SynNetGen

MATLAB toolbox for creating random dynamical models, Boolean networks, and graphs. The toolbox is highly extensible such that additional methods can easily be added to provide additional ways of generating dynamical models from Boolean networks, as well as additional ways to transform all three types of models.

Installation

Requirements

Package	Tested version	Required for
MATLAB	2014a	Core functionality
MATLAB-SimBiology Toolbox	2014a	Export models to SBML
MATLAB-Symbolic Math Toolbox	2014a	Test equality of ODE models
GraphViz	2.36.0	Plotting
libSBML-MATLAB	5.11.4	SBML import/export

Setup

1. Installed required software

2. Clone repository

   git clone https://github.com/jonrkarr/SynNetGen.git

3. Change to /path/to/SynNetGen

4. Open MALTAB

5. Run installer

   install();

Getting started

example.m illustrates how to use this package:

1. Create a random unsigned, undirected graph
2. Randomize the directions and signs to create a random signed, directed graph
3. Convert the graph to a random Boolean network
4. Convert Boolean network to dynamical ODE models
5. Simulate ODE model
6. Calculate steady state of ODE model
7. Print and plot the graphs, network, and ODE models
8. Export the graphs, network, and ODE models

Documentation

Overview

The package provides three model classes: Graph, BoolNet, and Odes to represent graphs, Boolean networks, and orindary differential (ODE) models. Each class provides methods for:

• setNodesAndEdges, setNodesAndRules, setNodesAndDifferentials: Adding and setting node and edges/rules/differentials
• generate: Generating random models
• transform: Transforming models (e.g. randomize edge signs of a graph)
• convert: Converting among all three types of models
• simulate: Simulating the model
• display, print, plot: Displaying, printing, plotting
• import, export: Importing, exporting

See the API docs for more information about each function.

Generators

Model type	ID	Description	Parameters
Graph	barabasi-albert	Barabasi-Albert	n, m
Graph	bollobas-pairing-model	Bollobas Pairing Model	n, k
Graph	edgar-gilbert	Edgar-Gilbert	n, p
Graph	erdos-reyni	Erdos-Reyni	n, m
Graph	qatts-strogatz	Watts-Strogatz	n, p, k

Transforms

Model type	ID	Transform
Graph	RandomizeDirections	Randomize directions
Graph	RandomizeSigns	Randomize signs
Graph	RemoveDirections	Remove directions
Graph	RemoveSigns	Remove signs

Converters

Model type	ID	Output
Graph	boolnet	BoolNet
BoolNet	graph	Graph
BoolNet	grn-ode	ODE – Gene regulatory network without proteins
BoolNet	grn-protein-ode	ODE – Gene regulatory network with proteins
BoolNet	simbiology	SimBiology model
Odes	graph	Graph
Odes	simbiology	SimBiology model

Gene regulatory network without proteins (grn-ode)

The grn-ode converter interprets each Boolean network node as a gene and creates dynamical models of transcription similar to SynTReN¹. grn-ode creates ODE models with variables r_i which represent the instantaneous concentration of the RNA species of each gene. The differential changes in mRNA concentrations are given by

where V^r_i is maximum transcription rate of gene i, τ^r_i is the half-life of RNA i, and f_i(r) is a Michaelis-Menten-like function with 2^K_i-1 rate constants V_i,k where K_i is the number of regulators of gene i.

Gene regulatory network with proteins (grn-protein-ode)

The grn-protein-ode converter interprets each Boolean network node as a gene and creates dynamical models of transcription and translation similar to GeneNetWeaver². grn-protein-ode creates ODE models with variables r_i which represent the instantaneous concentration of the RNA species of each gene and p_i which represent the instantaneous concentration of the protein species of each gene. The differential changes in mRNA and protein concentrations are given by

where V^r_i is maximum transcription rate of gene i, V^p_i is maximum translation rate of gene i, τ^r_i is the half-life of RNA i, and τ^p_i is the half-life of protein i, and f_i(p) is a Michaelis-Menten-like function with 2^K_i-1 rate constants V_i,k where K_i is the number of regulators of gene i.

File formats

Format	Extension	ID	Graph	BoolNet	ODEs	Import	Export
Dot	dot	dot	✓				✓
GML	gml	gml	✓				✓
GraphML	xml	graphml	✓				✓
MATLAB	m	matlab		✓	✓		✓
R BoolNet	bn	r-boolnet		✓		✓	✓
SBML	xml	sbml		✓	✓	✓	✓
TGF	tgf	tgf	✓			✓	✓

After the package is installed example files will be located in doc/example.

Note: Boolean networks exported to SBML have "NOT" operators replaced with "-" because SimBiology doesn't support NOT.

API docs

After installing the toolbox, open /path/to/SynNetGen/doc/m2html/index.html in your web browser to view the API docs.

About SynNetGen

Development team

SynNetGen was developed at the Icahn School of Medicine at Mount Sinai by:

• Jonathan Karr
• Rui Chang

Third party software included in release

• graphviz4matlab: revision 8cbc3eaa757b2fdcb10fc10af87ee7bd8ea1d6f2
• M2HTML: version 1.5
• RBN Toolbox: version 2.0

License

SynNetGen is licensed under The MIT License. See license for further information.

Questions? Comments?

Please contact the development team.

References

1. Van den Bulcke T, Van Leemput K, Naudts B, van Remortel P, Ma H, Verschoren A, De Moor B, Marchal K. SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. BMC Bioinformatics 2006, 7:43. PubMed
2. Schaffter T, Marbach D, Floreano D. GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics 2011, 27(16):2263-70. PubMed