are known to exist, of which a number is planned to be offered for free download by their developers in the near future. When more information is available I will update the list below.

Web-based GUTS calculations


A web-based and user-friendly way to perform GUTS calculations (reduced SD and IT models only) is available in the MOSAIC facility at https://mosaic.univ-lyon1.fr/guts. See also the papers of Baudrot et al (2018) and Charles et al (2022). Under the hood, it applies Bayesian inference with the MORSE R-package. This is a very elegant implementation, but the user needs to realise that the software applies weakly informative prior distributions (i.e., the output is more than just the information from the data set). The priors are based on test design, which I believe is awkward (see my comment in ES&T and the author's response to that). In general, these priors will not affect the results. However, in specific cases (when a parameter runs away to zero or infinity, e.g., 'slow kinetics'), they will influence the results (to some extent). In the current version, these cases are flagged with a warning, so the user is aware that the priors are affecting the results.

This touches upon a general issue for Bayesian inference (and MCMC) when the posterior is insufficiently constrained by the data (and becomes 'improper'). For more information I suggest the excellent and readable paper from Raue et al. (2013). At this moment, it is unclear to me how other Bayesian GUTS applications address this issue.

Standalone GUTS software


In December 2019, the first version of the openGUTS standalone software was released. This is an easy-to-use and robust software that runs under Windows. It can be freely downloaded from the dedicated website http://openguts.info/. There is also a Matlab version of openGUTS, available from the same website.

In June 2018, the Fraunhofer Institute has launched the first version of their standalone GUTS-3S. More information. A test version of this software was included in the ring test (which is part of the GUTS e-book). I have not looked at this version yet, so I cannot provide you with my opinion just yet.

A user interface for the GUTS-R (see below) package is currently being developed by RIFCON, turning it into a standalone software under the name EasyGUTS (presented in SETAC posters in 2017 and 2018). This version is planned to become available for free download, but is not available yet (only on request). This version should be able to do the reduced GUTS models (also SD and IT combined). Mentioned on the Rifcon downloads site under 'download software'.

R-packages for GUTS


Two R-packages are available to make the GUTS calculations in a Bayesian framework. The first package was developed by Carlo Albert & Sören Vogel, is currently maintained by RIFCON, and can be found at https://cran.r-project.org/package=GUTS. I am not sure if and how they deal with the issue of 'improper posteriors' noted above for the web-based MOSAIC software.

The second is the MORSE R-package, as developed and maintained by the University of Lyon. It contains the reduced SD and IT models. This package can be found at https://cran.r-project.org/package=morse. There is a web-based interface around it as well (see previous item above).

Python-package for GUTS


A Python toolbox is available for GUTS calculations. The toolbox is developed and maintained by Raymond Nepstad (SINTEF, Trondheim), and can be downloaded from GitHub: https://github.com/nepstad/epytox 

Web-based DEBtox predictions


Take a look at the DeEP tool for EPx predictions with DEBtox2019. Note that this tool does not perform calibrations or validations on data sets, but rather makes EPx predictions for exposure profiles, such as those from FOCUS. As input, it requires model parameters to be entered as well as an exposure profile (in simple text format). A possibility to automatically translate BYOM output to a DeEP input file is being considered at the moment.

DEB-IBM in NETlogo


The individual-based modelling implementation of DEB in NetLogo of Martin et al. (2012) can be found CREAM website or on Ben Martin's personal pages. This is a flexible model for population dynamics using DEB individuals, and quite user friendly. The associated publication is:

Martin B, Zimmer EI, Grimm V and Jager T (2012). Dynamic Energy Budget theory meets individual-based modelling: a generic and accessible implementation. Methods in Ecology and Evolution 3:445-449. http://dx.doi.org/10.1111/j.2041-210X.2011.00168.x

DEBtool in Matlab


DEBtool is an extensive library of code in Matlab to perform a wide range of DEB calculations. Originally developed by Bas Kooijman, but now maintained and developed by a larger group of experts. DEBtool can be found in the DEB portal, and that is the best place to get more information about it. In my opinion, the correct use of this software requires a sturdy background knowledge of DEB theory, and user-friendliness is limited. The DEBtool software is used in the DEB course, so the course is the best place to get started with it.




The DEBtox information site, www.debtox.info, since July 2011