Chromosomal location reprograms genetic circuit function
Abstract Genetic circuits confer computing abilities to living cells, performing novel transformations of input stimuli into output responses. Circuit editing often focuses on substituting DNA components, such as RBSs, regulators, or promoters, from part libraries to achieve desired performance. However, this approach is inherently limited by the availability of DNA components. Here, we show that circuit performance can be reprogrammed without altering its DNA sequence by using a library of positions: a set of physical locations within the cell’s volume. Using bacteria as the living chassis, we engineer 219 spatially unique genetic circuits of four different types—three regulatory cascades and a toggle switch—by either inserting the entire circuit in a specific chromosomal position or separating and distributing circuit modules. Their analysis, together with a mathematical model, reveals that spatial positioning can be used not only to optimize circuits but also to switch circuits between modes of operation, giving rise to new functions as circuit complexity increases. We provide foundational insights into leveraging intracellular space for circuit design.
Authors
- Jesús Miró-Bueno (ORCID: https://orcid.org/0000-0002-4543-0616)
- Lewis Grozinger (ORCID: https://orcid.org/0000-0002-9024-701X)
- Lorea Alejaldre (ORCID: https://orcid.org/0000-0003-3086-5446)
- Ángeles Hueso‐Gil (ORCID: https://orcid.org/0000-0002-9914-5589)
- Ángel Goñi‐Moreno (ORCID: https://orcid.org/0000-0002-2097-2507)
- Hüseyin Taş (ORCID: https://orcid.org/0000-0003-4804-2237)
- Sina Geißler
Institutions
- Harvard University (US)
- Centro Nacional de Biotecnología (ES)
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (ES)
- Centre for Plant Biotechnology and Genomics (ES)
- Universidad Politécnica de Madrid (ES)
Publication Details
- Journal
- Nature Communications
- Published
- 2026-11-03
- DOI
- https://doi.org/10.1038/s41467-026-78916-9
- Citations
- 3
- Primary Topic
- Gene Regulatory Network Analysis
- Type
- article
- Field-Weighted Citation Impact
- 3.43
Funders
- Comunidad de Madrid
- Agencia Estatal de Investigación