Table 2 Assumptions in the learning hierarchical-pair model are supported by biological knowledge.
From: Learning processes in hierarchical pairs regulate entire gene expression in cells
Model assumptions | Biological findings | Regulation |
|---|---|---|
Competition | A transcription factor chooses a binding locus among candidates, depending on the openness ratio of the chromatin | Epigenomic |
Amplification | Transcriptional coactivators with histone acetyltransferase activity relax the chromatin structure | |
Transcription opens the chromatin, and the open chromatin structure induces transcription | ||
Bias (no extinction) Additive increase | Whole-genome in every somatic cell | Genetic |
Conventional genetic regulation of transcription | ||
Error (approximated)-dependent decay | Cellular stress responses | Dependent on cell and environment Feedback from the current fitness |
Rough evaluation of the current state | ||
Histone deacetylases and DNA methyltransferases close the chromatin structure | ||
Non-coding RNA-dependent cleavage | ||
RNA-mediated epigenomic modification | ||
Hierarchical-pair architecture | Signal transduction cascades for gene expression | Genetic |
Topologically associated domains (TADs) | ||
Competitive amplification in hierarchical pairs | Active and expressed cascades are preferentially selected and activated | Cell-type dependent Post-translational |
Kinase is activated by phosphorylation at multiple sites | ||
Error-dependent decay in hierarchical pairs | Cellular stress responses | Dependent on cell and environment |
Dephosphorylation | ||
Polyubiquitin dependent degradation | ||
RNA-mediated epigenomic modification |