News:WORKSHOP: Computational Approaches to Study Biological Mechanisms. Bringing together large scale ‘omics approaches and classical computational modelling. REGISTRATION FEES NOW COVERED
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9.9 years ago
Daniel ★ 4.0k

UPDATE: Registration has now been extended to the 8th of August and additionally the workshop has now been sponsored to cover all costs. Please visit to find out more

The following workshop is associated with the international Environmental Omics Conference in Liverpool, UK from the 15th September. Registration and more information is available at

Computational Approaches to Study Biological Mechanisms

Bringing together large scale 'omics approaches and classical computational modelling

Organisers: Dr Natasha Savage and Dr Philipp Antczak

The inception of using modelling approaches in biology has revolutionised the understanding of biological systems and has greatly influenced today's state-of-the-art biological concepts and knowledge. Since their initial application half a century ago, the biological field has seen a number of technological advancements which allow us to study the complexity of biological systems in a much greater detail. 'Omics technologies have had a particular impact due to the ability of measuring molecular features on a genome wide scale. Such an unbiased approach to understanding biological systems has led to the development of less mechanistic and more correlative approaches, which allow the rapid development of potential molecular interaction networks.

In this workshop we will endeavour to provide the participants with an understanding of the approaches to developing both mechanistic and correlative models and study them in a biological context.

After a short introduction to the current state of modelling approaches and their limitations, the participants will be familiarised with the theoretical background in ordinary differential equations (ODEs). ODEs are widely used to study the dynamic behaviour of hypothesised reaction networks, investigating the feasibility of hypotheses. Further to this ODEs are used to suggest hypotheses and guide experimentation. Over the duration of the ODE section we will; become familiarised with simple systems of ODEs, learn how to design ODEs that represent various biological interactions, solve a biologically relevant system, analyse the solution, and understand the solution's relevance in the biological context.

In the second section of the workshop participants will explore network inference approaches to tease out the underlying regulatory network from high dimensional data. Dynamical networks provide a great resource for novel hypothesis generation due to the added directionality between the biological features represented in the data. Generally such links between features, such as genes, proteins and metabolites, are calculated using a correlative or mutual information based approach. In this particular example, the participants will learn to develop regulatory networks using TimeDelay-ARACNE: a dynamical modelling approach based on mutual information. We will showcase the advantages and disadvantages of the system by utilising the data generated in the first part of the workshop.

In the final part of the workshop participants will be invited to have an open discussion abased around the methods touched upon during the day. Possible topics of discussion include; the applicability of these methods in biological science, specific questions about technical aspects of the techniques, how one may utilise these techniques within their own work.


Part 1: Introduction to modelling approaches to study biological mechanisms

  • How can modelling help our problem?
  • How do we maximise the output?
  • What are the available tools?

Part 2: Representing and understanding interaction networks using ordinary differential equations (9am - 12pm)

  • Introduction to ordinary differential equations (ODEs)
  • Using ODEs to describe biological processes
  • Solving ODEs using matlab
  • Visualising results
  • Understanding results in a biological context

Part 3: Develop dynamical networks from time-series data (1pm - 4pm)

  • Introduction to Network inference
  • Network inference from data using TimeDelay-ARACNE
  • Understanding dependencies in a biological context

Part 4: Open discussion (4pm - 5pm)

  • Applicability of these approaches in biology
  • Technical issues
  • How to integrate techniques into the workflow
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