Université de Strasbourg

Nacho Molina

Biography - Nacho Molina

Institute of Genetics and Molecular and Cellular Biology (IGBMC), University of Strasbourg, CNRS and Inserm, France

Nacho Molina, USIAS Fellow 2021

Nacho Molina obtained a master's degree in theoretical physics at the Complutense University of Madrid in 2004 and a PhD in computational biology from the University of Basel in 2008. His doctoral work focused on genome evolution and regulatory network structure in bacteria under the supervision of Professor Erik van Nimwegen. Afterwards, he joined the group of Professor Felix Naef at the École Polytechnique Fédérale de Lausanne (EPFL) for his postdoctoral training from 2009 to 2013. In close collaboration with Professor Ueli Schibler, he developed novel computational methods to investigate the stochasticity of gene expression. His research revealed gene-specific kinetic signatures of transcriptional bursting in higher eukaryotic cells. For these results, he was awarded the Swiss Institute of Bioinformatics (SIB) Young Bioinformatician Award in 2011, and obtained a Chancellor’s fellowship to become an independent researcher at the University of Edinburgh in 2013. Since 2016, he is an investigator at the French National Centre for Scientific Research (CNRS) and group leader at the IGBMC in Strasbourg.

Nacho Molina’s main research focus at the IGBMC is to develop stochastic and biophysical models of eukaryotic gene regulation. His work lies at the interface between bioinformatics and biophysics, combining tools and methods from both fields to develop mechanistic models of transcriptional regulation based on both large-scale genome-wide data and single-cell imaging data. More recently, he started a new line of research combining mechanistic biophysical models and advanced machine learning methods to analyse single-cell genomic experiments.

Project - Modelling gene regulation dynamics across the cell-cycle in single embryonic stem cells

01/12/2021 - 30/11/2023

The cell cycle is a fundamental process of life, and tight control over its progression is crucial in many biological processes such as development, whereas loss of this control is implicated in diseases like cancer. However, despite all that we know about the cell cycle, a quantitative dynamic understanding of gene regulation throughout the entire cell cycle is still lacking. Moreover, the connection between the cell cycle regulation and pluripotency - the capacity of cells to become any cell of the body through a process called differentiation - is not well understood. In reality, the study of gene expression dynamics throughout the cell cycle is quite challenging. It has typically relied on the synchronization of cell populations; however, this approach has serious limitations as the protocols induce a strong perturbation on cells. Furthermore, and most critically, such approaches cannot be performed in vivo.

In this project, we propose to combine a state-of-the-art single-cell sequencing technology with biophysical modelling and advanced machine learning methods to construct high-resolution cell cycle gene expression maps in different stages of pluripotency and differentiation. This approach will allow us to shed new light on the connections between these processes and the cell cycle. We envision that these results will facilitate the development of new cell therapies. Ultimately, the computational framework developed in this project could potentially be applied to a wide range of systems, from cell lines to model organisms and human patients. Therefore, we expect that it will aid the scientific community to study the cell cycle in different biological contexts ranging from development to diseases like cancer.

Investissements d'Avenir