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Computational Biology Group

The Computational Biology Group at the LCSBFrom left to right: Menglin Zheng, Maureen Manche (left), Céline Barlier, Mohammad Ali, Antonio del Sol (Principal Investigator), Satoshi Okawa, Sascha Jung, Srikanth Ravichandran, Andras Hartmann, Gaia Zaffaroni.

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 signalling networks) mediate cellular processes involved in cellular differentiation and reprogramming. The group develops 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 modelling cell-environment interactions within the context of tissue regeneration. The development of these models is essential in order 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, the group also studies 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 diagnostic biomarkers. Thus, diseases can be diagnosed, treated and prevented by understanding and intervening in the networks that underlie health and illness.

[Contact : Prof. Dr. Antonio del Sol Mesa]












Figure: Modeling Cellular Reprogramming Using Network-based Models

Head of Team

Prof. Dr. Antonio del Sol