| KEYWORD |
Optimal decomposition of synergistic cores
keywords GENE EXPRESSION DATA ANALYSIS, SYSTEMS BIOLOGY
Reference persons MARCO AGOSTINO DERIU, JACEK ADAM TUSZYNSKI
External reference persons Prof. Antonio del Sol, University of Luxembourg
Research Groups 28- biomedica
Description In our recent work we proposed a computational method for identifying a set of transcription factors (TFs) that are highly synergistically interacting with each other, and together determine cell/subpopulation identity [1, 2]. In this method we employed an information theoretic measure of synergy to capture such TFs that form the synergistic identity core of each cell/subpopulation. However, the synergistic identity core usually consists of ~10 TFs and we are now interested in decomposing it into subsets of TFs, such that TFs within each subset exhibit high redundancy among themselves but high synergy among TFs from other subsets. These optimally decomposed subsets of TFs would guide experimentalists in prioritizing candidate TFs for efficient cellular reprogramming.
A necessary step for optimal decomposition of synergistic identity core is to validate the method with prior biological knowledge. Here, we first aim to collect the information on cell type-specific gene regulatory networks, especially those around TFs that constitute synergistic identity cores. Then, we investigate how different types of genetic interactions (e.g., transcriptional, protein-protein) give rise to different patterns of transcriptional synergy. Finally, we devise an algorithm for decomposing synergistic identity cores, such that genetic interactions relevant to cell identity and cell conversion efficiency are optimally captured. The algorithm will be incorporated into our existing computational pipeline, which will be made publicaly available to experimental biologists across the world.
Required skills The project requires a fluent programming skill in R or related languages, and good understanding of both basic biology and mathematics. In addition, familiarity with single-cell RNA-seq data processing is a plus.
Notes The student will be expected to spend an extended period of time at the University of Luxembourg and will receive a stipend to cover the related travel and local expenses.
References
[1] Okawa S, Saltó C, et al. “Transcriptional synergy as an emergent property defining cell subpopulation identity enables population shift.” Nature communications 9, 2595, 2018.
[2] Okawa S and del Sol A. “A general computational approach to predicting synergistic transcriptional cores that determine cell subpopulation identities”. Nucleic Acids Research. https://doi.org/10.1093/nar/gkz147
Deadline 26/11/2020
PROPONI LA TUA CANDIDATURA