CARDIOMyogenisis In Silico (CARDIOMIS): A computational framework for the study of stem cell differentiation towards cardiomyocytes
EXPL/CCI-BIO/1650/2021

We have a postdoc vacancy for this project!

Summary

One of the most fascinating questions in biology is how multicellular organisms achieve an astonishing diversity in cell types despite having essentially the same underlying genetic blueprint, the DNA, in each cell. How this cellular diversity is “bootstrapped” during development has been the focus of biological research for centuries, but only recently technological advents provided us with the tools to study the molecular details on a comprehensive level.

In this project, we will tackle this formidable challenge and obtain models for molecular circuits underlying stem cell differentiation. We will focus on cardiomyogenesis i.e. the differentiation of stem cells into cardiomyocytes. Although major components of cardiomyogenesis are known, many details of how these components interact in a tightly regulated manner during development and maturation are still to be discovered. Besides of its importance during development, cardiomyogensis is also of key interest in regenerative medicine.

The specific objectives are following:

• To derive gene regulatory networks for cardiomyogenesis including the maturation process
• To validate the functional relevance of importance of individual network components
• To predict the implications of these gene networks for cardiovascular diseases and to identify potential therapeutic targets

We will achieve these objectives by combining a wide range of computational and statistical approaches within a systems biology framework. In particular, we will apply and integrate methods of sequence analysis, motif detection, bulk and single cell transcriptomics, reverse engineering, and deep learning. Furthermore, we will develop novel approaches for the identification of activated or repressed regulons and for the consolidation of networks derived from cell population and single cell data. For the construction of gene networks, regulatory interactions will be combined with gene expression and epigenetic profiles. Additionally, potential interactions will be gained through the application of probabilistic, machine and deep learning techniques. Approaches of network biology will be applied to determine the structure, dynamics and functioning of the derived gene regulatory networks. To assess the medical relevance, included genes will be screened for genetic variants associated with cardiovascular disease human phenotypes. Importantly, the validity of the selected predictions for cardiomyogenic regulators will be validated by in vitro experiments and functional assays.

The project is hosted at the Chronic Diseases Research Centre (CEDOC) of the NOVA Medical School in Lisbon.

Project Team

• Matthias Futschik (PI), CEDOC, NOVA Medical School

• Fernando Cristo (Co-PI), CEDOC, NOVA Medical School

• José Belo, CEDOC, NOVA Medical School

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