Teaching code

4S009832

Teacher

Rosalba Giugno

Coordinator

Rosalba Giugno

Credits

6

Language

English

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Period

I semestre dal Oct 1, 2026 al Jan 29, 2027.

Courses Single

Authorized

Lessons timetable Moodle Seminars 0

Learning objectives

Knowledge and understanding The course aims to provide students with the knowledge and understanding of the algorithms and advanced programming tools for the management of single cell and spatial transcriptomic data. Applying knowledge and understanding The student will therefore be able to a) apply the algorithms and advanced programming tools for the analysis of single cell and spatial transcriptomic data; b) apply the code performance analysis and identify critical issues and their optimization. Making judgements Ability to independently propose effective and efficient solutions for the biomedical and bioinformatics application domain; ability to identify critical issues for the treatment of complex bioinformatics problems related to the analysis of single cell and spatial transcriptomic data. Communication The student will also be able to interact with various interlocutors in a multidisciplinary biomedical and bioinformatics context, to interact with colleagues in the performance of group work, and to interact with the interlocutors in the working or research environment. Lifelong learning skills Ability to understand scientific literature in the process of interpreting the results or proposed solution, and to carry out individual and group in-depth studies aimed at tackling problems from the research and business world.

Prerequisites and basic notions

Basic concepts and notions of Programming for Bioinformatics

Program

Advances in sequencing technologies, artificial intelligence, and genome engineering are reshaping the paradigm of precision medicine. Regulatory agencies, including the U.S. Food and Drug Administration (FDA), are progressively introducing new frameworks to support the development of individualized therapies, recognizing that future diagnostic and therapeutic strategies will increasingly rely on the molecular characteristics of individual patients rather than on population-based approaches. This paradigm shift requires computational models capable of integrating heterogeneous biological information and representing the complexity of human biology across multiple interconnected scales. Biological networks provide one of the fundamental computational frameworks for modeling complex biological systems. However, the increasing availability of personalized multimodal data has highlighted the limitations of traditional graph representations, motivating the development of more expressive models, including multilayer and higher-order networks, capable of capturing the complexity of biological interactions. Building upon these computational foundations, the course introduces the biological data driving this transformation. Students will explore personalized genomes and the Human Pangenome as emerging paradigms for representing genomic diversity beyond the traditional reference genome, providing the basis for precision medicine and personalized genome-editing strategies. Particular emphasis will be placed on the computational design of CRISPR-based therapeutic approaches, including the use of individual genomic information to improve the specificity and safety of genome-editing interventions. The course will also introduce single-cell and spatial omics technologies to illustrate how cellular heterogeneity, tissue organization, and cell-cell interactions contribute to biological complexity. Through selected case studies, students will learn how heterogeneous biological data can be integrated to address biomedical challenges ranging from biomarker discovery and patient stratification to the development of personalized therapeutic strategies. The course adopts a case study-driven approach, allowing students to explore representative applications of computational bioinformatics through advanced biological data. The topics introduced in this course can be explored further through elective activities and research collaborations with medical and biological research institutes. By taking advantage of the extra-credit opportunities available within the study programme, students will have the opportunity to gain hands-on experience with cutting-edge methodologies in computational biology and precision medicine.

Bibliography

Didactic methods

Students will attend theoretical lectures and, for selected topics, practical sessions. Course materials, including lecture slides and notebooks, will be provided to support both the theoretical and practical components of the course.

Learning assessment procedures

The assessment consists of solving written exercises and answering theoretical questions on paper.

Evaluation criteria

Assessment will be based on the correctness of the solutions, methodological rigor, critical thinking, and the level of autonomy demonstrated in solving the exercises and addressing the theoretical questions.

Criteria for the composition of the final grade

The final grade is expressed on a 30-point scale and is calculated as the sum of the scores obtained in the individual sections of the written examination.

Exam language

Inglese