1st Course Year

1st Semester

• Bioinformatics and Computational Biology
• Organic Chemistry

2nd Semester

• Biostatistics
• Genomics and Transcriptomics
• Machine Learning
• Scientific Programming

Elective Courses

Students with a Degree/Background in Computer Science, Engineering, Mathematics, or Physics (Knowledge Alignment Plan 1)

• Biochemistry
• Genetics, Cellular and Molecular Biology

Students with a Degree/Background in Life Sciences (Biology or Biotechnology or Equivalent) (Knowledge Alignment Plan 2)

• Programming and Databases
• Statistics

2nd Course Year

1st Semester

• Advanced Genomics and Epigenomics
• Structural Chemistry

2nd Semester

• Systems Biology and Network Analysis

Further Elective Courses

• Open Choice Courses: 12 CFU
• Genomic Big Data Management and Computing
• Interdisciplinary Project
• Neurogenomics and Brain Disease Modelling

Knowledge and Understanding

Graduates in BCG will acquire an in-depth knowledge allowing them to apply a multidisciplinary and computational approach to solving complex problems in the fields of biology and biotechnology. The degree aims at providing integrated knowledge of different fields of chemistry and biology, which represent the foundations for a Master's degree in the class of Biotechnology. The BCG Master's degree aims in particular to provide solid interdisciplinary knowledge for the development and application of computational tools for bioinformatic and genomic analysis.

Applying Knowledge and Understanding

A fundamental objective of the Master's degree in BCG is to provide students with the ability to apply the knowledge acquired. This aim will be achieved both with class contents and with the final internship in a research laboratory. Suitable teaching time will thus be devoted to problem-solving and activities that will augment the analytical and methodological skills of the students. The skills acquired in classes on fundamental topics will be applied to the design and development of bioinformatic tools for genomic, transcriptomic, epigenomic, and systems biology studies.

Autonomy / Judgment (Making Judgments)

The acquisition of autonomous skills for making judgments will be made possible by the teaching methods employed in classes and the respective topics. The latter ones will often refer to relevant problems and research lines in different areas and will include problem-solving activities that will take place during classes or the development of projects to be discussed at exams. The usage of teaching material alternative to textbooks (research articles, online tutorials, and videos) will be encouraged, in order to make students acquire notions with a proactive and autonomous approach.

Communication Skills

Communication skills can be defined as the acquisition of tools and knowledge for scientific communication using the English language; skills in computing for the elaboration, presentation, and discussion of experimental data; being able to work autonomously, and being able to communicate the results of one's activities to others in seminars, journal clubs, etc. The acquisition of these skills will be included in classes and in the experimental laboratory activities leading to the final dissertation (reading and discussion of scientific literature; reading and discussion of analysis protocols; elaboration and discussion of experimental data).

Learning Skills

Learning skills can be defined as the development of suitable skills for the acquisition of novel knowledge, also through retrieval and study of scientific articles in English or access to databases and retrieval of information. These skills will be acquired during the experimental laboratory activities leading to the final dissertation, or the reading and discussion of scientific literature during classes or exams. These activities will allow students to learn through a 'hands-on' approach and through constant interaction both with their peers and the instructors.

The BCG Master's degree aims to train highly skilled professionals able to merge in-depth knowledge of the molecular foundations of life sciences with up-to-date knowledge of the current techniques and technologies for bioinformatic and genomic analysis. Particular emphasis will be put on the quantitative and computational aspects of the latter, which will be focused on the analysis, modeling, and comprehension of biological systems. The ultimate goal is to train in a multidisciplinary context professionals ready to cope with the challenges deriving from modern biomolecular sciences in the postgenomic era, and able to conjugate and integrate knowledge on biology, genetics, computer science, information engineering, and statistics in different fields of basic or applied research. The BCG program aims to train the following professional figures: biologist and related figures, biotechnologies.

Graduates in BCG Will Be Able to:

1. Take part in the design and execution of large-scale genomic analyses
2. Identify and extract the biological meaning from the results obtained
3. Design autonomous tools and protocols for the bioinformatic analysis of different types of experimental data
4. Play a pivotal role in research groups focused on basic or applied genomics research
5. Coordinate and supervise research projects and groups focused on bioinformatics and genomics