From time-series transcriptomics to gene regulatory networks: A review on inference methods

Area of Science:

• Computational Biology
• Systems Biology
• Bioinformatics

Background:

• Gene regulatory networks (GRNs) are crucial for understanding biological systems.
• Inference of GRNs is a complex, long-standing research problem.
• Sophisticated algorithms have been developed, but demand for better models persists.

Purpose of the Study:

• To review inference algorithms for gene regulatory networks using time-series transcriptomics data.
• To provide an overview of GRN applications in computational biology.
• To guide researchers in selecting appropriate GRN inference tools.

Main Methods:

• Focus on inference algorithms specifically designed for time-series transcriptomics data.
• Categorization of algorithms based on underlying assumptions and approaches.
• Discussion of key applications within computational biology.

Main Results:

• Comprehensive summary of various GRN inference methods.
• Overview of the utility of GRNs in deciphering biological processes.
• Identification of factors influencing the choice of inference tools.

Conclusions:

• Gene regulatory network inference is vital for systems biology.
• Time-series transcriptomics offers rich data for network reconstruction.
• Selecting the right inference method is critical for successful biological discovery.