How are reaction networks represented in this skill?

Networks are represented as bipartite hypergraphs and stoichiometric matrices, where species net changes are mapped against specific reaction events.

Can this skill generate specific chemical motifs?

Yes, it includes templates for generating Brusselator-like oscillators, Schlögl-like bistable systems, and other common network structures.

Does it support visualization?

Yes, the skill provides commands to visualize reaction hypergraphs to better understand the connectivity between complexes and linkage classes.

What is CRN deficiency analysis?

It is a topological method based on the Deficiency Zero Theorem used to determine if a reaction network will reach a unique stable equilibrium regardless of kinetic parameters.

CRN Topology & Reaction Network Generator

Name: CRN Topology & Reaction Network Generator
Author: plurigrid

byplurigrid

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Data Science & ML

Generates and analyzes Chemical Reaction Network (CRN) hypergraph structures to predict dynamical behaviors like stability and oscillations.

About

CRN Topology is a specialized scientific computing skill designed for researchers and developers working in synthetic biology, systems chemistry, and metabolic modeling. It provides robust tools to generate bipartite species-reaction graphs, calculate stoichiometric matrices, and perform deficiency analysis using the Deficiency Zero Theorem to determine network persistence and equilibrium states. By mapping the hypergraph topology of reactions, users can design chemical oscillators, bistable systems, and complex computational reaction networks with mathematical certainty.

Key Features

2 GitHub stars
Compositional integration with Turing-chemputer and algebraic set frameworks
Network deficiency calculation (n - ℓ - s) for dynamical stability prediction
Bipartite hypergraph mapping of species and complex reactions
Automated stoichiometric matrix generation and linear algebra analysis
Pre-configured generation of oscillators and bistable network motifs

Use Cases

Generating mathematically valid reaction networks for molecular computing simulations
Designing synthetic biological circuits with predictable dynamical outcomes
Analyzing metabolic pathways for persistence and potential multistability

About

Key Features

2 GitHub stars
Compositional integration with Turing-chemputer and algebraic set frameworks
Network deficiency calculation (n - ℓ - s) for dynamical stability prediction
Bipartite hypergraph mapping of species and complex reactions
Automated stoichiometric matrix generation and linear algebra analysis
Pre-configured generation of oscillators and bistable network motifs

Use Cases

Generating mathematically valid reaction networks for molecular computing simulations
Designing synthetic biological circuits with predictable dynamical outcomes
Analyzing metabolic pathways for persistence and potential multistability