Alberts et al. BIOLOGIA MOLECOLARE DELLA CELLULA Zanichelli
Romualdi et al. FONDAMENTI DI BIOINFORMATICA - Zanichelli
Integrating material supplied by the teacher
Learning Objectives
Knolewdge acquired:
The course offers a deep knowledge of properties and functions of the main biomolecules that mediates several basic biologic processes, as well as the main techniques for their measureme,ts from an -omics point of view. It further offers the correct perspective for the bioinformatic investigation of system-level processes using modern methods and techniques.
Competence acquired
The student will learn the appropriate methodologies and approaches for the study of biological processes. It will also learn the usage of bioinformatics for data collection and integration.
Skills acquired
Use of bioinformatic tools to mine and investigate signalling and metabolic pathways.
Knowledge of important bioprocesses in different organisms.
Prerequisites
Knowledge of biochemistry,
molecular biology and cell biology
Teaching Methods
CFU: 6
Course total hours: 60
Theoretical lessons: 24 h
Applicative lessons: 12 h
Lab (computer): 24 h
Further information
The frequency of both frontal and lab lessons is strongly suggested. In the lab part, as examples, student’s selected datasets will be analyzed.
The course will be held primarily in the informatics labs of the Sesto Fiorentino campus. The labs are equipped with PC sufficient for all participating students, that will be able to face the proposed topic in a personal and individual way.
Type of Assessment
The exam will be based on an oral colloquium aimed at verifying the knowledge of the topics presented during the course as well as to assess the ability of the student to apply such knowledge to practical and experimental problems.
Mid-term examination is not foreseen.
Course program
Functional analysis of high-throughput data. Biostatistics (univariate and multivariate). Integration systems (genes, transcripts, proteins, metabolites). Genomic, metabolomic, trascriptomic, proteomic and metabolomic Applications. Metabolic and regulatory pathway analysis. Over Representation Analysis. Functional Class Scoring. Topology analysis. Metabolic reconstruction, biomodels and flux analysis.