Vol. 5 No. 1 (2025): Issue 5
Articles

Novel Genetic Circuits Design through Monte Carlo Simulation

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  • Julien Lefevre,
  • Camille Dubois,
  • Antoine Moreau
Julien Lefevre
Molecular Biology Institute of Montauban, Montauban, 82000, France
Camille Dubois
Bioengineering and Synthesis Laboratory, Université de Bourgogne Franche-Comté, Besançon, 25000, France
Antoine Moreau
Applied Systems Biology Research Group, University of La Rochelle, La Rochelle, 17000, France
DOI: https://doi.org/10.71070/oaml.v5i1.93

Published 2025-02-20

Keywords

  • Genetic Circuits,
  • Synthetic Biology,
  • Monte Carlo Simulation,
  • Probabilistic Modeling,
  • Circuit Optimization

How to Cite

Lefevre, J., Dubois, C., & Moreau, A. (2025). Novel Genetic Circuits Design through Monte Carlo Simulation. Optimizations in Applied Machine Learning, 5(1). https://doi.org/10.71070/oaml.v5i1.93
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Novel genetic circuits design is crucial for advancing synthetic biology applications. Currently, the design of genetic circuits faces challenges in achieving optimal functionality and efficiency due to the complexity of biological systems. This paper addresses the limitations in existing research by proposing a novel approach using Monte Carlo simulation. By utilizing Monte Carlo simulation, this study offers a new perspective on genetic circuits design, allowing for the exploration of a wider design space and the identification of more robust and efficient circuit configurations. The innovative aspect of this work lies in its integration of probabilistic modeling to optimize genetic circuits performance, paving the way for the development of more advanced and reliable synthetic biological systems.
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