The toxicity of a chemical depends on properties of the compound and of the species that is exposed, but also on the exposure time, the endpoint (e.g., reproduction or survival), and the exposure conditions (temperature, food level, etc.).
In ecotoxicology, these dependencies are generally ignored by rigid standardisation of the tests and descriptive summary statistics such as EC50 and NOEC. However, we need a more mechanistic interpretation of toxicity to make an unbiased comparison of toxicity between species and chemicals, and to extrapolate the effects to untested exposure conditions.
Because it is impossible to test all chemicals on all species under all possible exposure scenarios, extrapolation is of key importance for ecotoxicologists and environmental risk assessors.
Mathematical modelling is a powerful tool to interpret the results of laboratory toxicity tests and to make educated extrapolations. The process of mechanistically modelling toxicity can be divided into two steps: toxicokinetics (TK) and toxicodynamics (TD).
TK deals with the uptake, biotransformation and distribution of a chemical into the body of an organism, whereas TD deals with the next step: from internal concentration of the active compound to effects on the organism over time.
In this one-week course, you will learn the basics of TK and TD modelling, and how they can be linked to analyse and interpret toxicity data on a mechanistic basis. For TK modelling, we will focus on 1- and 2-compartment models; for TD modelling, we will focus on effects on life-history traits such as growth, reproduction and survival, which are essential for impacts at the population level. This will be accomplished by a combination of lectures, computer exercises, discussions and simple toxicity experiments.
In the computer exercises you will learn to build and use basic TKTD models yourself in Matlab, and work with more advanced pre-programmed models (GUTS and DEBtox) to fit more elaborate data sets and make excursions into population-level effects.
The output of the course will be individual reports where the students use their accomplished skills to fit TKTD models to their own and/or provided data, and to interpret the results.
Preparation for the course
Please see under course material.
Expected experience level of participants
This is not a math or stats course, but it will make use of mathematics and statistics. More specifically, (systems of) ordinary differential equations and basic likelihood functions. The "refresher", which can be found under course material
, should give you an idea what to expect. This is the level of knowledge that we will depart from in the course to teach you how to build, run, analyse and interpret TKTD models. In any case, we assume that you are familiar with mathematical functions, powers and logarithms, derivatives and integration, and basic statistical distributions such as the binomial and the normal.
Participants in the course will receive a temporary licence for MATLAB, and need to get acquinted with this software in the preparation phase (in "tele-course mode" before the "classroom mode" in Denmark). Note that the time you need for this is on top of the preparation time noted in the work load of the course! More information about the MATLAB experience required and a MATLAB tutorial can be found under course material.
In order to pass the course all participants must read the handouts provided before course start, get aquainted with MATLAB through the given tutorial and hand in a 10-page report at September 1st at the latest. The report should be approved by the course responsibles.
Please also remember to add the course to your PhD plan.