Analysis of Gene Regulatory Networks in Kidney Tumors

הרקע לפרויקט:

Gene regulatory networks (GRNs) play a crucial role in cancer by orchestrating the complex interactions between genes that drive tumor development and progression. In cancer, these networks often become dysregulated, leading to aberrant gene expression that promotes uncontrolled cell growth, resistance to cell death, and metastasis. By mapping and understanding GRNs in cancer, researchers can identify key regulatory nodes and pathways that contribute to malignancy.

מטרת הפרויקט:

In this project we will explore the landscape of gene regulatory networks in a type of kidney cancer known as renal cell carcinoma (RCC). We will do this using publicly available single cell datasets from several different modalities. Using different statistical and machine learning methods, we will explore the different tumor and immune cell states and types in RCC. We will then infer networks using state of the art techniques.

תכולת הפרויקט:

The student in the project will learn about the different techniques of analyzing single-cell datasets, methods for analyzing complex networks, and state of the art methods for inferring GRN's from single cell data.

קורסי קדם:

• מבוא ללימדת מכונה
• ניורו-גנומיקה או ביולוגיה חישובית/ביואינפורמטיקה

דרישות נוספות:

• Basic programming in R or Python
• Introductory courses in biology is recommended but not required

מקורות:

1. Trink Y, Urbach A, Dekel B, Hohenstein P, Goldberger J, Kalisky T. Characterization of Continuous Transcriptional Heterogeneity in High-Risk Blastemal-Type Wilms' Tumors Using Unsupervised Machine Learning. Int J Mol Sci. 2023 Feb 9;24(4):3532. doi: 10.3390/ijms24043532. PMID: 36834944; PMCID: PMC9965420.
2. Kamimoto, K., Stringa, B., Hoffmann, C.M. et al. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614, 742–751 (2023). https://doi.org/10.1038/s41586-022-05688-9
3. Bravo González-Blas, C., De Winter, S., Hulselmans, G. et al. SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks. Nat Methods 20, 1355–1367 (2023). https://doi.org/10.1038/s41592-023-01938-4