Explain the course, introduce the teachers, projects, evaluation. Outline the course. Robustness and optimality. Quantitative reasoning, not just in biology but in everyday's life.
In this block we want to introduce a few different modelling approaches, and in particular emphasise the difference between deterministic and stochastic modelling. Those notions are relevant to the Synthetic Biology and Practical Modelling UEs.
Objectives:
- Understand, formulate and analyse mathematical models based on ODEs, with basic analytical tools and numerically.
- Grasp the difference between deterministic and stochastic processes and being able to describe the notion of noise.
- Simulate a simple stochastic process (Gillespie algorithm) and compare to its deterministic variant.
- Understand a model based on PDEs.
Objective: Sketch a model based on ODE, simple solving techniques. Solve your system of equations with Python.
Time: During the class, demonstration by the teacher.
How: Jupyter notebook, step-by-step.
Modelling toolbox: stochastic simulations
Objective: Introduction to Monte Carlo methods. Understanding the concept of noise, learn how to simulate a stochastic biochemical reaction. Distributions, coefficient of variation (and similar).
Time: During the class, demonstration by the teacher.
How: Jupyter notebook, step-by-step
Very brief interlude. Revise the concept of partial derivatives, in particular make the link to diffusion (macroscopic). Introduction to diffusion equation.
We will first give an overview of the theory and some biological examples, that will then be used in the introduction "Basics of microscopy" and "Genome Biophysics" parts.
Objective: Give the basics of Random Walks in biology and emphasise the relation with the diffusion equation.
Objective: Understand the derivation and properties of the Freely Jointed Chain (FJC) and worm-like (WL) models.
Jupyter notebook is used to visualise concepts from the lecture.
Objectives:
- Basic principles of image manipulation in python
- Methods for detecting objects using image processing and artificial intelligence
Time: Constructs are run by students beforehand, and discussed with teacher during class.
How: Jupyter notebook, step-by-step
Image processing-based segmentation
Outline:
- You will start by following the constructs to load images and learn the basics of image manipulation.
- You will learn then how to segment an image of nuclei within a Drosophila embryo using conventional image segmentation methods, such as thresholding and watershed.
- Finally, you will learn how to apply artificial intelligence based (deep learning) algorithms to segment an image of nuclei within a Drosophila embryo.
*Objectives: *
1. principles of fluorescence
2. labeling methods
3. widefield microscopy
Time: Constructs to be learnt beforehand, discuss construct content with teacher during class (1h).
How: Jupyter notebook, step-by-step
Evaluation: not evaluated
Outline:
- Learn the basics of luminescence/fluorescence (Jablosky diagram)
- Learn about the existing methods for labeling biological molecules, and the relative advantages of each of them
- Learn about filters and dichroics, sources and cameras.
- Learn how a widefield microscope is built.
- What is the intrinsic resolution of a widefield microscope.
Objectives:
- Discover more sophisticated imaging techniques that enable background removal.
- Understand when to apply each method.
- Understand the physical principles behind these methods
Time: Constructs to be learnt beforehand, discuss construct content with teacher during class (1h).
How: Jupyter notebook, step-by-step
Evaluation: not evaluated
Outline:
- Follow video lecture on total internal reflection microscopy underlying the basic principles of the method and when to apply it, and answer questions to make sure you understand the basic concepts.
- Calculate the depth of field in a widefield and a TIRF image. Compare and conclude.
- Measure the resolution of diffraction limited spots
- Follow tutorial on confocal microscopy: how are fluorophores excited? how are images build? what kind of detectors are used? How do you scan the sample? What is a pinhole for?
- Calculate the excitation profile of a confocal microscope and compare to widefield excitation
- Estimate the 3D resolution in confocal and compare to widefield microscopy.
Objectives:
- What is the resolution limit imposed by light diffraction?
- Understand why and when higher spatial resolutions are needed in microscopy
- Grasp the basic physical concepts behind three well-established super-resolution methods
- When should you use each?
Time: Constructs to be learnt beforehand, discuss construct content with teacher during class (1h).
How: Jupyter notebook, step-by-step 1. Single-molecule localization microscopy (SMLM) 2. Structured illumination microscopy (SIM) 3. Stimulated emission depletion microscopy (STED)
Outline:
- Follow tutorials on SMLM, SIM and STED microscopies and answer questionnaires.
