Binary Channel Framework for Aging Gene Regulatory Networks

Integrating regulatory links and expression data to quantify information loss during aging and predict optimal knock-in restoration strategies

200
Genes
709
Regulatory Edges
67.2%
MI Loss with Aging
49.56
Young Total MI (bits)
16.24
Old Total MI (bits)
Gene 9
Best Knock-in Target

MI Distribution: Young vs Old

Aging Information Loss

Knock-in Predictions (Delta MI)

Validation: Predicted vs Observed

Network Properties

PropertyValue
Genes200
Regulatory edges709
Mean degree3.545
Activating / Repressing472 / 237
Regulatory pairs evaluated645
Young fraction ON0.534
Old fraction ON0.495

Knock-in Candidates

GeneDelta I (bits)DownstreamOld MI
Gene 9+0.0981316.37
Gene 16+0.0221416.12
Gene 5-0.0401716.51
Gene 3-0.0721016.51
Gene 0-0.0951916.47
Gene 15-0.1672016.28
Gene 18-0.2102116.44
Gene 6-0.2461016.57
Gene 7-0.2761516.51
Gene 1-0.3381216.41

Validation Summary

MetricValue
Pearson Correlation0.465
RMSE0.364
Top-3 Overlap2 of 3
Candidates Evaluated10