Systems Biology: From large datasets to biological insight

Date:

 Monday 8 Friday 12 July 2019

Venue: 

European Bioinformatics Institute (EMBL-EBI) - Training Room 1 - Wellcome Genome Campus Hinxton,  Cambridge CB10 1SD,  United Kingdom

Application opens: 

Friday 14 December 2018

Application deadline: 

Thursday 11 April 2019

Participation: 

Open application with selection

Registration fee: 

£630

Overview

For some time, advances in computer science and high performance computing have led to ground-breaking developments in systems biology model inference. However, only now has there been sufficient large-scale data available to parameterise these models and use them usefully. Similarly, machine learning approaches have recently started having a significant impact in our analysis of large omics datasets and extraction of useful biological knowledge.

Therefore this course, run in collaboration with the Wellcome Genome Campus Advanced Courses and Scientific Conferences Team, will provide timely advanced-level training in using large-scale multi-omics data and machine learning to infer biological models.

Audience

Applicants should be researchers who are using large multi-omics datasets to infer systems biology models. This is an advanced-level course, and so we will select applicants who already have some experience (ideally 1-2 years) of working with systems biology modelling or related large-scale multi-omics data analysis. Additionally, applicants will be expected to have a working knowledge of using Linux commands, and experience of using a programming language (e.g. Python or Perl).

Syllabus, tools and resources

The prgramme will include, lectures, discussions and practical computational exercises covering the following topics:

  • Introductory session
  • Omics data integration / reduction and interpretation
  • Protein-protein interaction, gene regulatory and signalling network inference
  • Logic modelling
  • Machine learning / deep learning for model inference
  • Genome scale models

Outcomes

After the course the participants should be able to:

  • Discuss and apply a range of data integration and reduction approaches for large scale omics data
  • Describe principles behind different machine learning methods and apply them on omics datasets to extract biological knowledge
  • Infer biological models using machine learning and statistical methods
  • Identify strengths and weaknesses of different inference approaches
  • Compare signal propagation through logic modelling vs diffusion-based approaches
  • Access, query and retrieve models from public repositories for systems biology

Programme

Time Topic Trainer

Day 1 - Monday 8th July

Introduction & Overview and Data reduction/integration session

08:30 - 08:45 Registration  
08:45 - 09:00 An introduction to EMBL-EBI Alexandra Holinski
09:00 - 09:15 Overview of course content Evangelia Petsalaki
09:15 - 09:45 Introduction to data reduction and integration methods Evangelia Petsalaki
10:15 - 10:45 Tea/coffee break  
10:45 - 12:45 Practical exercise using PCA for data reduction Evangelia Petsalaki
12:45 - 13:45 Lunch  
13:45 - 14:00 Matrix factorization approaches for data integration Ricard Argelaguet
14:00 - 15:45 Practical exercise using MOFA for data integration Ricard Argelaguet
15:45 - 16:15 Tea/coffee break  
16:15 - 17:15 Keynote lecture to be confirmed
17:15 - 18:30 Practical on visualisation and interpretation of data integration/reduction Evangelia Petsalaki
  Dinner

Day 2 - Tuesday 9th July

Network Inference

09:00 - 09:30 Introduction to network inference Evangelia Petsalaki
09:30 - 11:00 Protein interactions network inference Javier De Las Rivas
11:00 - 11:30 Tea/coffee break 
11:30 - 12:00

Introduction to team work projects

2 teams gene expression to gene regulatory networks

2 teams phosphoproteomics to signalling networks

 
12:00 - 13:00 Start of team work projects
13:00 - 14:00 Lunch  
14:00 - 16:00 Continue team work projects and prepare presentations
16:00 - 17:00 Keynote lecture: network inference and model parametrization Jan Hasenauer
17:00 - 18:30 Poster session with cheese and wine  
19:00 Dinner  

Day 3 - Wednesday 10th July

Signal propagation/modelling

09:00 - 12:45 modelling standards, tools and applications (including 0.5h break) Eva-Maria Geissen / Rahuman Sheriff / Sarah Keating / Nicolas Rodriguez
12:45 - 14:00 Lunch  
14:00 - 16:15 From data analysis to logic modelling (theory & practical) Julio Saez-Rodriguez
16:15 - 16:45 Tea/coffee break 
16:45 - 18:15 15 minute presentations from projects and discussions  
19:00 Dinner

Day 4 - Thursday 11th July

Signal propagation modelling ctnd & machine learning

08:45 - 11:00 Diffusion based approaches for signal propagation, Introduction and practical exercise Evangelia Petsalaki
11:00 - 11:30 Tea/coffee break  
11:30 - 12:30 Keynote: mechanistic modelling Jasmin Fisher
12:30 - 14:30 Lunch  
14:30 - 15:15 Introduction to supervised machine learning Konrad Förstner
15:15 - 16:00 Introduction & practical exercise: classification and regression Konrad Förstner
16:00 - 16:30 Tea/coffee break  
16:30 - 18:30 Practical exercise continued Konrad Förstner
19:00 Dinner  

Day 5 - Friday 12th July

Deep learning

08:00 - 08:30 Check-out  
08:45 - 11:00 Introduction to deep learning & Practical exercise Leo Parts, Dmytro Fishman
11:00 - 11:30 Tea/coffee break  
11:30 - 12:30 Practical exercise cntd Leo Parts, Dmytro Fishman
12:30 - 13:15 Keynote lecture to be confirmed
13:15 - 13:45 Q&A, course wrap-up and feedback
13:45 - 14:30 Lunch  
14:30 End of course