By the end of the course students will have gained (i) basic knowledge of different biological and biomedical phenomena; (ii) the knowledge of several types of mathematical and statistical models used to simulate phenomena in the LIfe Sciences; (iii) the ability to construct and evaluate the proposed mathematical and statistical models; (iv) the know-how to adapt and fit theoretical models to experimental data and to make predictions; (v) the ability to compare and evaluate models with analytical, statistical and numerical techniques.

A good knowledge of calculus and basic statistical concepts, tools, and methods. Particular attention has to be paid to: - differential and integral calculus; - linear algebra; - elementary probability theory, with particular emphasis on multivariate distributions; - methods of inference, specifically frequentist and Bayesian statistics: point and interval estimation, hypothesis testing, linear and generalized linear models, prior and posterior distributions. - basic notions of discrete-space continuous-time Markov Chains

The course will tackle the following topics: 1) general concepts of deterministic (bio)-chemical pathways: positive and negative feedback loops. Mutually inhibitory feedback loops; 2) population dynamics. SIR (and SIR-based) models. Models for growth of cell aggregates in physio-pathological situations. Analysis of exponential, Gompertz, and logistic growth laws; 3) bi- and three-compartmental models for pharmacokinetics and pharmacodynamics; 4) deterministic and stochastic models of chemical reactions. Bayesian approaches for the estimate of the parameters of the models introduced in 2) and 3); 5) clinical trials, randomization, sequential clinical trials, evaluation of the size of the sample; 6) analysis and modeling based on genomic data obtained by DNA and RNA sequencing. Next Generation Sequencing (NGS) and Third Generation Sequencing (TGS); 7) general concepts of high-throughput data analysis; 8) phylogenetic reconstruction.

The course will be organized in theoretical lectures and practice classes. The practice classes will require the active participation of students and will be devoted to train them to solve problems and exercises and to apply to real data the methods introduced in the theoretical lectures (with the use of software such as Matlab, Python, or R).

Ad hoc slides will be used during the lessons and will be made available through the Portale della Didattica. Other material will be suggested in class and, if possible, made available through the Portale della Didattica. 


Lecture slides; Lecture notes;

Exam: Written test; Optional oral exam;

The final exam will consists in a written test which will be divided in two parts: a multiple choice questionnaire and some open-ended questions. The final grade proposed will be given by the sum of the scores obtained in the two parts of the written test. If the sum of the two scores is higher than 30, the final grade will be 30 cum laude. The bioinformatic portion of the written exam may be waived by completing a small individual project during the course. An oral exam is optional, i.e., it may be only required by students with a grade higher than 18: the oral exam may however result in a variation of the grade that ranges from -3 to +3. The teachers of the course may also ask for an oral exam to further assess students knowledge.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.