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Integration of Systems and Synthetic Biology in Multicellular Systems

Design principles of how collective morphogenetic behaviors arise from genetically encoded GRNs of cells are inherently complex and incompletely understood. Advances in systems biology such as spatiotemporal single cell analysis and computational modeling provide the possibility to reverse engineer developmental processes [ link GRNs to cell-cell communications to self-organization behavior] to delineate the choice of factors to interrogate in vitro and in a rationale way. Subsequently, in vitro engineering can be carried out by devising gene circuits that are interfaced with the candidate cellular GRNs to understand the higher order transmission of signals from GRNs to Cells to Tissue morphogenesis /organ formation. Therefore collaborating with systems and computational biologist is one of the key facet in our program of research.


Targeting intera-cellular regulatory network:

It is critical to delineate how closely the engineered tissues resemble their in vivo human analogues. Through a collaboration with Patrick Cahan lab at Johns Hopkins University (Biomedical Engineering) we applied CellNet platform to perform analysis of our liver organoids . CellNet (Cell. 2014.) is a network biology platform that assess the fidelity of cell fate engineering and offer rational strategy to augment target fates. Our approach enabled identification of transcription factors for synthetic maturation of human liver organoids (Cell Systems 2021). We have also examined the similarity , differences and aberrant signatures in our developed tissues against their in vivo counterparts  (see other lab research areas).


Targeting inter-cellular regulatory network:
More recently we collaborated with Bar-joseph lab at CMU computational biology/ machine learning department. TraSig, a computational method for improving the inference of cell-cell interactions in scRNA-Seq studies. It utilizes the dynamic information to identify significant ligand-receptor pairs with similar trajectories, which in turn are used to score interacting cell clusters. We applied TraSig to our organoids and obtained unique predictions central for cell-cell interactions, vascularization and dynamic co-development of cell lineages in the organoids . 


Using TraSig to decode cell-cell communication signals in liver organoids : Read our paper by Li D and Velazquez JJ at Genome Biol. 2022.

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