- Calculate the resolution gained for each of these methods
- Localize single molecules from a single-source dataset
- Read additional resources with examples of how these methods can be used to gain biological insight.
Objectives:
- Introduction to the concept of FC(C)S : Fluorescence (Cross) Correlation Spectroscopy
- What can we measure : molecular diffusion, molecular interactions
- How do we measure : confocal and 2photon microscopes
- Data analysis : auto and cross correlation, photon counting histogram
Evaluation: not evaluated
Outline:
- Watch beforehand the lecture on FCS by Enrico Gratton (https://www.youtube.com/watch?v=xLNUFgYgU_w&list=PLrTP91eqdDiIzVtpn5gsUKqVciNUhm-CP&index=3)
- During the course, we will :
- Understand the autocorrelation curve and describe the type of information that can be extracted from it
- Review the possibilities and the limitaion of the methods
- Talk about advanced notions such as time resolved FCS, scanning FCS, Number and Brightness
- We will also make simulations and analysis of FCS experiments using the FluoSim software. Please dowload and install this software on you computer using the following link (https://filesender.renater.fr/?s=download&token=99d853e9-61ee-46f7-9309-b51f89a9e1dd). This software might help you as well to understand other types of experiments such as SPT, STORM or FRAP. Don't hesitate to explore it, and read the publication associated with it (https://www.nature.com/articles/s41598-020-75814-y) (Warning : do not use the link on the publication website, use my Filesender link)
Objectives:
- Get introduced to the main elements of an Atomic Force Microscope and understand how they work.
- Understand the main AFM imaging modes (static and dynamic).
- Understand the harmonic oscillator (physical concept) and why it is relevant in dynamic AFM.
- What we can image with an AFM, what is the spatial and temporal resolution we can achieve.
Time: Constructs to be learnt beforehand, discuss construct content with teacher during class and after class (1h).
How: Jupyter notebook, step-by-step 1. Introduction to AFM
Evaluation: not evaluated
Outline:
- Follow tutorials on AFM microscopies and answer questionnaires.
- Briefly describe the static and dynamic AFM operational schemes (drawings required).
- AFM operating in ambient (air) conditions and in physiological conditions (liquids) for biological purposes.
- You will get introduced to the most relevant calibrations necessary to run conventional AFM experiments.
- Read additional resources with examples of how AFM can be used to gain biological insight.
Two lectures, one for a general introduction to complex networks, the other for applications to gene networks and metabolic networks.
- Intro to network theory, with many applications (from traffic to epidemiology, relation with machine learning). Explain the concepts of directed vs indirected graph, different topologies, modularity, centrality index,...
- Small world effect, communities and robustness
- Models for generating networks: Erdos, Renjy; Preferential attachment
Objective: To acquire competences in analysing networks
Time: After class.
How: The students will be divided in groups and will be given a dataset of therotical model networks as well as a data driven one . Based on the lecture's material the students will provide a descriptive analysis of the networks and compared the characteristics of the 2. Jupyter notebook
This section has to be updated
Topics covered (mainly from the first chpaters of Uri Alon's book):
- Timescales of gene expression
- Gene regulation: Activator and Repressor, Hill functions, logic gates.
- Dynamics and response time of gene regulation
- Noise in gene expression
- Bulk and single cell measurements
For visualisation and support: Gene reguation
Partly based on parts of https://www.biorxiv.org/content/10.1101/2021.04.09.439163v1
Objective: Merging the concepts learned in networks (negative feedback), gene regulation (timescales), simulations (Gillespie algorithm), stochasticity (noise, CV).
Time: 2 weeks for this assignment
How:
Evaluation: evaluated
Outline:
Where do you find networks in Biology? Focus on metabolic and genetic networks. What are their properties? Network motifs.
Objective:
Time: During the class, demonstration by the teacher.
How: Jupyter notebook, step-by-step
Evaluation: not evaluated
Outline: To be done
At the end of this project, explain the principles of Flow Cytometry, emphasise the difference between bulk and population measurements.
Objective: import connectivity of a given GRN from E.coli. Plot it and compute relevant quantities (connectivity,...). Compare to a ER network.
