IDESS: Identification and Design in Stochastic Genetic Regulatory Networks
Authors/Creators
- 1. Computational Biology Lab, MBG-CSIC
- 2. Universidade de Vigo
- 3. Universidade da Coruña
- 4. Instistute for Integrative Systems Biology (I2SysBio)
Description
IDESS: Identification and Design in Stochastic Genetic Regulatory Networks
Carlos Sequeiros1, Manuel Pájaro4, Carlos Vázquez2, Julio R. Banga1 and Irene Otero-Muras3
1Computational Biology Lab
MBG-CSIC (Spanish National Research Council)
Pontevedra, 36143, Spain.
2Department of Mathematics and CITIC
Universidade da Coruña
A Coruña, 15071, Spain
3Institute for integrative systems biology (I2SysBio)
CSIC-Universitat de València
Paterna, València 46980, Spain.
4Department of Mathematics
Universidade de Vigo
Rúa Canella da Costa da Vela, 12, Ourense, 32004, Spain
Code developed by C. Sequeiros, cxsf299793000ms@gmail.com
Contact: j.r.banga@csic.es, ireneotero@iim.csic.es, carlos.vazquez.cendon@udc.es
In order to use the toolbox, you will need a Matlab installation under Windows, and a PC with a CUDA compatible GPU and a compatible C++ compiler.
Requirements:
- Matlab version R2019b or later (tested with version R2019b using a 64-bit Windows 10 Professional operating system)
- Matlab toolboxes: Optimization Toolbox, Parallel Computing Toolbox
- MEIGO optimization toolbox (https://github.com/gingproc-IIM-CSIC/MEIGO64)
- CUDA version 10.1 or later
- Microsoft Visual Studio 2019 or later
Installation:
- install Matlab and the recommended toolboxes, and make sure they can be executed normally
- decompress the .ZIP in a directory of your choice
- Copy the contents of MEIGO64-master to folder IDESS_1.0
- install CUDA runtime environment (https://developer.nvidia.com/cuda-zone)
- install Microsoft Visual Studio 2019 (https://visualstudio.microsoft.com/vs/). Make sure that the workload “Desktop development with C++” is installed.
- check the included manual for further usage and execution details
Files
IDESS_1.0.zip
Files
(901.9 kB)
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md5:602f0db917fa3b2aacfd4851376f4a52
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Additional details
References
- Egea, J. A. et al. (2014). MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics. BMC Bioinformatics, 15(136).
- Gillespie, D. T. (1976). A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. Journal of Computational Physics, 22(4), 403–434.
- Pájaro, M. et al. (2017). Stochastic modeling and numerical simulation of gene regulatory networks with protein bursting. Journal Theoretical Biology, 421, 51–70.
- Pájaro, M. et al. (2018). SELANSI: a toolbox for simulation of stochastic gene regulatory networks. Bioinformatics, 34(5), 893–895.