Implementation of Complex Biological Logic Circuits Using Spatially Distributed Multicellular Consortia
Javier Macia,
Romilde Manzoni,
Núria Conde,
Arturo Urrios,
Eulàlia de Nadal,
Ricard Solé and
Francesc Posas
PLOS Computational Biology, 2016, vol. 12, issue 2, 1-24
Abstract:
Engineered synthetic biological devices have been designed to perform a variety of functions from sensing molecules and bioremediation to energy production and biomedicine. Notwithstanding, a major limitation of in vivo circuit implementation is the constraint associated to the use of standard methodologies for circuit design. Thus, future success of these devices depends on obtaining circuits with scalable complexity and reusable parts. Here we show how to build complex computational devices using multicellular consortia and space as key computational elements. This spatial modular design grants scalability since its general architecture is independent of the circuit’s complexity, minimizes wiring requirements and allows component reusability with minimal genetic engineering. The potential use of this approach is demonstrated by implementation of complex logical functions with up to six inputs, thus demonstrating the scalability and flexibility of this method. The potential implications of our results are outlined.Author Summary: Synthetic biological circuits have been built for different purposes. Nevertheless, the way these devices have been designed so far present several limitations: complex genetic engineering is required to implement complex circuits, and once the parts are built, they are not reusable. We proposed to distribute the computation in several cellular consortia that are physically separated, thus ensuring implementation of circuits independently of their complexity and using reusable components with minimal genetic engineering. This approach allows an easy implementation of multicellular computing devices for secretable inputs or biosensing purposes.
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004685 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 04685&type=printable (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1004685
DOI: 10.1371/journal.pcbi.1004685
Access Statistics for this article
More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().