Time: During the class? Demonstration by the teacher?
How: Jupyter notebook, step-by-step
Evaluation: not evaluated?
Outline: To be done
At the end of this project, explain the principles of Flow Cytometry, emphasise the difference between bulk and population measurements.
Objective:
Time: During the class, demonstration by the teacher.
How: Jupyter notebook, step-by-step
Explain the principles of Flow Cytometry, emphasise the difference between bulk and population measurements.
(30') What is DNA? DNA is the molecule that carries information. Double helix structure. Basepairing. Basics of multi-scale chromosome organization.
Jupyter lab: DNA basics tutorial
(30') DNA in biotechnology: DNA origami. Talk by Gaetan Bellot.
Why is DNA supercoiled? What is the supercoiling state of DNA in a cell? Proteins acting on DNA management: replichore, topoisomerases, RNA polymerase, translocases and helicases. Example for bacteria. How can DNA supercoiling be manipulated in vitro? Principles and applications of magnetic tweezers.
Jupyter lab: DNA supercoiling
(30') Use of optical tweezers to understand the mechanisms of molecular motors. OT_construct. Talk by F. Pedaci.
(30') First level of higher order chromatin structure in eukaryotes: the nucleosome. Structure of the nucleosome. Histones, histone octamers, and how DNA is wrapped around. Jupyter lab: nucleosomes
(30') What is epigenetics? Histone modifications can alter the structure of chromatin and the function of the nucleus. Review of the most relevant histone modifications and their consequences. [Histone modifications] Jupyter lab: (constructs/UE_Introduction/histoneModifications.ipynb)
Mechanism of transcription by single-molecule manipulation methods. How do motor proteins read and write histone modification information. How do they work? Examples from structural and single molecule manipulation studies.
Jupyter lab: DNA machines
What is a transcription factor? what are general and developmental TFs? Why so many? What are cis-regulatory elements? What are enhancers? Why are enhancers important? What should I look at to identify an enhancer? Study of a genomic region using chip-seq profiles. transcription_factors
Use of structural biology methods to dissect the mechanisms of transcription factors in transcriptional regulation. External Talk.
How can we study the dynamics of DNA binding proteins? Are they always bound to their targets?
Jupyter lab: Study the diffusion of DNA binding proteins using sptPALM
From nucleosomes to TADs and chromosome territories. High-throughput sequencing-based chromosome architecture (Hi-C like techniques). Jupyter lab: Higher-order chromosome structure
Factors that modulate the organization of chromatin into domains and compartments. Cohesin/condensin and loop extrusion. Monitoring loop extrusion with single-molecule TIRF microscopy. How can SMCs modulate the higher order organization of bacterial chromosomes? Jupyter lab: The mechanism of loop extrusion
Heterogeneity in chromosome organization. Why it is important to detect chromatin organization in single cells? Imaging based methods to trace chromatin: Hi-M, chromatin tracing, ORCA, etc. Jupyter lab: Multiplexing imaging methods
Jupyter lab: Higher-order chromosome structure
Introduction to the structural biology of biological membranes (M1 HMBS103 - Biochimie Structurale) - C Doucet & PE Milhiet (pdf available soon)
-
Membranes and evolution
-
Composition of membranes in archeae and bacteria
-
Composition and compartments in eukaryotes
- Membrane components (lipid, protein, sugar)
- Membrane composition (organelles, organism including virus)
- Plasma Membrane
- Membrane organelles
- Links to the cytoskeleton
-
How to manipulate membrane components (detergents, SMALP, nanodiscs...)
-
Membrane structure and organization
- Lipid phase separation
- Membrane diffusion / lateral segregation
- Concept of domains
-
Membrane deformations
-
Bending energy
-
Equilibrium shapes
-
Membrane fluctuation
-
Model membranes versus native membranes
-
Mimicking biological membranes
-
Imaging and spectroscopy
- Spectroscopy and Microscopy (SMLM, FFS, NSNOM, EPR, SAXS/SANS, Raman, FTIR, Cryo-EM, NMR, X-ray crystallography)
- Computational characterization
- Construct FLUOSIM
-
Methodologies
-
Cell mechanics probed by Atomic Force Microscopy (AFM)