B5804 - Bioinformatics for System and Synthetic Biology

Learning outcomes

At the end of the course, the student has a description of new approaches and algorithms to cope with the future of Biological Science. The student acquires the know-how of: -The frontiers of Structural and Functional Genomics; - Data integration, knowledge mining and visualization of Systems Biology data; - Synthetic Biology: putting engineering into biology; - Modelling the data: towards a reconstruction of biological systems; - Synthetic Biology: how to mimic biological functions.

Course contents

Prerequisites

Basic knowledge of molecular biology, genetics, bioinformatics, and systems biology is recommended. Familiarity with fundamental algorithms, data analysis principles, and the reading of scientific literature is also advisable.

Course topics

The course is devoted to the critical analysis of bioinformatics methods used in systems biology and synthetic biology, with particular emphasis on the algorithmic principles underlying their development, their application contexts, and the approaches used to evaluate their performance.

The topics covered include:

• formulation of computational problems derived from biological questions and their translation into algorithmic problems;
• principles of the main computational approaches used in bioinformatics, including exact methods, heuristic approaches, and machine learning-based methods;
• criteria for selecting the most appropriate method according to the biological problem and the characteristics of the available data;
• analysis of datasets used for benchmarking bioinformatics methods, with particular attention to quality, representativeness, and potential biases;
• validation strategies and metrics used to assess the performance of bioinformatics algorithms;
• critical comparison of alternative methods and interpretation of results reported in the scientific literature;
• evaluation of the robustness, reproducibility, and limitations of benchmarking studies;
• discussion of case studies and recent scientific articles in the fields of bioinformatics, systems biology, and synthetic biology.

Learning activities

Students will analyse and discuss research articles and review papers related to bioinformatics methods for systems biology and synthetic biology. Particular attention will be devoted to understanding the computational strategies adopted, critically evaluating benchmarking protocols, and interpreting experimental results.

Activities may include guided discussions, individual presentations, and collaborative discussion of case studies from the scientific literature.

Readings/Bibliography

Study materials

The study material mainly consists of research articles and review papers selected by the instructor, covering methodologies in bioinformatics, computational biology, systems biology, and synthetic biology.

Additional in-depth readings may be suggested during the course in relation to the topics addressed by invited speakers.

Teaching materials

Lecture slides, scientific articles, and any supplementary materials will be made available through the Virtuale platform.

Teaching methods

The course combines lectures, specialised seminars, and discussion of scientific literature.

Teaching activities include:

• lectures by the instructor on methodological principles of bioinformatics applied to systems biology and synthetic biology;
• seminars held by national and international experts in the fields of bioinformatics and computational biology;
• presentation and critical discussion of recent scientific articles, with particular attention to methodological aspects, data quality, validation procedures, and interpretation of results.

The teaching approach aims to develop students’ ability to critically read scientific literature and evaluate the strengths, limitations, and application domains of different bioinformatics methods.

Assessment methods

Assessment is based on the development of a project and an oral examination.

The project aims to assess the student’s ability to retrieve, analyse, and interpret structural and functional data using databases and bioinformatics tools.

The oral examination assesses:

• knowledge of the course topics;
• ability to establish relationships between sequence, structure, and function;
• ability to interpret experimental and structural data;
• appropriate use of scientific terminology.

The final grade takes into account both the quality of the project and the oral examination.

Assessment criteria

• 18–21: basic knowledge of the topics and understanding of fundamental concepts.
• 22–25: good knowledge of the contents and ability to apply concepts.
• 26–29: in-depth knowledge, autonomy in analysis, and correct use of terminology.
• 30–30 cum laude: complete mastery of the topics, critical thinking skills, interdisciplinary connections, and excellent command of scientific language.

Use of Generative Artificial Intelligence

The project may include a declared use of AI, accompanied by a mandatory critical analysis of the results obtained.

Teaching tools

• Virtuale platform for the distribution of teaching materials;
• lecture slides;
• scientific articles, online seminars, and review papers selected by the instructor;
• links to biological databases, software repositories, and other bioinformatics resources used during the course;
• any datasets and additional in-depth materials.

The use of generative Artificial Intelligence tools is allowed as support for individual study (e.g., summarization, explanations, self-assessment, or further exploration), in accordance with the guidelines established for course assessment.

Students with Specific Learning Disorders (SLD) or temporary or permanent disabilities are encouraged to contact the University's dedicated office (https://site.unibo.it/studenti-con-disabilita-e-dsa/en ). The office will propose any necessary accommodations, which must be submitted to the instructor for approval at least 15 days before the examination. The instructor will evaluate the appropriateness of such accommodations in relation to the intended learning outcomes.

Students who have been granted working student status should consult the dedicated University web page (https://www.unibo.it/it/studiare/guida-alla-scelta-del-corso/conciliare-studio-e-lavoro ) to apply for this status and learn about the available support measures.

Office hours

See the website of Emidio Capriotti