Work package 5.  Computational modelling

Work package leader: Prof. dr. Michael Meyer-Hermann.

 

Research question: How to develop and use computational disease models to gain insight in the role of the germinal center reaction (GCR) in B-cell lymphoma (BCL) and rheumatoid arthritis (RA)?

 

We address three key challenges to develop new hypotheses about the role of the GCR in BCL and RA. Firstly, the development of models that explicitly include (putative) disease mechanisms. Secondly, we will investigate how these models can optimally make use of the experimental data from a wide range of experimental technologies and sources (WP3 and WP4). Thirdly, how to combine different computational modelling formalisms. Models will be validated by dedicated experiments in WP3 and WP4 (complemented with data from public repositories), and interpretation of WP3 and WP4 data will be supported by the computational models. The iterative process between computational modelling and experiments is expected to elucidate and characterise molecular and cellular defects of the GCR in BCL and RA. The models will include different components including B cells, Tfh cells, FDCs, transcription factors, and oncogenes. We will integrate different modelling formalisms. Novel deep machine learning approaches will be used for the construction of phenomenological interaction networks and knowledge graphs (e.g. from omics (public) data, literature) that comprise integrated components (e.g., genes, epigenetics, phenotypic information) for normal and diseased conditions, time-points, and/or species. These networks will identify key determinants of the GCR in BCL/RA and provide input for the mechanistic models. Dynamic mechanistic models will be implemented as agent-based models (ABM) or as Ordinary Differential Equations (ODEs). Briefly, ODEs are used to study to overall dynamics of the GCR (cellular and molecular level) by representing its continuous components (e.g., transcription factors) as equations. In contrast, ABMs provide a direct (lattice-based) representation to study the dynamics of the GC at the cellular level by modelling properties and behaviour of individual agents (e.g., B cells). ABMs are more suitable to model temporal and spatial dynamics simultaneously. Additionally, we will use Partial Differential Equations to model GC chemokine gradients, and cellular Potts models to incorporate cell volumes. Mechanistic probabilistic models based on the description of genes as Piecewise Deterministic Markov Processes are used to infer gene regulatory networks (GRN) from single cell data.

COSMIC aims to develop multiscale models that integrate the cellular level (e.g., B cells) with the molecular level (e.g., GRNs within these cells), to investigate the effect of (disturbed) molecular pathways on the GC B-cell population. 

Expected results: (multiscale) models and insights of the role of the GCR in BCL/RA. Putative biomarkers and drug targets that will be tested in sillico and in WP3 and WP4

 

Go to Work package overview 


 

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