Journal of Biological EngineeringSynthetic BiologyPeer-reviewed journal article
Growth control as a central regulator for tuning the cellular context
Ángeles Hueso‐GiliD, Jesús Miró-BuenoiD, Ángel Goñi‐MorenoiD
Abstract
The cellular context interacts with genetic circuits, decisively defining their performance. However, contextual dependencies (the interplay between the host and the circuit) are often difficult to engineer rationally, leading to a lack of control over circuit behaviour. To address this challenge, we replaced the native regulatory machinery of the RNA polymerase (RNAP), the cells core protein-making machine, from the bacteria Pseudomonas putida KT2440 with an inducible system, enabling tunable growth regulation and thereby gaining systemic control over the entire cellular machinery. Specifically, this was achieved by placing key components, the {beta} and {beta} subunits of the enzyme, under the control of the XylS-Pm inducible system. By using its cognate chemical inducer, 3-methylbenzoate, cells growth can be controlled at will, enabling the precise tuning of the cellular context into distinct, stable states. We correlated genetic circuit behaviour with the cells growth state by observing the constitutive expression of a reporter gene and the performance of a collection of genetic NOT logic gates. Our results show that the modulation of contextual dependencies is specific to the circuit components but not a random process. A mathematical model allowed us to classify that modulation into three different categories for our library of NOT gates, based on the prediction of how RNAP availability shapes host-circuit interaction. Finally, using growth control as an input for a 2-input circuit lead to a NAND gate with the potential for morphological computing, where the cells physical body itself undertakes part of the information processing. Our findings indicate that growth control can be used as an engineering parameter, allowing us to search for optimal scenarios that enhance the potential of genetic tools.
- organism: bacteria (other)
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- research focus: drug response / drug discovery
- research focus: benchmarking
- approach: experimental assay
- Citations
- 2
- FWCI
- 4.16
- Field percentile
- top 6%
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