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Memorias de investigación
Communications at congresses:
Probabilistic Reasoning with an Enzyme-driven DNA Device
Year:2013
Research Areas
  • Biocomputer,
  • Information technology and adata processing
Information
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.
International
Si
Congress
19th International Conference on DNA Computing and Molecular Programming
960
Place
Arizona State University, Tempe. USA.
Reviewers
Si
ISBN/ISSN
Start Date
22/09/2013
End Date
27/09/2013
From page
To page
Participants
  • Autor: Iñaki Sainz de Murieta Fuentes (UPM)
Research Group, Departaments and Institutes related
  • Creador: Departamento: Inteligencia Artificial
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