SELANSI: A toolbox for simulation of stochastic gene regulatory networks
Use este enlace para citar
http://hdl.handle.net/2183/37704
A non ser que se indique outra cousa, a licenza do ítem descríbese como Attribution-NonCommercial 4.0 International (CC-BY-NC)
Coleccións
- GI-M2NICA - Artigos [74]
Metadatos
Mostrar o rexistro completo do ítemTítulo
SELANSI: A toolbox for simulation of stochastic gene regulatory networksData
2018-03Cita bibliográfica
Manuel Pájaro, Irene Otero-Muras, Carlos Vázquez, Antonio A Alonso, SELANSI: a toolbox for simulation of stochastic gene regulatory networks, Bioinformatics, Volume 34, Issue 5, March 2018, Pages 893–895, https://doi.org/10.1093/bioinformatics/btx645
Resumo
[Abstract]: Motivation Gene regulation is inherently stochastic. In many applications concerning Systems and Synthetic Biology such as the reverse engineering and the de novo design of genetic circuits, stochastic effects (yet potentially crucial) are often neglected due to the high computational cost of stochastic simulations. With advances in these fields there is an increasing need of tools providing accurate approximations of the stochastic dynamics of gene regulatory networks (GRNs) with reduced computational effort. Results This work presents SELANSI (SEmi-LAgrangian SImulation of GRNs), a software toolbox for the simulation of stochastic multidimensional gene regulatory networks. SELANSI exploits intrinsic structural properties of gene regulatory networks to accurately approximate the corresponding Chemical Master Equation with a partial integral differential equation that is solved by a semi-lagrangian method with high efficiency. Networks under consideration might involve multiple genes with self and cross regulations, in which genes can be regulated by different transcription factors. Moreover, the validity of the method is not restricted to a particular type of kinetics. The tool offers total flexibility regarding network topology, kinetics and parameterization, as well as simulation options. © The Author 2017. Published by Oxford University Press.
Palabras chave
Algorithms
Computational Biology
Computer Simulation
Gene Regulatory Networks
Kinetics
Software
Stochastic Processes
Synthetic Biology
Transcription Factors
Computational Biology
Computer Simulation
Gene Regulatory Networks
Kinetics
Software
Stochastic Processes
Synthetic Biology
Transcription Factors
Versión do editor
Dereitos
Attribution-NonCommercial 4.0 International (CC-BY-NC)
ISSN
1367-4803