A computational framework to unravel gene regulatory mechanisms using single-cell omics data

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Identifying transcriptional regulatory mechanisms related to a specific context or a phenotypic outcome is an important biological problem. The transcriptional regulatory networks (TRNs), which represent the regulatory effects of transcription factors on the genes, have proven to be a useful model for describing gene expression programs in different biological contexts. TRNs are usually constructed computationally from bulk gene expression data across many samples, alone or in combination with other data types. However, since bulk omic datasets represent the 'average' profile of 'all' the cells in a sample, TRNs reconstructed based on these data types have limited value to study processes whose effects are best observed at a single cell resolution or scenarios in which samples contain heterogeneous cells. Consequently, the study of these processes and their TRNs should be performed using single-cell omics profiles of only cells involved in these processes. ******However, current methods for TRN reconstruction using single-cell data are usually counterparts of methods based on bulk data, and as such ignore challenges and opportunities that exist when using data from these new technologies. Moreover, these methods are agnostic to the phenotypic labels of different samples and cannot identify regulatory mechanisms that are responsible for the variation in a phenotype of interest. My plan is to develop a unified computational framework that will allow reconstruction of causal TRNs, across different samples containing (potentially) heterogeneous cells, at a single-cell resolution using multiomics datasets, related to a specific biological phenotype. This computational framework will be constructed based on novel machine learning and graph mining algorithms, which we will develop. ******This framework and its computational tools, developed as part of my research program, will have a significant impact in the fields of bioinformatics and computational genome biology by providing advanced novel methods for identification of regulatory mechanisms responsible for variation in a phenotypic outcome. They will unravel new biological mechanisms and will identify novel biomarkers related to a phenotype. In addition, they may reveal new ways of manipulating the phenotypic properties of a biological system (e.g. a cell, a disease, an organism): for example to improve the resistance of crops to pests or to develop new treatments for neurodegenerative diseases or cancer. These tools, which will be implemented as user-friendly publicly available software, will also enable experimental biologists (the end users) to analyze their data, extract new insights from it, and identify novel targets for their experiments. Finally, my program will have a significant impact by training the next generation of computational biologists and bioinformaticians at various levels of undergraduate, graduate (Masters and PhD) and postdoctoral.