Lena Straßer
Data Science in Systems Biology
A joint framework integrating microRNA and transcription factor regulation analysis at bulk and single-cell level
Although all cells of the human body share the same DNA, gene regulation drives the development towards at least 200 different cell types. Understanding gene regulation mechanisms across various molecular levels is pivotal in unraveling the complexities of cellular identity and function. Current computational approaches often overlook the complementary effects of transcription factors (TFs), micro RNAs (miRNAs), and competing endogenous RNAs (ceRNAs) in gene regulation.
This project addresses this gap by developing data science methods for dynamically integrating various regulatory levels. Firstly, novel techniques will be employed to infer single-sample gene regulatory networks (GRNs) incorporating miRNA regulation to detect sample-specific dysregulation. Subsequently, analysis at single-cell resolution will explore ceRNA-miRNA interactions, leveraging ceRNA modules inferred from existing networks to uncover the dynamics of miRNA-gene regulation. Finally, advanced ceRNA-miRNA networks will be integrated with TF regulatory networks, yielding comprehensive context-specific gene regulation maps.
These integrated networks will be offered as a resource to the scientific community, facilitating deeper insights into cellular regulatory processes.
