Learning outcomes

The course exhibits several methodologies to model phenomena occurring in nature by means of discrete mathematical tools and computational systems. The goal is to develop the ability of the student to master different approaches of discrete biological modeling, along the presentation of the state of the art and of the most recent problematics. Basic concepts (of mathematics, computer science, biology) are recalled, to better understand some traditional mathematical models, algorithms of natural computing, and biological networks, proposed along with a few case studies.

Program

Introduction to different classes of models, and to discrete models
Fibonacci numbers and golden section in nature
Differential biological models, and recurrence equations
Malthusian biological population growth models
Dynamical systems, and logistic map
Discrete SIR model of epidemics
Formal languages and biological grammars
Analysis of DNA bio-molecular processes related to PCR
String operations and molecular biology experimental techniques
XPCR for string recombination and concatenation
DNA computing
Trees and membrane models
Discrete models of metabolism
Graphs and biological networks
Models of population evolution
Operations on micro-organisms and experimental techniques

Examination Methods

Oral exam