Mathematical modelling to translate -omics data in mechanistic understanding of diseases

Julio Saez-Rodriguez (Heidelberg University)

Keynote Lecture, Wednesday May 25th, 2022, 09:30-11:00, Chair: Baldo Oliva

Abstract: Modern technologies allow us to profile in high detail biological and medical samples at fast decreasing costs. New technologies are opening new data modalities, including to measure at the single-cell level and with spatial resolution. Computational models, in particular those built with machine learning, are expected to help us to extract insight form these data. Using biological knowledge to aid machine learning can significantly improve the results. Towards this end, we have developed a number of tools that range from a meta-resource of biological knowledge to methods to infer pathway and transcription factor activities from gene expression and subsequently infer causal paths among them. Furthermore, and to complement a large-scale basal profiling of samples, we have develop approaches to build dynamic logic models of molecular networks and how they response to perturbations such as drug treatment. I will illustrate their utility in cases of biomedical relevance and show how they improve our understanding of molecular processes, identify biomarkers, and point at novel therapeutic opportunities.