Course at EMBL-EBI

Systems biology: from large datasets to biological insight

This course covers the use of computational tools to extract biological insight from omics datasets. The content will explore a range of approaches - ranging from network inference and data integration to machine learning and logic modelling - that can be used to extract biological insights from varied data types. Together these techniques will provide participants with a useful toolkit for designing new strategies to extract relevant information and understanding from large-scale biological data.

The motivation for running this course is a result of advances in computer science and high-performance computing that have led to groundbreaking developments in systems biology model inference. With the comparable increase of publicly-available, large-scale biological data, the challenge now lies in interpreting them in a biologically valuable manner. Likewise, machine learning approaches are making a significant impact in our analysis of large omics datasets and the extraction of useful biological knowledge.

In-person course
We plan to deliver this course in an in-person manner onsite at our training suite at EMBL-EBI, Hinxton.  Please be aware that we are continually evaluating the ongoing pandemic situation and, as such, may need to change the format of courses at short notice. Your safety is paramount to us; you can read our COVID guidance policy for more information. All information is correct at time of publishing.

Who is this course for?

This course is aimed at advanced PhD students, post-doctoral researchers, and non-academic scientists who are currently working with large-scale omics datasets with the aim of discerning biological function and processes. Ideal applicants should already have some experience (ideally one to two years) working with systems biology or related large-scale (multi-)omics data analyses.

Applicants are expected to have a working knowledge of the Linux operating system and the ability to use the command line. Experience of using a programming language (i.e. Python) is highly desirable, and while the course will make use of simple coding or streamlined approaches such as Python notebooks, higher levels of competency will allow participants to focus on the scientific methodologies rather than the practical aspects of coding and how they can be applied in their own research.


We recommend these free tutorials:

Regardless of your current knowledge we encourage successful participants to use these to prepare for attending the course and future work in this area. Selected participants will also be sent materials prior to the course. These might include pre-recorded talks and required reading that will be essential to fully understand the course.

What will I learn?

Learning outcomes

After the course you should be able to:

  • Discuss and apply a range of data integration and reduction approaches for large-scale omics data
  • Apply different approaches to explore omics data at the network level
  • Describe principles behind different machine learning methods and apply them on omics datasets to extract biological knowledge
  • Infer biological models using statistical methods
  • Identify strengths and weaknesses of different inference approaches
  • Compare signal propagation through logic modelling vs diffusion-based approaches
Course content

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

  • Data reduction and data integration methods – including comparisons of major approaches through lectures and practical exercises
  • Machine and deep learning – practical exercises on supervised machine learning, including classification and regression, graph neural network and deep learning
  • Functional inference from omics data – approaches to extract signatures of cell state from omics data including transcription factor activation and kinase activity states. Extraction of upstream signaling pathways from transcriptomics datasets
  • Network inference and signal propagation – network inference approaches from omics data
  • Introduction to executable modelling – including how to fit omics data to executable and predictive logic models

Trainers

Evangelia Petsalaki
EMBL-EBI
Federica Eduati
Eindhoven University of Technology
Konrad Förstner
TH Köln – University of Applied Sciences
Girolamo Giudice
EMBL-EBI
Ioannis Kamzolas
EMBL-EBI
Danila Bredikhin
EMBL
Javier De Las Rivas
University of Salamanca
Aurelien Dugourd
Heidelberg University
Alfonso Valencia
Barcelona Supercomputing Center
Sara-Jane Dunn
DeepMind
Emma Dann
Wellcome Sanger Institute and EMBL-EBI
Mikhail Papkov
University of Tartu

Programme

Time (BST)

Topic

Trainer

Day one - Monday 4 July 2022 - Data reduction and batch effects

10:30 - 10:45

Arrival and registration

 

10:45 - 11:30

Intro to the course and EMBL-EBI

Patricia Carvajal Lopez

11:30 - 12:00

Icebreaker

Patricia Carvajal Lopez

12:00 - 13:00

Data reduction and batch effects

Evangelia Petsalaki, Girolamo Giudice, Ioannis Kamzolas 

13:00 - 14:00

Lunch

 

14:00 - 15.30

Data reduction and batch effects

Evangelia Petsalaki, Girolamo Giudice, Ioannis Kamzolas 

15:30 - 16:00

Break

 

16:00 - 17:00

Machine Learning

Konrad Förstner

18:00

Dinner in Conference Center

 

Day two - Tuesday 5 July 2022 - Machine Learning

09:00 - 10:30

Machine Learning

Konrad Förstner

10:30 - 11:00

Break

 

