IDESS: a toolbox for identification and automated design of stochastic gene circuits
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IDESS: a toolbox for identification and automated design of stochastic gene circuitsAutor(es)
Data
2023-11Cita bibliográfica
Carlos Sequeiros, Manuel Pájaro, Carlos Vázquez, Julio R Banga, Irene Otero-Muras, IDESS: a toolbox for identification and automated design of stochastic gene circuits, Bioinformatics, Volume 39, Issue 11, November 2023, btad682, https://doi.org/10.1093/bioinformatics/btad682
Resumo
[Abstract]: Motivation
One of the main causes hampering predictability during the model identification and automated design of gene circuits in synthetic biology is the effect of molecular noise. Stochasticity may significantly impact the dynamics and function of gene circuits, specially in bacteria and yeast due to low mRNA copy numbers. Standard stochastic simulation methods are too computationally costly in realistic scenarios to be applied to optimization-based design or parameter estimation.
Results
In this work, we present IDESS (Identification and automated Design of Stochastic gene circuitS), a software toolbox for optimization-based design and model identification of gene regulatory circuits in the stochastic regime. This software incorporates an efficient approximation of the Chemical Master Equation as well as a stochastic simulation algorithm—both with GPU and CPU implementations—combined with global optimization algorithms capable of solving Mixed Integer Nonlinear Programming problems. The toolbox efficiently addresses two types of problems relevant in systems and synthetic biology: the automated design of stochastic synthetic gene circuits, and the parameter estimation for model identification of stochastic gene regulatory networks.
Availability and implementation
IDESS runs under the MATLAB environment and it is available under GPLv3 license at https://doi.org/10.5281/zenodo.7788692.
Palabras chave
Bioinformatics
Computational Biology
IDESS
Automated design
Stochastic synthetic gene circuits
Simulation methods
Computational Biology
IDESS
Automated design
Stochastic synthetic gene circuits
Simulation methods
Versión do editor
Dereitos
Atribución 4.0 Internacional
ISSN
1367-4811