66576 - SYSTEMS AND IN SILICO BIOLOGY

Learning outcomes

At the end of the course, the student acquires advanced machine learning based approaches (Support Vector Machine, Conditional Random Fields, Hybrid methods) to complement previous expertise. Problems of Systems Biology will be introduced with focusing on network theory and dynamic modeling to approach complexity at the cell level. In particular, the student will be able to: - understand and modeling biological complexity; - modeling time evolution of a biological system; - predicting protein-protein interaction and DNA/RNA protein interaction.

Course contents

ADVANCED METHODS FOR DATA ANALYSIS
Statistical models for significance assessment: z-test, t-test, ANOVA, chi-square-test
Methods for qualitative data analysis: Principal component analysis, Correspondence analysis
Clustering methods
Support Vector Machines
Kernel methods

INTRODUCTION TO SYSTEMS BIOLOGY
Biological Systems
Experimental Techniques
Genomics, Proteomics, Interactomics, Transcriptomics, Metabolomics
Basics on Model
Mathematical Methods: Networks
Mathematical Methods: Differential equations (Basics)
Mathematical Methods: Process Algebra (Basics)

A MODEL SYSTEM: THE TRANSCRIPTION NETWORK IN PROKARYOTS
-Elements of Transcription Networks
-Dynamics and Response Time of Simple Gene Circuits
-Negative Auto-Regulation Speeds the Response Time of Gene Circuits
-Negative Auto-Regulation Promotes Robustness to Fluctuations in Production
-Positive auto-regulation speeds responses and widens cell-cell variability
-The Feedforward Loop (FFL) is a Network Motif
-The Structure of the Feedforward Loop Circuit
-Dynamics of the Coherent FFL with AND-Logic
-The C1-FFL is a Sign-Sensitive Delay Element
-The Incoherent FFL: a pulse generator and response accelerator

Readings/Bibliography

Durbin R, Eddy S, Krogh A, Mitchison G (1998) Biological Sequence Analysis:
Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press [ISBN 0-521-62971-3]

Bishop C (2006) Pattern recognition and Machine Learning. Srpinger [ISBN 0-38-731073-8]

Klipp, E., Herwig, R., Kowald, A., Wierling, C. and Lehrach, H. 2005. Systems Biology in Practice: Concepts, Implementation and Application. Wiley-VCH , Weinheim. ISBN 3-527-31078-9

Aron U. 2006. An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC Mathematical and  Computational Biology (Vol. 10). ISBN-13: 9781584886426

Teaching methods

Lectures

Assessment methods

The final exam consists of a written test followed by an oral discussion.   
It aims at assessing the achievement of the learning goals of the course:  
- the knowledge of the theory and applications of the basic statistical tools for the analysis of complex systems (significance tests, PCA);   
- the knowledge of the theory and applications of Support Vector Machines and related kernel methods;  
- the knowledge of the theory and applications of clustering methods;  
- the knowledge of the theory of complex networks and their application to the description of biological systems;
- the knowledge of the basic theory of ordinary differential equations and their application to the description of biological systems.

Teaching tools

Office hours

See the website of Pier Luigi Martelli