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Network Visualisation and Analysis of Biological Data


This two day course will cover network-based approaches to visualise and analyse complex biological ‘big’ data and model pathway systems.  The course will be centred on the use of BioLayout Express3D, a tool developed between scientists at the University of Edinburgh and EBI over the last 10 years. 

BioLayout provides rapid and versatile means to explore and integrate very large datasets, providing a stunning interface to visualise the relationships between 10’s of thousands of data points. Originally designed for the analysis of microarray data, it is equally effective in analysing data matrices from other analysis platforms.

Day one of the course will introduce principles of network analysis and their use as a generic medium to understand relationships between entities.   I will introduce the basics of network visualisation and navigation within BioLayout and principles of correlation analysis of data matrices. We will then explore how data can be explored and clustered within the tool and how you can use the software to rapidly extract meaning from large and complex datasets.

Day two will focus on pathway modelling. I will explain how to collate information about a given system of interest from the literature, and to turn this information into a logic-based pathway model. We will then explore how these models can be parametrised and imported into BioLayout where simulations can be run that model the dynamics of these systems under different conditions. For more information see:   



Prof. Tom Freeman, The Roslin Institute


Audience and Prerequisites

  • Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals


Syllabus, Tools and Resources

During this course you will learn about:

  • Basic principles of network analysis
  • Data analysis with BioLayout Express
  • Network-based approaches for visualization and analysis of complex biological ‘big’ data
  • Basic principles of pathway modelling


Learning Objectives

After this course you should be able to:

  • Understand how BioLayout Express can help you to visualize network data
  • Collate pathway information from the literature, and turn this information into a logic-based pathway model



Day 1: Analysis of transcriptomics data and other data matrices

Session 1. 

  • Participant introductions
  • Introduction to the course: overview of aims and objectives, course content
  • Introduction to basic concepts of network analysis, background to BioLayout Express3D
  • Introduction to BioLayout Express3D: data inputs, navigation, 2D vs 3D, overview of tool‘s main and functionality (Practical 1) 

Session 2.

  • Introduction to microarray data and network analysis of this and other high dimensional. 
  • BioLayout Express3D practical: Preparation of input files, use of correlation values, graph structure, clustering and data handling (Practical 2).

Session 3.

  • A worked example: walk through the analysis of a new dataset. Participants will have the data and can follow along on their own machines or just watch (Practical 3).

Session 4.

  • Participant data analysis: Opportunity of participants to examine data from their own lab or data from another lab of interest to them (Practical 4). 

Day 2 Construction and analysis of pathway models

Session 5.

  • Introduction to biological pathway: Basic concepts in pathways, why they are useful, available resources, their depiction, modelling.

Session 6.

  • Introduction to yED graph editor: network editing, layout, navigation, overview of tool‘s main and functionality, drawing in the modified Edinburgh Pathway Notation (mEPN) system (Practical 5).

Session 7.

  • Petri net-based stochastic modelling of pathway systems using mEPN diagrams and BioLayout Express3D: Background to the method, relationship to other modelling methods, exploration of the approach (Practical 6).

Session 8.

  • Open session and wrap up.



Visualizations of complex biological data and systems. (A) Correlation graph showing similarity between approximately 2,000 human cell and tissue samples; (B) Correlation graph showing similarity between approximately 20,000 human genes expressed in data shown in A; (C) Simulation of flow through biological pathways; (D) graph-based assembly of human DNA sequence data.



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