EconPapers    
Economics at your fingertips  
 

Metabolic perceptrons for neural computing in biological systems

Amir Pandi, Mathilde Koch, Peter L. Voyvodic, Paul Soudier, Jerome Bonnet, Manish Kushwaha () and Jean-Loup Faulon ()
Additional contact information
Amir Pandi: Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay
Mathilde Koch: Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay
Peter L. Voyvodic: University of Montpellier
Paul Soudier: Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay
Jerome Bonnet: University of Montpellier
Manish Kushwaha: Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay
Jean-Loup Faulon: Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay

Nature Communications, 2019, vol. 10, issue 1, 1-13

Abstract: Abstract Synthetic biological circuits are promising tools for developing sophisticated systems for medical, industrial, and environmental applications. So far, circuit implementations commonly rely on gene expression regulation for information processing using digital logic. Here, we present a different approach for biological computation through metabolic circuits designed by computer-aided tools, implemented in both whole-cell and cell-free systems. We first combine metabolic transducers to build an analog adder, a device that sums up the concentrations of multiple input metabolites. Next, we build a weighted adder where the contributions of the different metabolites to the sum can be adjusted. Using a computational model fitted on experimental data, we finally implement two four-input perceptrons for desired binary classification of metabolite combinations by applying model-predicted weights to the metabolic perceptron. The perceptron-mediated neural computing introduced here lays the groundwork for more advanced metabolic circuits for rapid and scalable multiplex sensing.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.nature.com/articles/s41467-019-11889-0 Abstract (text/html)

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:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11889-0

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-11889-0

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-19
Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11889-0