Modeling Biological Data

This course is a natural continuation of the course “Quantification of Randomness” (HAV424B) offered in Semester 4. It is designed to provide students with the concepts needed to develop experimental protocols that address biological questions and to apply appropriate models for analyzing variability. The first part will focus on designing experimental protocols capable of addressing a wide range of questions in the life sciences, specifically by taking into account the inevitable dependencies among statistical individuals, such as kinship and the spatial or temporal structure of populations. This section will thus provide an opportunity to address the concepts of fluctuation, replication, and pseudo-replication, which will be incorporated into the models developed in the second part of the course. The second part will focus on demonstrating the link between the experimental protocol implemented and the modeling of the variability of a quantitative response variable, through the development of models that include several qualitative or quantitative variables. Particular attention will be paid to the conditions under which these methods are applicable, to Type I and Type II errors, to methods for estimating the parameters of the constructed models (including likelihood), and to the interpretation of the estimated parameters. Each concept will be illustrated through the analysis of real biological data from various fields, thereby helping students discover not only current and common biological questions but also the tools developed to address them. Hands-on exercises using R will enable students to independently perform analyses on published biological case studies.

- HAV312B, HAV313B, and HAV424B

- Use mathematical tools to build statistical models of variability and address issues in the life sciences

- adapt the protocol to the question asked

- fit a model to experimental data

- validate a model's predictions and assess its limitations