L

lassim

Python toolbox implementing LASSIM (Large Scale Simulating Modelling), a new simulation-based integrative framework for GRN inference.

LASSIM Toolbox

About LASSIM and the LASSIM toolbox

Recent and ongoing improvements in measurements technologies have given the possibility to obtain systems wide omics data of several biological processes. However, the analysis of those data has to date been restricted to crude, statistical tools with important biological mechanisms, e.g. feedback loops, being overlooked. The omitting of such high influence details in a large scale network remains a major problem in today’s omics based environment and is a key aspect of truly understanding any complex disease. Therefore, we herein present the LASSIM (LArge Scale SImulation Modeling) toolbox for GRNs Inference, which revolves around the expansion of a well determined mechanistic ODE-model into the entire system.

With this toolbox, it is possible to run a default implementation of lassim, but also to extend and improve its behaviour by creating new optimization algorithms, using a different system of ODEs, different types of integrators, and much more.

All the optimization algorithms currently available are implemented by PyGMO but there are no limitations on how the algorithms should be implemented, what it is important is to respect the signatures of the classes that are part of the module source/core

[Important] The project is in the beta release stage. It is working and currently under use inside our team but multiple tests are still missing and the documentation is far from completed. Moreover, the general structure is still subject to important changes with a continuous refactoring of the entire codebase. If you have any feedback or you want to contribute to the project, don't esitate to contact the current developers of it.

Current Members in the Project

How to install the toolbox

Before using the the toolbox, be sure to satisfy all the requirements in the Development environment and requirements. After you have done that, run the following command from a terminal:

git clone https://gitlab.com/Gustafsson-lab/lassim.git
cd lassim
./scripts/install.sh

How to use the toolbox

For the core system optimization, the command is:

python lassim_core.py <configuration-file>

While for the peripheral genes optimization, the command is:

python lassim_peripherals.py <configuration-file>

while for the list of terminal options availables use the command:

  • python lassim_core.py -h
  • python lassim_peripherals.py -h

Development environment and requirements

The current toolbox environment for its development and testing is:

but is going to be tested and used mainly on the NSC system available at the Linköping University.

Instead, the list of mandatory dependencies is:

Except for sortedcontainers, all of them are already present in Anaconda 4.1.1.

For tips on how to install PyGMO, look at INSTALL file.

[!] clang compiler seems to be the one that gives less problems during the compilation process, but, even if not tested, there shouldn't be any issue with the gcc compiler too.

[!] Windows OS is not supported.

Python Version Supported/Tested

  • Python 3.5

What's next?

  • Making source/core a Python module installable from PyPI.
  • New kind of base implementation for the optimization process, in order to use different algorithm in different ways.
  • New formats for input data.
  • Improvements on tests, documentation and code quality.
  • A Gitter channel.

Here you can find one of the reasons why the support for Python 2.7 is highly improbable.

Current Branches

The master branch, usually, contains working code, tested on different environments. Check releases to see the latest stable version of the toolbox.

The development branch, instead, contains unstable, untested code, with future features and bug fixes, should not be used unless you want to help with the development.

How to contribute

In order to contribute to the project, it is necessary to first fork it. Then, it is important to decide on which branch to work on:

  • master branch accepts only bug fixes and small corrections to the available code.
  • development branch accepts bug fixes, corrections and the development of new features that can become part of the master branch.

In all cases, for each pull request it would be nice to have some tests related to the code submitted and an explanation of what it is submitted, for making the job easier for the repository maintainers.

References

The generalized island model, Izzo Dario and Ruciński Marek and Biscani Francesco, Parallel Architectures and Bioinspired Algorithms, 151--169, 2012, Springer