About this class
Cette UE permet d’acquérir des bases solides sur les modèles usuels en dynamique des populations cellulaires. Les formalismes de systèmes dynamiques les plus courants seront introduits : Processus stochastiques, équations différentielles ordinaires et stochastiques, systèmes discrets. L’accent sera mis sur l’étude qualitative des systèmes dynamiques et des méthodes de résolution et d’analyse numérique.
In this class you will acquire solid background on modelling cell population dynamics. The most widely used dynamical system formalisms will be intriduced: Stochastic processes, ordinary and stochastic differential equation, and discrete systems. Emphasis will be put on qualitative study of dynamical systems and on the numerical methods and analysis.
Synopsis (subject to modifications)
- Chapter 1: Stochastic Processes and the birth-death process
Équation maîtresse, Équation de Fokker-Planck, Algorithme de simulation stochastique. Lien avec les systèmes déterministes. Exemples de modèles de prolifération cellulaire - Chapter 2: Nonlinear Systems of ODEs
Existence/unicité des solutions, Théorème de Hartman-Grobman, Linéarisation et stabilité linéaire, Classification des points fixes, Bifurcations de co-dimension 1 et 2 pitchfork, col-nœud, transcritique, Hopf, systèmes bistables. Étude numérique avec logiciels d’analyse de stabilité et de continuation de bifurcation. Exemples de la dynamique des populations cellulaires (dynamique du HIV, croissance tumorale, cycle cellulaire). - Chapter 3: Discrete Systems
Existence/unicité des solutions, Linéarisation et stabilité linéaire, comparaison avec les EDO, Application de Poincaré, Bifurcations de doublement de périodes, Chaos. Applications : Équation logistique, Matrices de Leslie. - Chapter 4: Large Soupled Systems and Collective Dynamics
Oscillateurs (oscillateur de phase, modèle de Goodwin), Réseaux, Synchronisation d’oscillateurs. Étude du Modèle de Kuramoto, Entrainement de systèmes périodiques. Exemples et étude numérique de modèles pour la synchronisation d’oscillateurs circadiens, synchronisation du cycle cellulaire par l’horloge circadienne. - Chapter 5: Selected Topics
Evaluations
- 1 Project (~2/3)
- Reading assignements (~1/3)
Lecture notes
Lecture notes (in progress).Reference books
- L Perko, Differential Equations and Dynamical Systems, Texts in Applied Mathematics Volume 7, 1991, Springer
- S Strogatz, Nonlinear Dynamics And Chaos: With Applications To Physics, Biology, Chemistry, And Engineering, Studies in Nonlinearity, Westview Press
- J Guckenheimer, P Holmes Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields, Applied Mathematical Science, Springer
More references
- Practical introduction to stochastic differential equations with Matlab codes: An algorithmic introduction to numerical simulation of stochastic differential equations
- An exhaustive introduction to Kuramoto model: The Kuramoto model: A simple paradigm dor synchronization phenomena
- SSA: Stochastic simulation of chemical kinetics
Numerical softwares
Codes used in class on Github (github/samubernard/popdyn). Mostly Matlab, but also XPPAUT, C Python.
- Short Matlab tutorial
- Logiciel XPP (Windows, Mac, Linux, iPhone/iPad) on Bart Ermentrout's website at University of Pittsburgh.
- numpy (Python)
- Github repository
- C/C++
Template for reading assignments
For the reading assigments, you can use the template fiche de lecture (French), or fiche de lecture (English).
Contact/Questions
Samuel Bernard to bernard@math.univ-lyon1.fr
Project
The term project will deal with a recent modelling article. For the project, each student will hand in a report along with the codes used to generate the results presented in the report.
The project is in three parts: 1/ Understanding the model, 2/ its analysis (stability, bifurcation, ...), 3/ an extension of (stochastic or discrete version)
List of reading assigments
Write the report for yourself, not for your teacher.
If a question is not relevant for the article, do not answer it.
For instance, biological experiments do not apply for mathematics papers.
You can hand in your assignment as a .doc, pdf or scanned file, or in paper format in class.
Here are templates for the reading assignments in French or
in English.
1. Reading assignment on variation in cancer risk. Date due: Feb. 28
- C Tomasetti, B Vogelstein, Variation in cancer risk among tissues can be explained by the number of stem cell divisions, (2015) Science, 347:78-81.
Chapter 1 - Stochastic and Deterministic Processes
Solutions for the birth and death process: Kendall (1948) On the Generalized "Birth and Death" Process
Moment closure and the stochastic logistic equation model (Nåsell, 2003)
Stochastic equations in Matlab: An algorithmic introduction to numerical simulation of stochastic differential equations
Chapter 2 - Modelling with ODEs
Case study: Tumor-immune interaction (pdf, French)
Case study: Tumor-immune interation, numerical simulations with XPPAUT (pdf, French)
Numerical bifurcation continuation methods
- XPPAUT oldish but still widely used
- MATCONT Matlab package for ODEs
- DDE-BIFTOOL - Matlab package for delay differential equations
- PyDSTool - Dynamical systems in Python (ODEs and algebraic differential equations, hybrid systems). Based on numpy et scipy
Chapter 3 - Finite Difference Equations
Li and Yorke (1975) Period Three Implies ChaosChapter 4 - Delay differential equations
Lecture notes on delay differential equations
- Delay-induced oscillations D Bratsun, D Volfson, LS Tsimring, J Hasty (2005) Delay-induced stochastic oscillations in gene regulation. 102:14593-14598
- Chaos in delay equations MC Mackey, L Glass (1977) Oscillation and chaos in physiological control systems. Science 197:287-289
- Delayed logistic growth J Arino, L Wang, GS Wolkowicz (2006) An alternative formulation for a delayed logistic equation. Journal of theoretical biology 241:109-119
- Complex white blood cell regulation model with state-dependent delaysM Craig, AR Humphries, MC Mackey (2016) A mathematical model of granulopoiesis incorporating the negative feedback dynamics and kinetics of G-CSF/neutrophil binding and internalization. Bulletin of mathematical biology 78:2304-2357