SYSTEMS BIOLOGY
- Overview
- Assessment methods
- Learning objectives
- Contents
- Full programme
- Delivery method
- Teaching methods
- Contacts/Info
Basics in mathematics, genetics and molecular biology from B.Sc. courses are sufficient for a fruitful learning.
Oral exam – the teacher selects one of the topics of the course and the student discusses it in a free format.
LEARNING OBJECTIVES
The present course is integrated in the wide learning objectives of the whole MS degree in Biomedical Sciences. Indeed, it provides data analysis and interpretation tools that may apply to several experimental approaches, such as proteomics, next-generation sequencing and metabolomics. In particular, the student will start with a general overview on controversies in the epistemological definition of the scientific approach, such as holism vs. reductionism or inductivism vs. deductivism. The holistic approach that characterizes systems biology will be exploited through pathway analysis and network theory, to finish with bottom-up modeling of biological circuits.
EXPECTED LEARNING OUTCOMES
At the end of the course, the student is expected to be able to:
1. Understand how emergent properties of a complex system may provide hints to understand the mechanism under lying a given phenotype
2. Obtain mechanistic information from large datasets obtained through an unbiased approach and how to evaluate the false discovery rate of proposed mechanisms
3. Understand and analyze protein networks and get advantage of network analysis tools
4. Build deterministic and stochastic bottom-up models
5. Identify the main network motifs in transcriptional and signal transduction networks and their dynamics
Part 1. Introduction to SB. The Fisher exact test. Over-representation Analysis. Gene.Set Enrichment Analysis (1 ECTS).
Computer room session on Part 1 (1/3 ECTS).
Part 2. Graph and Network theory. Network statistics and topological analysis. Biological networks (1 ECTS).
Computer room session on Part 2 (1/3 ECTS).
Part 3. Deterministic and stochastic models. Modeling biological circuits (1 ECTS).
Computer room session on Part 3 (1/3 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.
Contents are transferred to students by integrating classroom lectures (3 ECTS) and computer room practical sessions (1 ECTS). Each of the three parts of the course is organized in a quite compact mode so to allow students to revise in the next week the contents of the teaching unit (lecture or practical activity).
The lecturer meets the students by e-mail accomodation.