MSc in Quantitative and Computational Biology

The Master of Science (Laurea Magistrale) in Quantitative and Computational Biology is a multidisciplinary degree that formally integrates quantitative sciences and applied biology, thanks to the involvement of the following organizations at the University of Trento:

• CIBIO, Centre for Integrative Biology
• Department of Physics
• Department of Mathematics
• Department of Information Engineering and Computer Science

The course focuses on a strategic area where technology and methodology enable students to face essential questions at the interface between fundamental research and clinical and pre- clinical areas, through analytical and quantitative approaches.

The course - entirely taught in English - is designed to capture the increasing need for researchers and experts able to transform the enormous amount of biological information ("big data") into knowledge and to gain quantitative insight into the behavior of biological systems using bio-mathematical and biophysical models.

Key target areas include pharmacogenomics, biotechnology, food science, and precision medicine, which represent applied research fields where the growing availability of multidimensional data demands high interdisciplinarity.

Program Overview

The course is designed to train experts in biotechnology, computational biology, bioinformatics, and biological data and systems biology analysis, who will have the opportunity to learn in a multidisciplinary context, interacting with students with different experiences. Strong emphasis will be given to quantitative and computational aspects, with a focus on tools to analyze, model, and understand biological systems and phenomena.

Teaching activities include lectures, laboratory courses, and seminars. The master of science thesis will be carried out during the second semester of the last year. Students will have the chance to carry out research projects within the University of Trento organizations involved in the Master&rsquo's Degree, at other Italian or European Universities, or in industries operating in the biotechnology, bioinformatics, and computational areas.

Admitted students will follow one of the four tracks based on their educational background acquired in previous studies. The four different tracks offer the opportunity for students to integrate their background based on their first-level degree and individual preparation. In the first, second, and third semesters, students will follow different courses with a focus on biotechnological computational, or physical topics. The fourth semester is entirely dedicated to the preparation of the thesis, with a rotation system among research groups.

Complete programme 120 CFU or ECTS. The course consists of two tracks:

Biotechnological Track

• Biostatistics
• Scientific Programming OR Data Mining*
• Genomics
• Chemistry and Biochemistry OR Biotechnology Engineering*

Quantitative Track

• Molecular Biology of the Cell
• Chemistry and Biochemistry
• Advanced-Data Analysis
• Mathematical Modeling and Simulation OR Molecular Physics**

Electives

• Scientific Programming
• Bioinformatics
• Advanced Data Analysis Genomics
• Biotechnology Engineering
• Data Mining
• Molecular Physics
• Mathematical Modeling and Simulation
• Computational Biophysics
• Foundations of Entrepreneurship in Biotech and Pharma
• Digital Signal Processing Knowledge Graph Engineering

Internship
English C1
Final thesis

*selected by the commission during the evaluation of the applications for admission.
**selected by the student.

Efrem Bertini

The course aims to build a scientific and professional profile that enables the understanding of complex biological phenomena, mathematical and computer models, as well as analytical approaches to physical phenomena.

Graduates will be trained as experts in the field of Quantitative and Computational Biology with particular relevance to the biomedical field.

Skills will be acquired in the following areas

Biotechnology area, with the objective of:

• Providing advanced skills in cellular metabolism and how it can be manipulated through genetics and metabolic engineering approaches applicable both to microbial systems and to mammalian cells;
• Providing expertise in the generation, analysis, representation, and interpretation of data omics (essentially genomics and transcriptomics);
• Providing skills for the use of complex experimental equipment and related software.

Information science area, with the objective of:

• Introducing students to computational problem solving;
• Providing the practical basis of scientific programming;
• Providing the basic knowledge of biological networks and the skills to manipulate them with data integration;
• Providing expertise and knowledge to model and simulate the dynamics of biological systems;
• Providing knowledge of specific algorithms for bioinformatics applications and knowledge for the development and implementation of bioinformatics resources;
• Providing knowledge and skills in machine learning and practical and theoretical knowledge of specific data mining applications for biological systems.

Mathematical-physical area, with the objective of:

• Introducing fundamental notions on statistical inference based on likelihood;
• Providing knowledge for the construction and the use of statistical models for the analysis of univariate and multivariate data, with particular reference to biomolecular data, and of dynamic deterministic or stochastic models for the description of biological and biochemical phenomena;
• Providing skills to build numerical computer simulations and to recognize the potential and the limitations in the use of models;
• Providing expertise in quantum mechanics fundamental equations and in the main elements of atomic and molecular physics phenomenology,
• Introducing students to the approximation methods for the theoretical description of many-body systems physics, providing skills to critically evaluate molecular models limits;
• Providing concepts in statistical mechanics fundamentals and applications of biomolecular systems, as well as numerical techniques for the study of their thermodynamic and kinetic properties.

Roberto Bernardinatti

Graduates in Quantitative and Computational Biology will be trained to build the following professional profiles:

• Biotechnologist
• Computational Biologist
• Bioinformatics
• Biologist data and systems biology analyst

The profiles are characterized by a set of shared competencies, to which is added specific expertise in the fields of biotechnology, information technology, and/or mathematics and physics.

Graduates, trained for the above-mentioned professions, will be able to use biological data publicly available and to work closely with biologists, clinicians, pharmacologists, engineers, and epidemiologists, as well as in experimental research and a a pre-clinical context and in analysis/hospital laboratories by using a common language.

The professional perspective of the graduates is in the context of public and private research centers, pharmaceutical, food, and biotechnology companies, as well as in advanced medical investigation laboratories.

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