Mathematics for Biologists

Academic year 2026/2027
Lecturer Franco Flandoli

Examination procedure

Projects with R and oral exam


Examination procedure notes

The projects with R will be designed and developed with the help of a tutor


Prerequisites


Second year. Prerequisite: the first year course, in particular calculus in one and more variables and ordinary differential equations.

Syllabus

Ordinary differential equations




  • reminders of the main results seen in the first year (Cauchy problem, Cauchy-Lipschitz theorem, maximal solutions, Gronwall's Lemma, global existence criteria)
  • explicit solution methods (equations with separate variables, some linear equations) and qualitative analysis
  • asymptotic behavior, stability and instability, oscillations
  • detailed study of some examples, from population dynamics (e.g. Lotka-Volterra), virus diffusion models (e.g. SIR), neuronal spike models, tumor growth models.
  • projects, also with the help of R software, for particular problems and examples such as differentiation and cell cycle, feedback mechanisms and enzyme-substrate dynamics.

Calculus of Probability and Statistics



  • basic elements (events, their probabilities, first rules, conditional probability and independence, Bayes formula and total probabilities)
  • examples of discrete and continuous random variables, mean values ​​and their properties
  • linear models in statistics (multiple linear regression, principal components method, implementation on data using R software)
  • Markov chains (graphs, transition probabilities, state classification, invariant measures) and applications, also to models similar to those of ordinary differential equations.

Fourier series


  • Fourier series expansion of regular periodic functions in one variable
  • generalizations to less regular functions
  • some elements on Fourier series in two variables
  • time series analysis with R

Possible advanced topics

  • stochastic processes, Brownian motion
  • stochastic differential equations (Langevin), Fokker-Planck equations
  • graphs and networks
  • Bayesian statistics.


Bibliographical references

Teacher's notes.