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
[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:
- Fedora 25 Workstation
- PyCharm 2016.x
- Anaconda 4.1.1 with Python 3.5.2
- Boost 1.61.0
- GSL 2.2.1
- clang 3.8
Instead, the list of mandatory dependencies is:
[!] 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
source/corea 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.
master branch, usually, contains working code, tested on different environments. Check
releases to see the
latest stable version of the toolbox.
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:
masterbranch accepts only bug fixes and small corrections to the available code.
developmentbranch accepts bug fixes, corrections and the development of new features that can become part of the
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.
The generalized island model, Izzo Dario and Ruciński Marek and Biscani Francesco, Parallel Architectures and Bioinspired Algorithms, 151--169, 2012, Springer