ArticleNucleic acids research2014
A bottom-up characterization of transfer functions for synthetic biology designs: lessons from enzymology.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- A stable and antibiotic-free plasmid expression system foriScience · 2025Article
- START: A Versatile Platform for Bacterial Ligand Sensing with Programmable Performances.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Synthetic Lateral Inhibition in Periodic Pattern Forming Microbial Colonies.ACS synthetic biology · 2021Article
- General Analyses of Gene Expression Dependencies on Genetic Burden.Frontiers in bioengineering and biotechnology · 2020Article
- Spatial dynamics of synthetic microbial mutualists and their parasites.PLoS computational biology · 2017Article
- Re-using biological devices: a model-aided analysis of interconnected transcriptional cascades designed from the bottom-up.Journal of biological engineering · 2017Article
- Toolbox for Exploring Modular Gene Regulation in Synthetic Biology Training.ACS synthetic biology · 2016Article
- Quorum-Sensing Synchronization of Synthetic Toggle Switches: A Design Based on Monotone Dynamical Systems Theory.PLoS computational biology · 2016Article
- Dealing with the genetic load in bacterial synthetic biology circuits: convergences with the Ohm's law.Nucleic acids research · 2016Article
- Single Cell Analysis of a Bacterial Sender-Receiver System.PloS one · 2016Article
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Authors and funding
6 authors.
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Abstract
Within the field of synthetic biology, a rational design of genetic parts should include a causal understanding of their input-output responses-the so-called transfer function-and how to tune them. However, a commonly adopted strategy is to fit data to Hill-shaped curves without considering the underlying molecular mechanisms. Here we provide a novel mathematical formalization that allows prediction of the global behavior of a synthetic device by considering the actual information from the involved biological parts. This is achieved by adopting an enzymology-like framework, where transfer functions are described in terms of their input affinity constant and maximal response. As a proof of concept, we characterize a set of Lux homoserine-lactone-inducible genetic devices with different levels of Lux receptor and signal molecule. Our model fits the experimental results and predicts the impact of the receptor's ribosome-binding site strength, as a tunable parameter that affects gene expression. The evolutionary implications are outlined.
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