npx claudepluginhub plurigrid/asi --plugin asiThis skill uses the workspace's default tool permissions.
**Status**: ✅ Production Ready
Models gene regulatory networks in molecular biology as signed graphs in CatColab, using positive (activation) and negative (inhibition) edges between genes, proteins, RNA, and metabolites.
Creates, analyzes, and visualizes graphs and networks in Python with NetworkX. Use for graph algorithms like shortest paths, centrality, clustering, community detection, and synthetic network generation.
Creates, manipulates, and analyzes complex networks and graphs in Python with NetworkX. Supports graph algorithms like centrality, shortest paths, community detection, and visualization.
Share bugs, ideas, or general feedback.
Status: ✅ Production Ready Trit: +1 (PLUS - generator) Color: #D82626 (Red) Principle: Network structure → Dynamical behavior Frame: Hypergraph topology of chemical reactions
CRN Topology generates and analyzes the graph structure of chemical reaction networks. The topology determines qualitative dynamics—multistability, oscillations, and computational capacity.
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
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
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
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"
])
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)
}
# 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
assembly-index (-1) ⊗ turing-chemputer (0) ⊗ crn-topology (+1) = 0 ✓ [Molecular Complexity]
persistent-homology (-1) ⊗ turing-chemputer (0) ⊗ crn-topology (+1) = 0 ✓ [Topological CRN]
Skill Name: crn-topology Type: Reaction Network Generator Trit: +1 (PLUS) Color: #D82626 (Red)
This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:
graph-theory: 38 citations in bib.duckdbThis 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
The skill participates in triads satisfying:
(-1) + (0) + (+1) ≡ 0 (mod 3)
This ensures compositional coherence in the Cat# equipment structure.