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Лекция Антонио Дель Соль Меза «Systems Biology Approaches to Stem Cell Research and Disease Modelling»

Antonio_Sol.pngС открытой лекцией на Физтехе выступит профессор Люксембургского университета, биоинформатик Антонио Дель Соль Меза. Тема: «Systems Biology Approaches to Stem Cell Research and Disease Modelling». 

Также все желающие смогут пройти собеседование на позицию аспиранта или пост-дока в лабораторию профессора. Подробнее о требованиях к кандидату доступно по ссылке. Для участия в собеседовании необходимо до 17 мая включительно выслать своё резюме Сергею Викторовичу Леонову на почту leonov.sv@phystech.edu. Сотрудничество осуществляется в рамках коллаборативного проекта с Физтех-школой биологической и медицинской физики (ФБМФ).   

Время и место: 18 мая (четверг) в 12:00 в 107 БК. 

Abstract

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 modeling 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 diagnosis biomarkers. Thus, diseases can be diagnosed, treated   and prevented by understanding and intervening in the networks that underlie health and illness.
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