Platform to design new strategies for cellular reprogramming in regenerative medicine

SCHEME: CORE

CALL: 2013

DOMAIN: BM - Regenerative Medicine in Age-related Diseases

FIRST NAME: Antonio

LAST NAME: Del Sol Mesa

INDUSTRY PARTNERSHIP / PPP: No

INDUSTRY / PPP PARTNER:

HOST INSTITUTION: University of Luxembourg

KEYWORDS: Regenerative Medicine, Cellular Reprogramming, Gene Regulatory Network Inference and Analysis, Mathematical modelling, Computational Biology

START: 2014-02-01

END: 2017-01-31

WEBSITE: https://www.uni.lu

Submitted Abstract

Regenerative medicine, where in the ultimate goal is to replace damaged or regenerate healthy human cells or to stimulate repair mechanisms from endogenous cellular populations, is significantly impeded by our ability to understand reprogramming (un)differentiated cells into specialised cell types. Cellular reprogramming, including the conversion of one differentiated cell type to another differentiated cell type (trans-differentiation) or to a more immature, progenitor cell (dedifferentiation), is a complex regulatory event, which is carefully orchestrated by activation and repression of specific set of genes, called reprogramming determinants. An increasing amount of experimental evidences show that only few key driver genes are required for reprogramming. These findings also reveal the relevance of cellular reprogramming for regenerative medicine and disease modelling. Although substantial progress has been made in developing novel experimental reprogramming techniques, to date there is no unique protocol that is able to systematically predict combinations of driver genes which can trigger transitions between specific cellular phenotypes. Additionally, there are also no general procedures that can tackle the problem of low reprogramming efficiency and fidelity. To this end, based on transcriptomics profiling, in this proposal we seek to implement a computational platform that provide predictions of optimal combinations of genes which can enable reprogramming with increased efficiency and fidelity. Using discrete boolean and continuous modelling approaches, the proposed platform intends to infer gene regulatory networks at stable steady states from high-throughput transcriptomics data and thereby analyse the models to obtain the reprogramming determinants. This platform will thereby enable us to investigate relevant pathways and regulatory mechanisms that are responsible for the maintenance and conversion of cellular identities. We expect that such a platform will become highly relevant and useful to the experimental and computational communities working in the areas of regenerative medicine, cellular therapy, and is also likely to have much wider impact across many diverse fields of study.

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