Gene regulation is one of the main drivers of a cell's fate and function, but current methods that infer genome-wide regulatory networks have problems with inferring causal and/or combinatorial interactions. Pooled single-cell screening technologies, such as those developed in the host lab, now provide a powerful alternative to regulatory network inference. However, as this technology matures, data analysis is now more and more becoming the rate-limiting step in inferring causal and combinatorial regulatory networks.
In this project, I will develop a combined computational and experimental workflow to infer regulatory networks. At the computational side, I will develop an algorithm that models how combinations of transcription factors affect a cell's state. The model will go beyond a ball-stick model, and investigate combinatorial logic, dosage effects, cell proliferation and cellular heterogeneity. The algorithm will also propose new combinations that may move a cell towards a fate of interest. Through multiple rounds of modelling and experimentation, I will iteratively improve our understanding of the causality and combinatorics of a regulatory network.
I will validate the workflow on mesenchymal stem cells (MSCs), a cell type for which some (combinations of) transcription factors that drive its differentiation are already known, but for which a combinatorial view across lineages is still missing. I will first start with easier well-studied systems, such as adipocyte differentiation, and later in the project move on to less-studied systems such as adipocyte progenitors.
By integrating experimental and computational techniques, the proposed project will provide a causal regulatory network across MSC lineages, and yield a framework to generate such a network in other systems. Moreover, the project will allow me to expand my computational skill set with emerging experimental techniques and management skills which will be invaluable for advancing my scientific career.
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