Framework for high-throughput personalization of logical models using multi-omics data

Abstract

Part of Chapter 4 “Mathematical Modelling of Signalling Networks in Cancer” of the book “Computational Systems Biology Approaches in Cancer Research”. Mathematical models of cancer pathways are built by mining the literature for relevant experimental observations or extracting information from pathway databases to study successive events of tumorigenesis. As a consequence, these models generally do not capture the heterogeneity of tumors and their therapeutic responses. We present here a novel framework, PROFILE, to tailor logical models to particular biological samples such as patient tumors, compare the model simulations to individual clinical data such as survival time, and investigate therapeutic strategies. Our approach makes use of MaBoSS, a tool based on Monte-Carlo kinetic algorithm to perform stochastic simulations on logical models resulting in model state probabilities. This semi-quantitative framework allows to integrate mutation data, copy number alterations (CNA), and expression data (transcriptomics or proteomics) into logical models. These personalized models are validated by comparing simulation outputs with expression of patient biomarkers, or clinical data, and then used for patient-specific high-throughput screenings investigating the effects of new mutations or drug combinations. Our approach aims to combine the mechanistic insights of logical modeling with multi-omics data integration to provide patient-relevant models. This work leads to the use of logical modeling for precision medicine and will eventually facilitate the choice of patient-specific drug treatments. &: equal contribution

Arnau Montagud
Arnau Montagud
Researcher on Systems Biology / Data Science / Models

My research interests include Boolean and multiscale modelling, data analyses and data integration.

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