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

MACHINE LEARNING TOOLS FOR BIOINFORMATICS
Neural Networks
Support Vector Machines
Kernel methods
Deep learning methods
Decision trees and Random Forests

Applications to protein structure and function prediction

INTRODUCTION TO SYSTEMS BIOLOGY
Biological Systems
Experimental Techniques
Genomics, Proteomics, Interactomics, Transcriptomics, Metabolomics, Metagenomics, Epigenomics

Basics on Model
Mathematical Methods: Networks
Mathematical Methods: Differential equations

Kinetics of biochemical reactions and simple metabolic pathways

Transcription networks in Prokariotes.
Analysis of simple motifs (self-regulation, Feed-forward loops)

Readings/Bibliography

Slides of the lecture and papers cited therein
Suggested books for a deeper study: 

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

Goodfellow I, Bengio Y, Courville A. Deep Learning (2016) MIT Press [ISBN: 9780262035613]

Ingalls BP (2013) Mathematical Modeling in Systems Biology. MIT press [ISBN: 9780262018883]

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
The frequency requires to have passed the e-learning modules: Moduli 1 e 2 di formazione sulla sicurezza nei luoghi di studio, [https://elearning-sicurezza.unibo.it/].

Assessment methods

The final exam consists of a written test followed, if necessary, 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 machine learning tools for Bioinformatics

- the knowledge of the theory of complex networks and their application to the description of biological systems;

- the analysis and integration of omics data

- 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