ArticleScientific reports2020
Algorithms for ribosome traffic engineering and their potential in improving host cells' titer and growth rate.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Designing genetically stable multicopy gene constructs with the ChimeraUGEM web server.NAR genomics and bioinformatics · 2025Article
- Negative feedback and oscillations in a model for mRNA translation.Journal of the Royal Society, Interface · 2025Article
- Specific codons control cellular resources and fitness.Science advances · 2024Article
- Scaling of nuclear numbers and their spatial arrangement in skeletal muscle cell size regulation.Molecular biology of the cell · 2023Review
- Modelling genetic stability in engineered cell populations.Nature communications · 2023Article
- On the gain of entrainment in theJournal of the Royal Society, Interface · 2023Article
- Translation in the cell under fierce competition for shared resources: a mathematical model.Journal of the Royal Society, Interface · 2022Article
- Large-scale mRNA translation and the intricate effects of competition for the finite pool of ribosomes.Journal of the Royal Society, Interface · 2022Article
- Modeling the ribosomal small subunit dynamic in Saccharomyces cerevisiae based on TCP-seq data.Nucleic acids research · 2022Article
- Accelerating Whole-Cell Simulations of mRNA Translation Using a Dedicated Hardware.ACS synthetic biology · 2021Article
- A Modelling Framework Linking Resource-Based Stochastic Translation to the Optimal Design of Synthetic Constructs.Biology · 2021Article
- Determinants of efficient modulation of ribosomal traffic jams.Computational and structural biotechnology journal · 2021Article
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4 authors.
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Abstract
mRNA translation is a fundamental cellular process consuming most of the intracellular energy; thus, it is under extensive evolutionary selection for optimization, and its efficiency can affect the host's growth rate. We describe a generic approach for improving the growth rate (fitness) of any organism by introducing synonymous mutations based on comprehensive computational models. The algorithms introduce silent mutations that may improve the allocation of ribosomes in the cells via the decreasing of their traffic jams during translation respectively. As a result, resources availability in the cell changes leading to improved growth-rate. We demonstrate experimentally the implementation of the method on Saccharomyces cerevisiae: we show that by introducing a few mutations in two computationally selected genes the mutant's titer increased. Our approach can be employed for improving the growth rate of any organism providing the existence of data for inferring models, and with the relevant genomic engineering tools; thus, it is expected to be extremely useful in biotechnology, medicine, and agriculture.
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