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Inference of Gene Regulatory Networks With Cellular Genetic Algorithm: A Multi-objective Approach

MONET is a software project aimed at solving the precise inference of Gene Regulatory Networks (GRNs) by using multi-objective metaheuristics. It is based on the jMetal multi-objective framework, which is extended with an new multi-objective approach for GRNs, and encoding of solutions to tune parameters in S-System model.

Currently it contains an implementation of the MOCell algorithm configured with a SBX crossover, polynomial mutation operators, and two objectives to optimize: MSE and Topology Regularization (TR). Additional classes for S-System modeling and time-series management are included in the util sub-folder of problem contents. This folder contains a series of classes adapted and integrated from the additional software package provided in (Sirbu et al., 2010)

Summary of features

MONET containts the following features:

The algorithm is instantiated through a MOCEllRunner class, although other runners can be employed to use other well-known multi-objective algorithms: NSGAII and SPEA2.
The included datasets in "resources" folder are: Noisy time-series (Sirbu et al., 2010) in files "SS5GeneratedData(0-10)Noise.txt" and DREAM3 Challenge for GRNs.
The "GeneNetWeaver" folder contains classess to covert solutions in variable files (VAR.tsv) to graphs representing the inferred networks.
more features ....

Requirements

To use jMetalMSA the following software packages are required:

Downloading and compiling

To download MONET just clone the Git repository hosted in GitHub:

git clone https://github.com/jMetal/monet.git

Once cloned, you can compile the software and generate a jar file with the following command:

mvn package

This sentence will generate a directory called target which will contain a file called monet-1.0-SNAPSHOT-jar-with-dependencies.jar

Runing MOCell

To execute the algorithm to solve a problem in DREAM3, for example Ecoli1 size-10, just run this command:

java -cp target/monet-1.0-SNAPSHOT-jar-with-dependencies.jar  org.uma.jmetal.runner.multiobjective.MOCellRunnerGRN /datasets-gnw/DREAM3/InSilicoSize10/InSilicoSize10-Ecoli1-trajectories.txt
```

The output of the program are two files:
* `FUN.tsv`: contains the Pareto front approximation. For each solution, this file contains a line with the values of the two objectives: MSE TR.
* `VAR.tsv`: contains the Pareto set approximation. Each solution (in each line) is represented in S-System parameter's format: Kynetic orders(gene1), Rate constansts (gene1), Kynetic orders(gene2), Rate constansts (gene2), ...

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