GeneNetWeaver (GNW)

Category Cross-Omics>Pathway Analysis/Gene Regulatory Networks/Tools

Abstract GeneNetWeaver (GNW) is a tool for the automatic generation of in silico gene networks and reverse engineering benchmarks.

Note: With the words “in silico” the manufacturer means ‘in simulation’, as opposed to in vivo, which would be in the real biological cell.

GNW was used to generate the DREAM3 and DREAM 4 In Silico Challenges (see below...), which are currently one of the most widely used ‘gene network’ reverse engineering benchmarks in the community.

Biomimetic Reverse Engineering of Gene Regulatory Networks (GRNs)--

Gene regulatory networks perform fundamental information processing and control mechanisms that are essential for the survival and correct functioning of biological cells.

Reverse engineering and modeling gene networks are a necessary first step in understanding cells at a system level and is expected to have substantial impact on the pharmaceutical and biotech industries in the next decades.

Technologies to assay ‘gene expression’ levels and protein concentrations are currently advancing at a fast pace, opening the door for reverse engineering of gene networks (see Reverse Engineering below...) using biologically plausible, ‘nonlinear models’ where traditional methods often fail.

The manufacturer’s approach is based on a ‘genetic algorithm’ that employs a novel biomimetic representation called Analog Genetic Encoding (AGE) - (see below...), which can be used with nonlinear gene models where analytical approaches or local (gradient-based) optimization methods are Not appropriate.

Analog Genetic Encoding allows simultaneous inference of network structure (size, topology) and numerical parameter values. It differs from other state-of-the-art genetic algorithms and global optimization methods in the sense that it mimics the encoding of natural gene regulatory networks.

The manufacturer hypothesizes that this is an effective way of incorporating prior biological knowledge in the search.

Analog Genetic Encoding (AGE) --

Analog Genetic Encoding (AGE) is a new way to represent and evolve ‘Analog Networks’ (see below...).

The genetic representation of Analog Genetic Encoding is inspired by the working of biological genetic regulatory networks (GRNs). Like genetic regulatory networks, Analog Genetic Encoding uses an implicit representation of the interaction between the devices that form the network.

This results in a genome that is compact and very tolerant of genome reorganizations, thus permitting the application of genetic operators that go beyond the simple operators of mutation and crossover that are typically used in genetic algorithms.

In particular, Analog Genetic Encoding permits the application of operators of duplication, deletion, and transpositions of fragments of the genome, which are recognized as fundamental for the evolution and complexification of biological organisms.

The resulting evolutionary system displays state-of-the-art performance in the evolutionary synthesis and reverse engineering of analog networks.

Analog Networks --

Many systems of technical and scientific interest can be seen as collections of devices connected by links characterized by a numeric value. The manufacturer calls these systems ‘analog networks’.

Examples of analog networks are analog electronic circuits -- where the devices are the electronic components that are Not resistors and the link values correspond to the conductance between the terminals of the devices;

Artificial neural networks -- where the devices are the neurons and the values correspond to the weights associated with the neuron inputs; and

Genetic regulatory networks (GRNs) -- where the devices are the genes and the link values represent the effect of one gene on the activation of another.

Reverse Engineering --

Reverse Engineering is a so-called inverse problem. The goal is to infer/estimate a model of the system from experimental data (system identification).

In the case of reverse engineering of gene regulatory networks, gene expression data from different perturbation experiments (e.g. gene knockouts, over-expression of genes) is used as input data.

The reverse engineering algorithm infers a dynamical model of the gene regulatory network under study, which can be used for computer simulation and prediction of different network responses for instance.

Generating Realistic in silico Gene Networks for Performance Assessment of Reverse Engineering Methods --

Numerous methods have been developed for inferring (reverse engineering) gene regulatory networks from expression data. However, both their absolute and comparative performance remains poorly understood.

The manufacturer has developed novel approaches for the generation of realistic in silico benchmarks and for the identification of systematic errors of network inference algorithms.

The manufacturer’s framework is available as an easy-to-use Java tool called GeneNetWeaver (GNW).

GNW is free software, released under an MIT license. The source code and additional resources are available at Sourceforge.

GeneNetWeaver (GNW) Resources:

1) GNW (extensive) user manual.

2) Sourceforge project webpage.

3) MATLAB scripts (to evaluate predictions, plot datasets...)

libSDE --

libSDE is an Java library for numerical integration of ‘Stochastic Differential Equations’ (SDEs).

It allows integration of Ito and Stratonovich SDEs using several methods (Euler-Maruyama, Euler-Heun, derivative-free Milstein, and stochastic Runge-Kutta).

GeneNetWeaver uses libSDE for the simulation of noise in the dynamics of gene networks.

DREAM in silico challenge(s) --

The DREAM (Dialogue for Reverse Engineering Assessments and Methods) initiative organizes an annual reverse engineering “competition” (The manufacturer prefers to see it as a community experiment) called the DREAM challenges.

The manufacturer uses GeneNetWeaver to provide the so-called DREAM in silico network challenge.

The goal of this challenge is to predict ‘network connectivities’ from in silico generated gene expression datasets.

The true structure of the in silico gene networks is revealed only after the submission of the predictions.

In addition to inference of the network structure, the manufacturer also encourages prediction of gene expression measurements for specific conditions that the manufacturer withholds from the training data.

System Requirements

Contact manufacturer.

Manufacturer

Manufacturer Web Site GeneNetWeaver (GNW)

Price Contact manufacturer.

G6G Abstract Number 20587

G6G Manufacturer Number 104190