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Course Informatics Guide
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From data to models to digital twins
This advanced course will focus on integrating biological data in computational models to study complex disease mechanisms, focusing on oncology and autoimmunity. It will emphasise building mechanistic computational models that can be used to analyse a system from a dynamic perspective and give insights into the emerging behaviour under multiple perturbations. The course will present examples from small to medium-sized models to developing large-scale, multicellular digital twins, covering the state of the art and the new developments in the field.
The participants will be guided through the basics of network biology, with use cases ranging from cancer to inflammation and autoimmunity. Networks are the backbone of biological mechanisms, as nothing acts in isolation in living organisms. Graph-based models can formalise and integrate large parts of prior biological knowledge, serve as templates for visualising and analysing “omics” datasets and enable novel insights and predictions. Adding the mathematical description of the interactions allows us to perform simulations and study the behaviour of these systems in time and under multiple scenarios. In other words, a formal description of the interactions will enable us to pass from static to dynamic, executable models of biological processes. Executable modelling is a powerful tool for capturing networks’ dynamic behaviour, revealing the system’s emergent behaviour under different conditions by performing in silico simulations and perturbations.
This course is aimed at PhD students, postdocs and more senior scientists with backgrounds ranging from experimental biology, clinical research, healthcare and hospital practice, data science, bioinformatics, who are interested in using systems biology approaches and computational modelling to tackle biological and biomedical problems concerning human disease.
After completing the course, participants should be able to:
- Understand the differences between static and dynamic representations of disease mechanisms
- Understand basic concepts of discrete modelling approaches
- Retrieve disease networks/ mechanisms from dedicated public repositories and databases, and use them as model templates
- Use a range of computational modelling software to develop and analyse discrete computational models
- Use available high and low throughput data to feed and train the models
The course will consist of lectures, discussions, and computational exercises covering as many of the following topics as possible:
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Static Disease Networks (molecular maps, pathway assembly, structural analysis etc.)
- Overview of databases/ resources
- Different network representations using systems biology graphical notation (SBGN) languages (Process Description, Activity Flow)
- Interactive network visualisation and topological analysis
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Dynamic Disease Networks (Stochastic Models, Logical (Boolean)/ Discrete Models, Hybrid models)
- Network/ Model Curation and sharing
- Disease Boolean Networks
- Logical/Discrete modelling and simulation of disease networks, and analysis of their dynamical properties
- Stochastic modelling and simulations Data integration
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Datatypes and discretization
- Integrating results from machine learning and AI into mechanistic models
- Data flows for digital twins
Course Instructors
- Anna Niarakis, Université de Toulouse III - Paul Sabatier, Center of Integrative Biology, France
- Ben Hall, University College London, UK
- David Shorthouse, University College London, UK
- Sylvain Soliman, Inria Saclay, France
- Tomas Helikar, University of Nebraska - Lincoln, USA
- Pedro T. Monteiro, IST / INESC-ID - University of Lisbon, Portugal
- Arnau Montagud, Institute for Integrative Systems Biology (I2SysBio), CSIC-UV, Spain & Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Rahuman S Malik Sheriff, European Bioinformatics Institute, UK
- Martina Summer-Kutmon, Maastricht Centre for Systems Biology and Bioinformatics (MaCSBio), Maastricht University, the Netherlands
Wellcome Connecting Science Team
- Alice Matimba, Head of Training and Global Capacity
- Karon Chappell, Event Organiser
- Martin Asltett, Informatics Manager
- Vaishnavi Vikas Gangadhar, Informatics Technical Officer
The course data are free to reuse and adapt with appropriate attribution. All course data in these repositories are licensed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
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