RBS and Promoter Strengths Determine the Cell-Growth-Dependent Protein Mass Fractions and Their Optimal Synthesis Rates.

RBS strength burden gene expression growth rate promoter strength protein synthesis mass fractions resources allocation

Journal

ACS synthetic biology
ISSN: 2161-5063
Titre abrégé: ACS Synth Biol
Pays: United States
ID NLM: 101575075

Informations de publication

Date de publication:
17 12 2021
Historique:
pubmed: 13 11 2021
medline: 1 4 2022
entrez: 12 11 2021
Statut: ppublish

Résumé

Models of gene expression considering host-circuit interactions are relevant for understanding both the strategies and associated trade-offs that cell endogenous genes have evolved and for the efficient design of heterologous protein expression systems and synthetic genetic circuits. Here, we consider a small-size model of gene expression dynamics in bacterial cells accounting for host-circuit interactions due to limited cellular resources. We define the cellular resources recruitment strength as a key functional coefficient that explains the distribution of resources among the host and the genes of interest and the relationship between the usage of resources and cell growth. This functional coefficient explicitly takes into account lab-accessible gene expression characteristics, such as promoter and ribosome binding site (RBS) strengths, capturing their interplay with the growth-dependent flux of available free cell resources. Despite its simplicity, the model captures the differential role of promoter and RBS strengths in the distribution of protein mass fractions as a function of growth rate and the optimal protein synthesis rate with remarkable fit to the experimental data from the literature for

Identifiants

pubmed: 34767708
doi: 10.1021/acssynbio.1c00131
pmc: PMC8689641
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

3290-3303

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Auteurs

Fernando N Santos-Navarro (FN)

Synthetic Biology and Biosystems Control Lab, Institut d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain.

Alejandro Vignoni (A)

Synthetic Biology and Biosystems Control Lab, Institut d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain.

Yadira Boada (Y)

Synthetic Biology and Biosystems Control Lab, Institut d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain.

Jesús Picó (J)

Synthetic Biology and Biosystems Control Lab, Institut d'Automàtica i Informàtica Industrial, Universitat Politècnica de València, Camí de Vera S/N, 46022 Valencia, Spain.

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Classifications MeSH