EZO-22306 Concepts and Approaches in Developmental Biology

Course

Credits 6.00

Language of instruction:

Dutch and/or English

Assumed knowledge on:

First year courses in biology, including Mathematics 1 (MAT-14803) and MAT-14903 Mathematics 2 (MAT-14903) and Statistics 1 (MAT-15303).

Continuation courses:

EZO-30306 Developmental biology of animals;
MOB-30806 Regulation of plant development. MOB-31303 Molecular development.

Contents:

Biological systems, ranging from the simplest living cells, through multicellular organisms, populations of individual organisms, to whole ecosystems, are arguably the most complex systems science tries to understand. To do so, our primary method is making observations. But how do we organise these observations into coherent explanations that provide useful insights? This inevitably requires some form of prior hypotheses; 'mental maps' of how we believe the world around us is structured and operates. This in turn raises the question of how we can determine whether our hypotheses actually fit the reality we are trying to understand. This is where models step in: they are crucial in translating our hypotheses into concrete predictions about our objects of study that can subsequently be tested by targeted experiments. The more complex the systems we study, the more crucial the use of models. There is a key role for one of the strongest tools in our scientific toolkit: mathematics, the universal language of patterns and relationships. Mathematics is able to capture complexity in a form that makes it manageable. Mathematical models allow a full exploration of the consequences of our hypotheses and provide quantitative, and therefore precisely testable, predictions. Nowadays, this modelling toolkit is vastly extended by the wide availability of computers, which allow us to implement and test mathematical models, or even directly simulate the behaviour of complex systems.
In this course you will become acquainted with the 'spirit' of modelling biology as it is applied to a range of different phenomena. These phenomena occur at all the different scales of space and time where biological processes take place, from intra-cellular molecular events at the nanometer and millisecond scale, to populations dynamics of animals dispersed over wide areas and changing over the scale of years. Nevertheless, all these phenomena share deep interconnections. It is precisely these interconnections that are brought to light by modelling: the same notion of diffusion e.g. applies to the random motion of individual enzymes, to small changes accumulating over time in genomes and the way diseases spread through spatially dispersed populations. This reveals another strength of modelling: insights gained in one area of study can often be used to understand analogous phenomena in other areas.

Learning outcomes:

After successful completion of this course students are expected to student should be able to:
- identify the key components of a relatively complex biological system;
- draw a simplified graphical representation of a complex biological system;
- write down the mathematical equations governing this graphical representation;
- analyse the system of governing equations to make predictions about the biological system;
- write a simple computer program in Python to simulate the simplified biological system;
- interpret the predictions and simulations of biological systems.

Activities:

Lectures present modelling tools (mathematics, programming) that are practiced and applied to biological examples in tutorials. At the end of each week the answers to the tutorial exercises become available for evaluation prior to a general discussion to close the week.

Examination:

- written exam with open questions (90%);
- presentations, including discussion (10%);
- 5 compulsory reports (individual), marks count if the average of the other marks is in between 5.0 and 5.5.

Literature:

A manual can be bought in the WUR-shop.