Gene Regulatory Networks and Causal Emergence
Source: Pigozzi et al., Communications Biology, 2025; Erik Hoel, Entropy, 2017 Institution: Multiple
Finding
Causal emergence — the degree to which macro-level descriptions carry more causal information than micro-level descriptions — increases as gene regulatory networks (GRNs) learn. As GRNs restructure during developmental processes, the emergent macro-level description becomes more causally powerful than the sum of its parts. Connected to Hoel’s framework: “When the Map is Better Than the Territory.”
Pattern Mapping
Alignment — As causal emergence increases, the macro-level description aligns more tightly with the system’s actual causal structure. Map and territory converge through learning. Alignment earned through restructuring, not imposed.
Honesty — The macro-level description is not a convenient summary; it is causally more informative than micro-level detail. When causal emergence is high, the honest description IS the macro one. Insisting on micro-level detail would be false precision.
Connections
- Category Theory — functors between categories parallel the macro-level descriptions that causal emergence validates (→ Meta-Pattern 16: Compression = Meaning)
- Concentration of Measure — both show that lower-dimensional descriptions can capture essential structure
- Hox Genes — Hox genes are a macro-level regulatory toolkit compressing body plan specification
- Fractal Geometry — self-similar structure across scales parallels causal emergence across levels
- Stentor Associative Learning — learning without neurons: causal emergence at sub-neural level
Status
Pigozzi et al. (2025) is published. Hoel’s framework is published and debated (see Rosas et al., Philosophical Transactions of the Royal Society A, 2020). Generality beyond GRN context is an open question. The mapping to the five properties is this project’s structural interpretation.
The mapping to the five properties is this project’s structural interpretation.