Getting Started

SBMLNetwork provides a concise, high‑level Python interface for loading, styling, analyzing, and exporting SBML models with full Layout and Render support.

Installation

For complete installation options, see Installation.

Example use‑cases

Basic visualization

Load a model, let SBMLNetwork auto‑layout/style it if necessary, then render and save both the figure and the updated SBML.

import sbmlnetwork as sb

# Load SBML; auto‑layout and auto‑style will run if the file lacks them
net = sb.load("my_model.xml")

# Render the network and save a PNG
net.draw("network.png")

# Save SBML back to disk with Layout/Render information embedded
net.save("network.xml")

Styling and theming

Apply a built‑in theme before drawing to instantly switch color palette and typography.

import sbmlnetwork as sb

# Load the model
net = sb.load("my_model.xml")

# Apply one of the built‑in themes (e.g. “escher”)
net.set_style("escher")

# Render with the new look
net.draw("styled_network.png")

Mapping reaction fluxes

Colour‑code reactions by experimental or simulated flux values to highlight bottlenecks or high‑throughput steps.

import sbmlnetwork as sb

# Define an Antimony model
antimony_str = """
    J0: S1 -> S2 ; k1*S1
    J1: S2 -> S3 ; k2*S2
    k1 = 1;  k2 = 0.5;
    S1 = 10; S2 = 0; S3 = 0;
"""

# Load the model from the Antimony string
net = sb.load(antimony_str)

# Flux values for each reaction
fluxes = {
    "J0": 12.3,
    "J1": 6.8,
}

# Map flux magnitude to reaction colours (log‑scaled) and add a colour bar
net.show_fluxes(fluxes, log_scale=True)

# Render the flux‑mapped network
net.draw("flux_map.png")

Note

The data argument for show_fluxes may be provided in two ways:

Single float – interpreted as a simulation time (SBMLNetwork will run your simulator at that time to compute fluxes).
Dict[str, float] – a mapping of reaction IDs to pre‑computed flux values.

See the full Data Visualization documentation for details on parameters and behavior.

Mapping species concentrations

Encode absolute or log‑scaled species abundances as node colour or size to visualise high‑ and low‑concentration pools at a glance.

import sbmlnetwork as sb

# Define an Antimony model
antimony_str = """
    J0: S1 -> S2 ; k1*S1
    J1: S2 -> S3 ; k2*S2
    k1 = 1;  k2 = 0.5;
    S1 = 10; S2 = 0; S3 = 0;
"""

# Load the model from the Antimony string
net = sb.load(antimony_str)

# Measured or simulated concentrations
conc = {
    "S1": 2.5,
    "S2": 0.8,
    "S3": 5.1,
}

# Map concentrations to node size (log‑scaled)
net.show_concentrations(conc,
                        show_by="size",
                        log_scale=True,
                        min_size=20,
                        max_size=80)

# Render the concentration‑mapped network
net.draw("concentration_map.png")

Note

The data argument for show_concentrations may be provided in two ways:

Single float – interpreted as a simulation time (SBMLNetwork will run your simulator at that time to compute concentrations).
Dict[str, float] – a mapping of species IDs to pre‑computed concentration values.

See the full Data Visualization documentation for details on parameters and behavior.

Grouping reactions

Highlight a pathway or functional module by assigning its reactions to a shared group and giving them a distinct colour.

import sbmlnetwork as sb

# Antimony model with three reactions
antimony_str = """
    J0: S1 -> S2 ; k1*S1
    J1: S2 -> S3 ; k2*S2
    J2: S2 -> S4 ; k3*S2
    k1 = 1; k2 = 0.5; k3 = 0.2;
    S1 = 10; S2 = 0; S3 = 0; S4 = 0;
"""

# Load the model
net = sb.load(antimony_str)

# Group two reactions (J0 and J1) and colour them red
net.group_reactions(["J0", "J1"], color="red")

# Render the grouped network
net.draw("grouped_pathway.png")

Next steps

Explore the full Python API reference reference in Python API reference.
See the Gallery for real‑world, fully‑annotated examples.