Cancer genetics and biology for mathematical modelling and analysis
- Unit Coordinator: Alessandra Tessitore, Daria Capece
- ECTS Credits: 6
- Semester: 1
- Year: 2
- Campus: University of L'Aquila
- Language: English
- Aims:
Tumor initiation, progression and invasion are complex processes involving multiple and different phenomena. Mathematical models and computer simulations can help to describe, schematize and comprehend them, to provide data which could be putatively used in clinical practice to prevent and/or more specifically treat cancer. In this context, a multidisciplinary approach is fundamental to reach this goal. The main objective of this course is to approach and understand the biological processes at the base of cancer, focusing on the most significant features of oncogenesis, with the aim to provide basic information which can be applied to mathematical modelling. On completion, the student should:
- know fundamentals about structure and functions of nucleic acids and proteins in eukaryotic cell;
- understand the significance of gene mutations and epigenetics alterations in diseases;
- identify tumor classification criteria;
- understand biological and functional mechanisms at the base of cancer initiation and progression; -
- know the most important databases for DNA mutation classification, microRNA and protein pathway analysis as well as on-line resources for acquiring datasets to be applied to big data analysis,
- know and understand the principles at the base of personalized therapy in cancer to predict the response to therapeutic schemes.
- Content:
Topics of this module (6 CFU)
Fundamentals about the structure and the role of nucleic acids and proteins (8 hrs).
Genetic and epigenetic mechanisms at the base of oncogenesis (gene mutations, DNA damage repair system failure, methylation, microRNA dysregulation) (9 hrs).
Features of cancer cells and tumor classification (15 hrs).
Biological mechanisms of angiogenesis, invasion and metastasis (8 hrs).
Molecular pathways involved in cell differentiation, proliferation and survival (7 hrs).
Big data in cancer analysis (6 hrs).
Personalized therapy in cancer (5 hrs).
In vitro and in vivo models for the study of tumor biology (2 hrs). - Reading list:
Articles/reviews about the topics of the course
Study material provided by the Professor
