The goal of this project is to build a computational platform that relies on gene regulatory network models to develop strategies for identifying instructive conversion factors triggering transitions between specific cell types. Importantly, it is applicable for closely related cell sub-types with similar transcriptional profiles. This platform will be applied to projects relevant to cell therapies and regenerative medicine. For example, in collaboration with Prof. Michele De Luca at the Centre for Regenerative Medicine in Modena, we intend to predict reprogramming determinants to derive corneal limbus stem cells from cultured epithelial keratinocytes. The derived experimental protocol will be further optimized and used for treating patients who have lost their corneal limbus stem cells due to injury or burn. Further, in collaboration with Prof. Mark Tuszynski at the Center for Neural Repair in San Diego, we will apply our platform to generate corticospinal tract neuron progenitors in-vitro for transplantation in cases of spinal cord injuries. Finally, we have generated predictions for the efficient conversion of cardiac right ventricular cells into left counterparts, which are being currently validated by Prof. Deepak Srivastava at the Gladstone Institutes in San Francisco. The outcome of this project will be essential for designing strategies to treat patients with cardiovascular diseases.

Current reprogramming protocols are only able to reprogram one cell type into another.
However, derivation of multiple cell subtypes from a single source of cells is a challenge and is of clinical interest. In this project we aim to develop a computational method that predicts instructive factors that can convert one cell type into multiple cell subtypes in a controlled way. The experimental validation will be performed in collaboration with Prof. Ernest Arenas at Karolinska Institute in Stockholm. Experiments on cultured astrocyte cell colonies will be conducted to induce cellular conversions into different populations of dopaminergic neuron subtypes. The outcome of this project will be used in a disease model of Parkinson’s disease, which will enable drug screening and drug development strategies for therapeutic intervention.

In this project our goal is to model tissue-specific cell-cell communication networks to gain insights into general principles of tissue homeostasis and to design strategies to rejuvenate aged tissues. In particular, in collaboration with Prof. Pura Muñoz at the Pompeu Fabra Univeristy in Barcelona, we plan to coin a systems biology definition of tissue homeostasis. This will enable us to propose intervention strategies for the rejuvenation of old muscle tissue based on the cell-cell communication network model. Further, in collaboration with Prof. María Martínez Chantar at CIC bioGUNE in Bilbao, we plan to apply the developed methodology to the rejuvenation of old liver tissue for increasing its regenerative capacity. The validation of predicted target molecules in both projects will be conducted in in-vivo models.

The systemic inflammatory response syndrome (SIRS) is a complex of nonspecific symptoms. SIRS is a life-threatening condition characterised by a mortality rate of 7-25% depending on timely recognition of the underlying disease. Currently, we lack targeted therapy for SIRS not only because triggers are often unknown, but also because host responses to triggers may vary.
In this project, we will develop a novel machine learning algorithm for integrative analysis of the multiOMICs data. Notably, the algorithm will not only predict appropriate diagnostic patterns for each cohort of patients, but also enable identification of important features and their molecular interactions that together determine the course of disease progression. In collaboration with Prof. Catharina Schütz at Technical University Dresden, experiments will be conducted to validate the predicted interactions and provide mechanistic insights into patient diagnostics and treatments.