• Training structure

    Faculty of Science

Description

This course unit aims to provide a broad overview of emerging quantitative interdisciplinary fields in biosciences, ranging from cutting-edge experimental techniques in microscopy and synthetic biology to systemic approaches.

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

The main topics will first be introduced through traditional lectures and then 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 bibliographic studies, the use of existing code, or the development of new code (depending on the student's experience) and will constitute half of the final assessment.

 

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Objectives

  • Ability to approach a biological system from a quantitative perspective, either through modeling or data analysis;
  • Understanding how the fundamentals of microscopy (interference in optical systems, diffraction, etc.) and imaging systems (widefield, confocal, etc.) apply to cutting-edge techniques;
  • Understanding the principles of genetic design.
  • Learn how to apply and/or develop simple Python programs to simulate and analyze data (images or large genomic datasets).

 

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Teaching hours

  • Introduction to Quantitative Biology - LectureLecture40 hours

Mandatory prerequisites

Good knowledge of the fundamentals of biochemistry, molecular biology, mathematics, and physics at the bachelor's degree level.

Differential equations, Fourier transform, complex numbers.

Basic probability theory.


Recommended prerequisites: 

Bootcamp (HAV704V)

 

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Syllabus

  • Introduction: quantitative biology as a cross-disciplinary meeting point;
  • 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 

 

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