Mathematical Biology - Math 4780/6780
Instructor: Caner KazanciOffice: 444 Boyd Graduate Studies
Course Information: You can download the syllabus here.
About this website: Please visit this website frequently to read important announcements (updates on test times, class schedule, syllabus etc.) and to get the homework assignments and related course material. Please notify me if you notice any inaccurate information.
Announcements
- Here is the updated information about the projects: projects_Coronavirus.pdf.
- You can install XXP here: http://www.math.pitt.edu/~bard/xpp/xpp.html.
- Check these videos out for help on installing XPP: Video1, Video2.
- Here are some project presentations from previous years: SEIR.pdf, Tipping_Points.pdf, Zombie_Apocalypse.pdf, Peer_Influences.pdf.
- Solutions of the first test.
- Solution of the equations for the bifurcation point r at which the discrete logistic equation transitions from a stable 2-cycle to a stable 4-cycle.
- I suggest that you go over this Matlab tutorial. You may wish to take a look at these video lectures on Matlab as well.
Codes, scripts, handouts & software links
- Matlab code: Goal function for parameter optimization of the Coronavirus pandemic (Italy).
- XPP ODE file: competition.ode.
- Matlab code: Time series and state space of Romeo-Juliet model.
- Matlab diary file: Text file containing the commands we ran in class.
- Lecture notes: Discrete systems.
- Matlab code: Period doubling bifurcations and Liapunov exponent.
- Desmos link: Time series, Discrete logistic equation.
- Desmos link: Cobweb, Discrete logistic equation.
Homework Assignments
Online Assignments
- Online Assignment 1 - Due Friday 4/3 by 9pm.
- Online Assignment 2 - Due Tuesday 4/7 by 9pm.
- Online Assignment 3 - Due Wednesday 4/8 by 9pm.
- Online Assignment 4 - Due Tuesday 4/14 by 9pm.
- Online Assignment 5 - Due Thursday 4/16 by 9pm.
Lectures
- Modeling chemical reactions
- X Phase Plane (XPP)
- Enzymatic reactions
- Modeling of the Covid-19 pandemic I: Infectious disease models
- Modeling of the Covid-19 pandemic II: Analysis
- Modeling of the Covid-19 pandemic III: Parameter tuning
- Modeling of the Covid-19 pandemic IV: Optimized data fitting
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