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Computational Biology Group

The Computational Biology Group at the LCSBFrom left to right: Sevgin Demirci, Satoshi Okawa, Aravind Tallam, Gökhan Ertaylan, Antonio de Sol (Principal Investigator), Sarah Killcoyne, Sascha Zickenrott, Vladimir Espinosa, Susana Martínez Arbas, Kavita Rege

About the Computational Biology Group

The main goal of the Computational Biology Group is to understand how molecular networks (e.g. gene regulatory, protein-protein interactions and signaling networks) mediate cellular processes involved in cellular differentiation and reprogramming. We develop mathematical and computational approaches using multiple sources of biological information (e.g. transcriptomics, epigenomics, proteomics) in order to build network-based models that consider key molecular characteristics underlying these cellular processes. These models, which range from single-cell to cell population levels, aim to address relevant questions in the field of stem cell research and regenerative medicine including: the study of iPSC disease models; increasing efficiency and fidelity of cellular differentiation and reprogramming; and modeling cell-environment interactions within the context of tissue regeneration. The development of these models is essential to gain a better understanding of cellular differentiation and reprogramming, which will enable researchers to design novel strategies to guide stem cell experimental research and regenerative medicine approaches.

In addition, we also study how perturbations of molecular networks give rise to phenotypes resulting in human disease. These perturbations, which range from the complete loss of a gene product to the specific perturbation of a single molecular interaction, may arise from genetic variations, epigenetic modifications, and genome-environment interactions. We model disease states as stable equilibrium states of gene regulatory networks, allowing researchers to address a number of relevant issues, such as the identification of molecular signatures of disease states and their master regulators. These might serve as novel drug targets or diagnosis biomarkers. Thus, diseases can be diagnosed, treated and prevented by understanding and intervening in the networks that underlie health and illness. [Contact : Ass. Prof. Dr. Antonio del Sol Mesa]

 

 

 

 

 

 

 

 

 

 

 

Figure: Modeling Cellular Reprogramming Using Network-based Models

Head of Team


 
Ass. Prof. Dr. Antonio del Sol

Members


 
Dr. Marek Ostaszewski (LCSB)