ArticleACS synthetic biology2023
Automated Design of Synthetic Gene Circuits in the Presence of Molecular Noise.
Article in ACS synthetic biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Review
- EMBED: Ensemble MATCONT-Based Detection and Classification of Bifurcations in Dual Phosphorylation-Dephosphorylation Reaction Networks.ACS omega · 2026Article
- Dynamics of genetic circuits in Pseudomonas protegens.Cell systems · 2026Article
- Generative design of synthetic gene circuits for functional and evolutionary properties.NPJ systems biology and applications · 2026Article
- Advances and Challenges in Mycobacterial Genetic Engineering: Techniques for Knockout, Knockdown and Overexpression.Journal of microbiology and biotechnology · 2025Review
- A tool for modeling gene regulatory networks (GRN_modeler) and its applications to synthetic biology.Molecular systems biology · 2025Article
- Automatic design of gene regulatory mechanisms for spatial pattern formation.NPJ systems biology and applications · 2024Article
- IDESS: a toolbox for identification and automated design of stochastic gene circuits.Bioinformatics (Oxford, England) · 2023Article
- The Landscape of Synthetic Biology and Bioengineering in Spain.Engineering biologyReview
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4 authors.
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Abstract
Microorganisms (mainly bacteria and yeast) are frequently used as hosts for genetic constructs in synthetic biology applications. Molecular noise might have a significant effect on the dynamics of gene regulation in microbial cells, mainly attributed to the low copy numbers of mRNA species involved. However, the inclusion of molecular noise in the automated design of biocircuits is not a common practice due to the computational burden linked to the chemical master equation describing the dynamics of stochastic gene regulatory circuits. Here, we address the automated design of synthetic gene circuits under the effect of molecular noise combining a mixed integer nonlinear global optimization method with a partial integro-differential equation model describing the evolution of stochastic gene regulatory systems that approximates very efficiently the chemical master equation. We demonstrate the performance of the proposed methodology through a number of examples of relevance in synthetic biology, including different bimodal stochastic gene switches, robust stochastic oscillators, and circuits capable of achieving biochemical adaptation under noise.
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