Lecture Notes and exercises
Prolog
A lecture by Joshua M. Epstein which he treats some enduring misconceptions about modeling. One of these is that the goal is always prediction. The lecture distinguishes between explanation and prediction as modeling goals, and offers sixteen reasons other than prediction to build a model. It also challenges the common assumption that scientific theories arise from and ‘summarize’ data, when often, theories precede and guide data collection; without theory, in other words, it is not clear what data to collect. Among other things, it also argues that the modeling enterprise enforces habits of mind essential to freedom.
How to solve ordinary differential equations? (tutorials in various common programing enviourments)
Lecture 1 – Epidemiology of COVID-19
- Lecture Notes
- Recording of the lecture
- Basic simulation of the SIR model (Python, Mathematica)
Lecture 2 – The insulin-glucose circuit
Lecture 3 – Beta-cell tissue size control has fragilities that lead to type-2 diabetes: Dynamical compensation and mutant resistance in tissues
Exercise 2 – Solution (courtesy of Alon Bar)
Lecture 4 – Two-gland feedback in the stress-hormone axis generates seasonal clocks and explains clinical phenomena with a timescale of months
Lecture 5 – Addiction
Exercise 3 – Solution (by Alon Bar)
Lecture 6 – The immune system detects exponential threats
Lecture 7 – Autoimmune disease as a fragility of surveillance against hyper-secreting mutants
Lecture 8 – Inflammation and fibrosis as a bistable system
Lecture 9 – Basic facts of aging
Lecture 10 – Aging and the saturation of damage removal
Lecture 11 – Aging-related diseases and their exponentially rising incidence with age.
Lecture 12 – Periodic table of diseases.
