Introduction to quantitative Biology

This EU aims to provide a broad overview of emerging quantitative interdisciplinary fields in bioscience, ranging from advanced experimental techniques in microscopy and synthetic biology, to systems approaches.

In an innovative way, these methodological aspects will be presented in the context of biological and biophysical concepts such as robustness and optimality of biological systems, gene regulation and the fundamental principles underlying membrane and genome organization.

The main topics will be introduced first with traditional lectures and will be developed through individual or team projects where students will learn to apply specific techniques through examples, and see how these can be used to explore specific biological questions. These projects will involve literature reviews, use of existing code, or development of new code (depending on the student's experience) and will constitute half of the final assessment.

• Ability to approach a biological system with a quantitative perspective, either through modeling or data analysis;
• Understand how the basics of microscopy (interference of optical systems, diffraction, etc.), imaging systems (widefield, confocal, etc.) apply to advanced techniques;
• Understand the principles of genetic design.
• Learn to apply and/or develop simple python programs to simulate and analyze data (images or large genomic data sets).

Good knowledge of basic biochemistry, molecular biology, mathematics and physics at the undergraduate level.

Differential equations, Fourier transform, complex numbers.

Basic probability theory.

Recommended prerequisites: 

Bootcamp (HAV704V)

• Introduction: quantitative biology as a crossroads of disciplines;
• Key concepts in biology: robustness and optimality
• Random walks and stochasticity in biology
• Introduction to biological networks
• Transcription networks and gene regulation.
• Genome biophysics
• Membrane biophysics