Probabilistic reasoning with an enzyme-driven DNA device

Sainz de Murieta Fuentes, Iñaki and Rodríguez-Patón Aradas, Alfonso (2013). Probabilistic reasoning with an enzyme-driven DNA device. In: "DNA 19", 22-27 Sep 2013, Tempe, Arizona, Estados Unidos. ISBN 978-3-319-01927-7. pp. 160-173.

Description

Title: Probabilistic reasoning with an enzyme-driven DNA device
Author/s:
  • Sainz de Murieta Fuentes, Iñaki
  • Rodríguez-Patón Aradas, Alfonso
Item Type: Presentation at Congress or Conference (Article)
Event Title: DNA 19
Event Dates: 22-27 Sep 2013
Event Location: Tempe, Arizona, Estados Unidos
Title of Book: Probabilistic Reasoning with an Enzyme-Driven DNA Device
Date: 2013
ISBN: 978-3-319-01927-7
Volume: 8141
Subjects:
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

We present a biomolecular probabilistic model driven by the action of a DNA toolbox made of a set of DNA templates and enzymes that is able to perform Bayesian inference. The model will take single-stranded DNA as input data, representing the presence or absence of a specific molecular signal (the evidence). The program logic uses different DNA templates and their relative concentration ratios to encode the prior probability of a disease and the conditional probability of a signal given the disease. When the input and program molecules interact, an enzyme-driven cascade of reactions (DNA polymerase extension, nicking and degradation) is triggered, producing a different pair of single-stranded DNA species. Once the system reaches equilibrium, the ratio between the output species will represent the application of Bayes? law: the conditional probability of the disease given the signal. In other words, a qualitative diagnosis plus a quantitative degree of belief in that diagno- sis. Thanks to the inherent amplification capability of this DNA toolbox, the resulting system will be able to to scale up (with longer cascades and thus more input signals) a Bayesian biosensor that we designed previously.

More information

Item ID: 26732
DC Identifier: https://oa.upm.es/26732/
OAI Identifier: oai:oa.upm.es:26732
Official URL: http://link.springer.com/book/10.1007/978-3-319-01928-4
Deposited by: Memoria Investigacion
Deposited on: 17 Jun 2014 07:31
Last Modified: 30 Nov 2017 08:53
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