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Related Experiment Video

Updated: Dec 4, 2025

Rapid Assembly of Multi-Gene Constructs using Modular Golden Gate Cloning

Rapid Assembly of Multi-Gene Constructs using Modular Golden Gate Cloning

Published on: February 5, 2021

A permutation method for network assembly.

Shawn A Means¹, Christian Bläsche¹, Carlo R Laing¹

¹School of Natural and Computational Sciences, Massey University, Auckland, New Zealand.

|October 23, 2020

Summary

This summary is machine-generated.

We developed a novel method to construct directed networks with specific in- and out-degrees, enabling the creation of weighted or binary networks. This approach facilitates comparisons with real-world networks and aids in studying network dynamics.

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Area of Science:

Network science
Graph theory
Computational mathematics

Background:

Constructing synthetic networks with specified degree sequences is crucial for network analysis.
Existing methods often struggle with directed networks and multi-edges.
Understanding network structure requires robust methods for generating comparable null models.

Purpose of the Study:

To present a flexible and efficient method for assembling directed networks with prescribed bi-degree sequences.
To enable the generation of synthetic networks with controlled multi-edge proportions.
To provide a tool for null-space comparisons and the study of network dynamics.

Main Methods:

Utilizes permutations of adjacency matrices conforming to in-degree sequences.
Employs directed edge-swapping and constrained Monte-Carlo edge-mixing to match out-degree sequences.
Allows for the inclusion or exclusion of multi-edges, supporting weighted and binary network generation.

Main Results:

Successfully constructs large-scale directed networks (O(10^7) edges) rapidly (minutes).
Provides weightings for non-uniform graph space sampling, enabling accurate statistical average computations.
Can generate simple graphs for sequences satisfying graphicality conditions.

Conclusions:

The method offers a unique approach to exploring weighted synthetic network spaces.
It is efficient, scalable, and applicable to real-world network analysis and neuronal network dynamics.
An implementation is available on GitHub for the research community.