11:00 - 13:00

Machine Learning

Konrad Förstner

13:00 - 14:00

Lunch Break

 

14:00 - 15:30

Deep Learning

Mikhail Papkov

15:30 - 16:00

Break

 

16:00 - 16:45

Deep Learning

Mikhail Papkov

16:45 - 17:30

Flash talks I

 

17:30 - 18:30

Poster session I

 

19:00

Dinner at Conference Centre

 

Day three - Wednesday 6 July 2022  - Data integration

09:00 - 10:30

Integration using MOFA intro + practical

Danila Bredikhin

10:30 - 11:00

Break

 

11:00 - 12:30

Integration using MOFA intro + practical

Danila Bredikhin

12:30 - 13:30

Lunch break

 

13:30 - 15:00

Single Cell multiomics data integration

Emma Dann

15:00 - 15:30

Break

 

15:30 - 16:45

Single Cell multiomics data integration

Emma Dann

16:45 - 17:30

Flash talks II

 

17:30 - 18:30

Poster session II

 

19:00

Dinner in Conference Center

 

Day four - Thursday 7 July 2022 - Network inference and signal propagation

09:00 – 10:00

Introduction to Cytoscape

Javier De Las Rivas

10:00 - 10:30

Break

 

10:30 - 12:00

Network inference

Federica Eduati & Javier De Las Rivas

12:00 - 13:00

Lunch break 

 

13:00 - 14:00

Network inference practical

Javier De Las Rivas 

14:00 - 15:30

Basics of logic modelling + practical

Federica Eduati 

15:30 - 16:00

Break

 

16:00 - 17:00

Keynote Lecture: "

Automated Synthesis and Analysis of Logical

Network Models to Study Pluripotency"

Sara-Jane Dunn (remote)

18:00

Dinner at the Red Lion

 

Day five - Friday 8 July 2022 - Network inference and signal propagation

09:00 - 11:00 

Data analysis to logic modelling + practical

Aurelien Dugourd

11:00 - 11:30

Break

 

11:30 - 12:30

Keynote Lecture about PerMedCoE project (remote)

Alfonso Valencia (remote)

12:30 - 13:30

Network diffusion for signal propagation (lecture)

Girolamo Guidice

13:30 - 14:00

Final discussion session

All

14:00 - 14:15

Course wrap-up and feedback

Patricia Carvajal Lopez

14:15 

Lunch and end of course

 

 

Please note minor programme changes may occur. 

Please read our page on application advice before starting your application. In order to be considered for a place on this course, you must do the following:

  • Complete the online application form providing answers as directed
  • Ensure you add relevant information to the "submission details" section where you are asked to provide information on your: 
    • pre-requisite skills and knowledge
    • current work and course expectations 
    • current research 
  • Upload a letter of support from your supervisor or a senior colleague detailing reasons why you should be selected for the course

Please complete all sections and upload your letter of support by Friday 25 March 2022. We will not consider incomplete applications. 

All applicants will be informed of the status of their application (successful, waiting list, unsuccessful) by Friday 08 April 2022. If you have any questions regarding the application process please contact Meredith Willmott (meredith@ebi.ac.uk). 

All participants are expected to present a poster that will be displayed during the course outside the training room. Please send your poster in PDF format to Meredith Willmott (meredith@ebi.ac.uk) and we will print it on campus. 
All posters should:

  • be A2 in size - 420mm x 594 mm 
  • be in a portrait orientation
  • include your photograph and contact information

We cannot display posters of a different size or orientation.

We expect the posters to act as a talking point between you, other participants and the trainers on the course. The posters will be displayed throughout the week so people can view them during breaks and lunch. They should give the reader an idea of the work you are engaged in, what you are planning to do next, and anything of interest that might be useful for sharing with the gathered participants.

Accommodation will be provided in the Wellcome Genome Campus Conference Centre Monday-Friday inclusive. Please contact the Conference Centre directly if you wish to arrange to stay additional nights. The course fee includes breakfast and evening meals at Hinxton Hall and the Red Lion Pub in nearby Hinxton village, as well as breaks and lunches outside the EMBL-EBI training rooms.

Applications closed
25 March 2022

04 - 08 July 2022
European Bioinformatics Institute United Kingdom
£825.00
Contact
Meredith Willmott
Open application with selection
30 places

Organisers
  • Evangelia Petsalaki
    EMBL-EBI
  • Konrad Förstner
    TH Köln – University of Applied Sciences
  • Federica Eduati
    Eindhoven University of Technology
  • Patricia Carvajal Lopez
    EMBL-EBI

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