openGUTS logo
Supporting mechanistic modelling for survival

DEBtox Researc

About the openGUTS project

Project team.
openGUTS is created by the following people; shown with their main responsibilities:
  • Roman Ashauer, Univ. of York (currently working with Syngenta), project management and organisation.
  • Tjalling Jager, DEBtox Research, development of the Matlab version (started as prototype for the standalone version).
  • WSC Scientific GmbH, development of the standalone version 1.0 of openGUTS.
  • RIFCON GmbH was responsible for the development of the standalone version 1.1-1.2.

Funding. For version 1.0: Cefic-LRI through the project ECO39.2. For Version 1.1-1.2: Syngenta Crop Protection AG.

History. In 2017, Roman and Tjalling obtained funding from Cefic-LRI to write an extensive e-book on GUTS and perform a ring test on the available software implementations. One of the conclusions from that project was that there was a need for a robust and user-friendly standalone software to support chemical risk assessment. The software would follow the workflow proposed by EFSA in their Scientific Opinion on TKTD models for risk assessment of pesticides. However, the potential applications of the software are much wider (including scientific research).
    Cefic-LRI funded an extension of the project that started in 2018 with the development of a first version of a Matlab protoype, which was used to discuss various options for the software in a stakeholder workshop held in York in November 2018. After that, the prototype was finalised and WSC started on the task of developing the standalone software, using the prototype as the blueprint for the model calculations.
    In 2020, Syngenta funded an update of the standalone to Version 1.1, which was released in February 2021. This update fixes a calculation error in the IT model code, and extends the batch calculations of the LPx. A further update was released in March 2023 to fix an error message for some (rather extreme) data sets, and to provide extra time points for LCx estimates to support risk assessment for bees.

License information. openGUTS is free and open source software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This holds both for the standalone version as for the Matlab version. This ensures that openGUTS will stay in the public domain. Information about GPL on Wikipedia.

Technicalities. openGUTS is based on 'frequentist inference' (likelihood-based) and applies a combination of grid search, genetic algorithm, and likelihood profiling to explore parameter space to find the optimum (the best-fitting parameters) and a sample to be used for error propagation. The GUTS models that are implemented are the reduced models for pure stochastic death (SD) and pure individual tolerance (IT). Analytical solutions are applied as much as possible, although numerical integration is needed for SD models when faced with time-varying exposure. More technical background can be found in the e-book and the design document (see download page). The e-book also contains an appendix that explains how frequentists can use a sample from parameter space for error propagation (which is not trivial, at least not for me).

About GUTS

What is GUTS. GUTS stands for the General Unified Threshold model for Survival. It is a framework that unifies (almost) all toxicokinetic-toxicodynamic (TKTD) models for the endpoint survival. GUTS was born out of a workshop, organised by Roman Ashauer and Thomas Preuss in 2010, to discuss the differences and similarities between various survival models that were in use at the time. It turned out that all of these models could be seen as special cases of a single over-arching framework. GUTS was originally published in an ES&T paper in 2011. In 2015, a second workshop was organised to streamline the experiences with GUTS over the last 5 years, and to prepare the framework for more routine use in risk assessment.

Recent developments. In 2017-2018, the developments of GUTS took a major leap forward with the preparation of the e-book on GUTS (which is now the definitive guide to the framework) and the preparation of the EFSA opinion on TKTD models (which judged GUTS as "ready for use" in risk assessment of pesticides). More information on GUTS on
  In 2023, EFSA released a revised guidance document on risk assessment for bees, which prescribes the use of GUTS for extrapolating toxicity tests to longer time scales. In response to this guidance, openGUTS was updated in 2024 to v1.2 to output the specific time points required for bee risk assessment. GUTS is also mentioned as a possible tool in the 2023 EFSA guidance for birds and mammals.
  With version 3.0 of the GUTS e-book, we added a case study with openGUTS.

What about sub-lethal effects. Note that GUTS is a model for survival only, and for other all-or-nothing responses that can be treated as non-reversible (e.g., immobility, in many cases). GUTS cannot be used for continuous endpoints such as growth and reproduction, which require a DEBtox model. For DEBtox calibration to data, there is currently no user-friendly (GUI-based) software, but several BYOM packages under Matlab are available.

Super-short description of the reduced GUTS models

Overview. In the reduced GUTS models, toxicokinetics is combined with damage dynamics into a single compartment. This compartment has first-order kinetics, determined by a 'dominant' rate constant kd. The scaled damage is subsequently linked to the death mechanism. The openGUTS software includes only the two extreme cases of pure stochastic death (SD) and pure individual tolerance (IT). Under SD, each individual is identical, and damage above a threshold value (mw) increases the probability to die (with effect strength, or killing rate, bw). Under IT, individuals differ in the value of the threshold (mw), which follows a frequency distribution in the population with a certain width (the spread factor, Fs). There may also be deaths that are unrelated to the chemical, which is covered by a constant background hazard rate (hb).

GUTS reduced scheme

Statistics. The expected and observed mortality are compared in a likelihood framework, using the multinomial distribution. The multinomial distribution is an extension of the binomial distribution to more than two possible outcomes. Each individual will die in one of the intervals of the test: either between two observation times or after the test has ended. The multinomial likelihood function uses the predicted death in each interval (calculated from the model) and the observed deaths in each interval. As long as the death of each individual is independent from the survival or death of the others (which would generally be the case), this statistical framework is a perfect match to the problem.

Multinomial deaths

More-complex GUTS models. The GUTS framework can be used to derive more complex models as well. For example, models that combine SD and IT (each individual draws a threshold from a distribution, but also has a probability to die), and models with separate compartments for toxicokinetics and damage (the 'full' model). These are not implemented in openGUTS but they are part of the BYOM-GUTS package.

About this website

This website is maintained by Tjalling Jager (DEBtox Research), and started on 14 May 2019.

UnivYork logo

DEBtox Res

WSC logo

CeficLRI logo

Rifcon logo



The openGUTS project, This site is maintained by Tjalling Jager, email: tjalling (at)