DEsigning new Paths in The differentiation Hyperspace
Description: The adult human organism contains heterogeneous reservoirs of pluripotent stem cells characterized by a diversified differentiation potential. Understanding their biology at a system level would advance our ability to selectively activate and control their differentiation potential. Aside from the basic implications this would represent a substantial progress in regenerative medicine by providing a rational framework for using small molecules to control cell trans-determination and reprogramming.Here we propose a combined experimental and modelling approach to assemble a predictive model of mesoderm stem cell differentiation. Different cell states are identified by a vector in the differentiation hyperspace, the coordinates of the vector being the activation levels of a large number of nodes of a logic model linking the cell signalling network to the transcription regulatory network.The premise of this proposal is that differentiation is equivalent to rewiring the cell regulatory network as a consequence of induced perturbation of the gene expression program. This process can be rationally controlled by perturbing specific nodes of the signalling network that in turn control transcription factor activation. We will develop this novel strategy using the mesoangioblast ex vivo differentiation system. Mesoangioblasts are one of the many different types of mesoderm stem/progenitor cells that exhibit myogenic potential. Ex vivo, they readily differentiate into striated muscle. However, under appropriate conditions they can also differentiate, into smooth muscle and adipocytes, albeit less efficiently. We will start by assembling, training and optimizing different predictive models for the undifferentiated mesoangioblast. Next by a combination of experiments and modelling approaches we will learn how, by perturbing the signalling models with different inhibitors and activators we can rewire the cell networks to induce trans-determination or reprogramming.
Amount: €2,640,000
Funding type: research
Funder scheme: ERC-AG
Start date: 2013-04-01
End date: 2018-09-30
Institution: UNIVERSITA DEGLI STUDI DI ROMA TOR VERGATA
Funded outputs count: 19
Funder award ID: 322749
OpenAlex ID: G6943385951