85306 - Proteomes, Interactomes and Biological Networks

Learning outcomes

By the end of the course, the student has the basic knowledge of the experimental techniques for analyzing at large proteins expressed in a biological systems and their interactions. The student is familiar with the publicly available databases collecting proteomics and interactomics data and knows how to represent and analyze the structural complexity of interactions with the Network theory.

Course contents

The course explores proteomes and protein interactions across progressively finer levels of spatial resolution. It begins with quantitative, mass-spectrometry-based profiling of the global proteome and then moves to the investigation of protein–protein interactions and molecular complexes in situ at single-cell resolution. It subsequently introduces spatial proteomics approaches for the region-specific characterization of tissue microenvironments, integrating spatially resolved data with mass spectrometry, and culminates in high-dimensional multiplex imaging for mapping tissue architecture and cellular interaction networks. The final part of the course focuses on the computational analysis, visualization, and integration of proteomic, interactomic, and spatial data using dedicated network biology tools. Hands-on sessions are embedded throughout the course, providing practical experience with the analysis of real-world datasets.

Readings/Bibliography

Lecture slides, selected research articles, and review papers will be made available online through the Virtuale platform.

Teaching methods

Teaching activities will combine lectures, hands-on laboratory sessions, and the implementation of data-analysis scripts. Lectures will be delivered by the instructor with the support of PowerPoint presentations, which will be made available to students.

Assessment methods

The final assessment consists of an oral examination covering the theoretical topics addressed during the course and the discussion of an individual practical project, based on one of the laboratory topics and submitted as a written report before the oral examination. The final evaluation will consider both the student’s theoretical knowledge and their ability to present and discuss the project.

Students are entitled to decline the official recording of a proposed passing grade once, in accordance with Article 16, paragraph 5, of the University Academic Regulations.

Students with learning disorders and\or temporary or permanent disabilities: please, contact the office responsible ( https://site.unibo.it/studenti-con-disabilita-e-dsa/en ) as soon as possible so that they can propose acceptable adjustments. The request for adaptation must be submitted in advance (15 days before the exam date) to the lecturer, who will assess the appropriateness of the adjustments, taking into account the teaching objectives.

Students recognized as “working students”: please consult the dedicated website ( https://www.unibo.it/en/study/guide-to-choosing-your-programme/balancing-study-and-work ) to apply for this status and to learn about the available measures.

The use of artificial intelligence (AI) tools is prohibited in all assessment activities. Any use of AI will be considered a breach of academic integrity.

Teaching tools

Teaching activities will be supported by online resources, scientific literature, public databases and datasets, and course materials available on Virtuale. During practical sessions, students are encouraged to use their personal laptops and freely available software for data analysis and visualization. Basic familiarity with scripting and programming is recommended.

Office hours

See the website of Pierluigi Di Chiaro