Chemical Reaction Network topology for generating and analyzing reaction
Scanned 9/6/2026
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---
name: crn-topology
description: Chemical Reaction Network topology for generating and analyzing reaction
version: 1.0.0
---
# CRN Topology Skill: Reaction Network Generation
**Status**: ✅ Production Ready
**Trit**: +1 (PLUS - generator)
**Color**: #D82626 (Red)
**Principle**: Network structure → Dynamical behavior
**Frame**: Hypergraph topology of chemical reactions
---
## Overview
**CRN Topology** generates and analyzes the graph structure of chemical reaction networks. The topology determines qualitative dynamics—multistability, oscillations, and computational capacity.
1. **Species-reaction graph**: Bipartite hypergraph
2. **Stoichiometric matrix**: Linear algebra of reactions
3. **Deficiency**: Gap between complexes and rank
4. **Persistence**: Network admits no extinctions
## Core Formula
```
Deficiency δ = n - ℓ - s
n = number of complexes
ℓ = number of linkage classes
s = rank of stoichiometric matrix
Zero deficiency theorem:
δ = 0 and weakly reversible ⟹ unique stable equilibrium
```
```python
def crn_deficiency(network: CRN) -> int:
n = len(network.complexes)
l = network.linkage_classes()
s = np.linalg.matrix_rank(network.stoichiometry)
return n - l - s
```
## Key Concepts
### 1. Stoichiometric Matrix Generation
```python
class CRNGenerator:
def __init__(self, species: list[str]):
self.species = species
def random_reaction(self) -> Reaction:
"""Generate topology-valid reaction."""
reactants = self.sample_complex()
products = self.sample_complex()
return Reaction(reactants, products)
def stoichiometry_matrix(self, reactions) -> np.ndarray:
"""S[i,j] = net change in species i from reaction j."""
S = np.zeros((len(self.species), len(reactions)))
for j, rxn in enumerate(reactions):
S[:, j] = rxn.products - rxn.reactants
return S
```
### 2. Network Motif Generation
```python
def generate_oscillator_topology() -> CRN:
"""Generate Brusselator-like topology."""
return CRN([
"A → X",
"2X + Y → 3X",
"B + X → Y + D",
"X → E"
])
def generate_bistable_topology() -> CRN:
"""Generate Schlögl-like bistability."""
return CRN([
"A + 2X ⇌ 3X",
"X ⇌ B"
])
```
### 3. Deficiency Analysis
```python
def analyze_topology(crn: CRN) -> dict:
"""Determine dynamical properties from topology."""
delta = crn_deficiency(crn)
wr = is_weakly_reversible(crn)
return {
"deficiency": delta,
"weakly_reversible": wr,
"unique_equilibrium": delta == 0 and wr,
"multistability_possible": delta > 0,
"complex_balanced": check_complex_balance(crn)
}
```
## Commands
```bash
# Generate CRN with target properties
just crn-generate --oscillator --species 3
# Compute deficiency
just crn-deficiency network.crn
# Visualize reaction hypergraph
just crn-topology network.crn
```
## Integration with GF(3) Triads
```
assembly-index (-1) ⊗ turing-chemputer (0) ⊗ crn-topology (+1) = 0 ✓ [Molecular Complexity]
persistent-homology (-1) ⊗ turing-chemputer (0) ⊗ crn-topology (+1) = 0 ✓ [Topological CRN]
```
## Related Skills
- **turing-chemputer** (0): Execute reactions in CRN
- **assembly-index** (-1): Validate molecular complexity
- **acsets** (0): Algebraic representation of CRN hypergraph
---
**Skill Name**: crn-topology
**Type**: Reaction Network Generator
**Trit**: +1 (PLUS)
**Color**: #D82626 (Red)
## Scientific Skill Interleaving
This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:
### Graph Theory
- **networkx** [○] via bicomodule
### Bibliography References
- `graph-theory`: 38 citations in bib.duckdb
## Cat# Integration
This skill maps to **Cat# = Comod(P)** as a bicomodule in the equipment structure:
```
Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826
```
### GF(3) Naturality
The skill participates in triads satisfying:
```
(-1) + (0) + (+1) ≡ 0 (mod 3)
```
This ensures compositional coherence in the Cat# equipment structure.Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
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