Learning outcomes

The course is designed to introduce 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, by means of 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 both traditional mathematical models and computational models of cellular and molecular processes, proposed along with a few case studies.

Program

Part I (traditional mathematical models)
Introduction to different classes of models, and to discrete models
Discrete mathematics fundamentals - induction and recurrence
Fibonacci numbers and golden section in nature
Iterative biological models
Malthusian biological population growth models
Dynamical systems, and logistic map
Lotka-Volterra model and cobweb model of supply/demand interaction
Discrete SIR model of epidemics

Part II (non-conventional bioinformatics models)
Formal languages and biological grammars
Computational models of bio-molecular processes, NP-completeness
Informational structure of DNA molecule, operations, experimental techniques, and bio-algorithms
XPCR for string recombination and concatenation
DNA algorithms solving SAT
Membrane models, and minimal cell
Discrete models of metabolism
Procedures based on bacterial growth, and related experimental techniques

Examination Methods

Oral exam, or a couple of written midterm exams

Teaching materials e documents

•   CorrezioneCompitoFinale   (pdf, it, 97 KB, 06/02/12)
•   RisultatiPrimaProvaParziale   (pdf, it, 20 KB, 02/02/12)
•   RisultatiSecondaProvaParziale   (pdf, it, 26 KB, 31/01/12)
•   VotiCompitoFinale   (pdf, it, 26 KB, 06/02/12)
•   VotiFinaliEntrambeProveParziali   (pdf, it, 27 KB, 02/02/12)