Quantitative and Computational Biology

Overview

Introduction

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 by means of 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’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 or computational, or physical topics. The fourth semester is entirely dedicated to the preparation of the thesis, with a rotation system among research groups.

Admissions

Application deadlines

• 10th of March 2023 for non-EU citizens living outside Italy

• From 12th of April 2023 to 30th June 2023 for EU citizens and non-EU citizens regularly living in Italy

How to apply

• Access the online application form

• Upload the required documents

• Submit your online application by the deadline

Requirements

• Bachelor’s degree (or equivalent) in Mathematics, Physics, Biology, Biotechnologies, Information Engineering, Pharmacy, Chemistry, Computer Science or in any related field

• English at the B2 level of the Common European Framework of Reference for Languages

Selection criteria

• Assessment of previous studies and their coherence with the MSc

• Academic curriculum

• Statement of purpose

• Video presentation

The course will consist of four tracks as follows: 

Biotechnology track

• Biostatistics 

• Scientific Programming

• Computational Genomics

• Biotechnology Engineering

Biocomputational Track

• Quantum mechanics

• Data Mining

• Computational Genomics

• Experimental and Computational Biochemistry

• Biotechnology Engineering 

Computational track

• Molecular Biology of the Cell

• Chemistry and Biochemistry

• Biological Networks and Data Analysis

• Mathematical Modeling

Physical track

• Molecular Biology of the Cell

• Organic and Biological Chemistry

• Algorithms and Data Structures

• Genetic and Metabolic Engineering 

• Machine Learning

• Computational Biophysics

Electives

• Programming

• Bioinformatics

• Biological Networks and Data Analysis

• Theoretical Methods for Soft Matter

• Laboratory of Biological Data Mining

• Mathematical Modeling

• Biotechnology Management and Regulations

• Quantum Chemistry

For all tracks

• Internship 

• Research Seminars/Journal Club

• English 

• Final thesis

Complete Program: 120 CFU or ECTS.

For a.y. 2023/24 there may be some changes within the tracks.

Program Outcome

The course aims at building 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 for 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.

Career Opportunities

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

• Biotechnologist

• Computational Biologist

• Bioinformatics

• Biologists data and systems biology analyst

The profiles are characterized by a set of shared competencies, to which are added specific expertise in the field 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, epidemiologists in experimental research and pre-clinical context, 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.