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SYSTEMS BIOLOGY

Degree course: 
Corso di Second cycle degree in BIOMEDICAL SCIENCES
Academic year when starting the degree: 
2018/2019
Year: 
2
Academic year in which the course will be held: 
2019/2020
Course type: 
Supplementary compulsory subjects
Language: 
English
Credits: 
4
Period: 
First Semester
Standard lectures hours: 
36
Detail of lecture’s hours: 
Lesson (24 hours), Exercise (12 hours)
Requirements: 

Basics in mathematics, genetics and molecular biology from B.Sc. courses

Final Examination: 
Orale

Oral exam - free discussion of one of the topics

Assessment: 
Voto Finale

• Understand how emergent properties of a complex system may provide hints to understand the mechanism under lying a given phenotype
• Learn how to get mechanistic information from large datasets obtained through an unbiased approach and how to evaluate the false discovery rate of proposed mechanisms
• Understand and analyze protein networks and get advantage of network analysis tools
• Learn how to build deterministic and stochastic bottom-up models
• Learn to know the main network motifs in transcriptional and signal transduction networks and their dynamics

Introduction to SB. Over-representation Analysis (1 ECTS).
Network theory. Biological networks (2 ECTS).
Modeling biological circuits (1 ECTS)

1. Introduction
What is Systems Biology? Nonlinearity and stochasticity in biological systems. Holism vs. Reductionism, Induction vs. Deduction. Modeling complexity.
2. Over-representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) of pathways, ontologies and interactions
Aims. The Fisher’s Exact Test. Databases for ORA. Tools for ORA. Gene Set Enrichment Analysis
3. Graphs and Networks
Basics of graph theory. Descriptive properties and network statistics. Community finding, Clustering and Ranking . Graph representation of a biological system: gene/protein networks. Building and analyzing metabolic/signaling networks in the Cytoscape environment.
4. Modeling biological circuits
Deterministic vs. stochastic models. Reaction-based models. Ordinary differential equations.
5. Transcriptional regulation networks
Introduction to transcriptional regulation. Patterns and network motifs. Negative auto-regulation. The feed-forward loop is a network motif. Temporal programs in sensory transcription networks. Topological generalization of motifs. Signal transduction pathways.
6. Conclusions 87

Lecture notes written by the teacher

Convenzionale

Classroom (66%) and computer room (34%) lectures

None

Professors

FASANO MAURO

Borrowers