Gene Expression Is Not Random: Scaling, Long-Range Cross-Dependence, and Fractal Characteristics of Gene Regulatory...

Area of Science:

• Systems Biology
• Computational Biology
• Genomics

Background:

• Gene expression is crucial for cellular function and environmental response.
• Understanding transcriptional dynamics is key to controlling cellular processes.
• Previous studies focused on DNA structure, not gene expression dynamics.

Purpose of the Study:

• To investigate the scaling properties of gene expression time series.
• To analyze individual gene expression dynamics and inter-gene dependencies.
• To explore cross-dependencies within gene regulatory networks.

Main Methods:

• Analysis of gene expression time series data.
• Investigation of fractal and long-range dependence characteristics.
• Examination of cross-correlations between genes and transcription factors in regulatory networks.

Main Results:

• Gene expression time series display fractal and long-range dependence.
• Gene regulatory network dynamics show fractal and long-range cross-correlations.
• Cross-correlation exponents in gene regulatory networks are not unique.

Conclusions:

• Gene expression dynamics exhibit complex, long-range dependent behavior.
• Gene regulatory networks display fractal cross-correlations.
• The distribution of cross-correlation exponents can measure cellular functional complexity.