Research Projects

This section introduces all current and prospective projects of the Computational Biology group.

Key Projects

Modelling mouse neural stem cells differentiation into astrocytes

This project aims to construct a gene regulatory network-based model to describe mouse neural stem cell (NSCs) differentiation into astrocytes, and their potential ability to de-differentiate under specific stimuli. The analysis of such a network will provide insights about the mechanisms involved in these processes and potential gene candidates to control and induce both differentiation and dedifferentiation. This is a collaborative effort of our group at LCSB, and Professor Noel J. Buckley from the Department of Neuroscience at the Institute of Psychiatry of the King’s College, London. The outcome of this project will be an important step towards reliable NSCs to astrocytes differentiation and de-differentiation models with potential application in related brain pathologies.

Differential network-based approach to designing new strategies for cellular reprogramming: application to cardiac cells

The goal of this project is to build a computational platform that integrates transcriptomic and epigenetic data, and to develop strategies based on differential network analysis for identifying optimal reprogramming determinants triggering transitions between specific cell types. This platform will be applied to the differentiation of cardio-progenitor cells into different types of cardiomyocytes in order to address relevant questions, such as how to direct differentiation into desired cell types in a controlled fashion, and how to increase the efficiency of this process for clinical purposes. This project is being carried out in collaboration with Professor Christine Mummery at Leiden University Medical Centre, the Netherlands. The outcome of this project shall assist researchers in the field in designing new strategies in regenerative medicine.

Modelling Stochasticity and Population Heterogeneity in Pluripotent States

In this project we intend to model stochasticity of gene expression in the pluripotent state, bringing together epigenetic and transcriptional regulatory interactions in the context of differentiation. This stochastic model will enable us to elucidate the role of cellular heterogeneity in differentiation, and will be useful to predict more efficient combinations of cell fate determinants triggering differentiation. Our predictions will be experimentally validated in different stem/progenitor cell types. This research line is part of a collaboration project with Professors Ihor Lemischka and Kateri Moore at the Black Family Stem Cell Institute of the Mount Sinai School of Medicine, United States. We expect that the scientific outcome of this project will be useful for designing alternative strategies for cellular differentiation.

Network analysis of synaptosomes in natural and Alzheimer’s disease aging

This project aims to elucidate the dynamical molecular mechanisms underlying normal and Alzheimer’s disease aging using proteomics data from hippocampal synaptosome fractions of aging wild-type mice, and two different transgenic mouse models for Alzheimer‘s disease. It is based on a strategic collaboration with Dr. Ronald van Kersteren and Prof. Guus Smit from the Center for Neurogenomics and Cognitive Research (VU University Amsterdam, The Netherlands).  Importantly, we will apply network analysis methods of the full synaptosome proteomics data to elucidate the dynamical behavior and functional consequences underlying the progression of normal aging and Alzheimer’s disease. This network-based statistical analysis and modelling pipeline will enable us to deduce dysregulation pathways in the brain and predict useful targets for future treatment.