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 and iterative biological models
Criteria to solve polynomials and linear recurrence equations
Malthusian biological population growth models
Dynamical systems, and logistic map
Discrete SIR model of epidemics
Formal languages and biological grammars
Algorithmic models of bio-molecular processes
DNA computing
XPCR for string recombination and concatenation
Membrane models and minimal cell
Discrete models of metabolism
Models of population evolution
Operations on micro-organisms and experimental techniques

Examination Methods

Oral exam