• Training Structure

    College of Sciences

Description

This course aims to provide a broad overview of emerging interdisciplinary quantitative fields in the biosciences, ranging from cutting-edge experimental techniques in microscopy and synthetic biology to systems-based 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 explored through individual or team projects, in which students will learn to apply specific techniques using examples and see how these techniques can be used to investigate specific biological questions. These projects will involve literature reviews, the use of existing code, or the development of new code (depending on the student’s experience) and will account for half of the final grade.

 

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Objectives

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

 

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Class Hours

  • Introduction to Quantitative Biology - LectureLecture40 hours

Mandatory Prerequisites

A solid understanding 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: 

Boot Camp (HAV704V)

 

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Course Outline

  • Introduction: Quantitative Biology as an Interdisciplinary Crossroads;
  • Key Concepts in Biology: Robustness and Optimality
  • Random Walks and Stochasticity in Biology
  • Introduction to Biological Networks
  • Transcription Networks and Gene Regulation.
  • Genome Biophysics
  • Biophysics of Membranes 

 

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