WASABI: a dynamic iterative framework for gene regulatory network inference

Area of Science:

• Systems Biology
• Computational Biology
• Genomics

Background:

• Gene regulatory network inference is challenging in systems biology.
• Single-cell transcriptomic data offers new opportunities but has limitations for network inference.

Purpose of the Study:

• To develop an iterative algorithm, WASABI, for inferring causal dynamical networks from time-stamped single-cell data.
• To address limitations of current gene regulatory network inference approaches.

Main Methods:

• Introduced the "waves" concept: information spreads sequentially through a network.
• Developed an iterative algorithm to infer networks gene-by-gene based on regulation timing.
• Validated WASABI using in silico simulated networks and in vitro avian erythroid differentiation data.

Main Results:

• WASABI successfully inferred simulated gene regulatory networks.
• Applied to avian erythroid differentiation, WASABI revealed a distributed network structure without significant hub genes.
• Identified that most genes are directly regulated by the differentiation stimulus.

Conclusions:

• WASABI demonstrates versatility in addressing gene regulatory network inference challenges.
• The algorithm effectively utilizes time-stamped single-cell data.
• WASABI is expected to aid biologists in analyzing complex gene regulatory mechanisms.