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 diagnosis. 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.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-01928-4_15
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Adleman, L.M.: Molecular computation of solutions to combinatorial problems. Science 266(5187), 1021–1024 (1994)
Benenson, Y., Gil, B., Ben-Dor, U., Adar, R., Shapiro, E.: An autonomous molecular computer for logical control of gene expression. Nature 429(6990), 423–429 (2004)
Stojanovic, M.N., Stefanovic, D.: A deoxyribozyme-based molecular automaton. Nature Biotechnology 21(9), 1069–1074 (2003)
Pei, R., Matamoros, E., Liu, M., Stefanovic, D., Stojanovic, M.N.: Training a molecular automaton to play a game. Nature Nanotechnology 5(11), 773–777 (2010)
Hagiya, M., Arita, M., Kiga, D., Sakamoto, K., Yokoyama, S.: Towards Parallel Evaluation and Learning of Boolean μ-Formulas with Molecules 48, 105–114 (1997)
Yurke, B., Turberfield, A.J., Mills, A.P., Simmel, F.C., Neumann, J.L.: A DNA-fuelled molecular machine made of DNA. Nature 406(6796), 605–608 (2000)
Seelig, G., Soloveichik, D., Zhang, D.Y., Winfree, E.: Enzyme-Free Nucleic Acid Logic Circuits. Science 314(5805), 1585–1588 (2006)
Rodríguez-Patón, A., de Murieta, I.S., Sosík, P.: Autonomous resolution based on DNA strand displacement. In: Cardelli, L., Shih, W. (eds.) DNA 17. LNCS, vol. 6937, pp. 190–203. Springer, Heidelberg (2011)
Sainz de Murieta, I., Rodríguez-Patón, A.: DNA biosensors that reason. Biosystems 109(2), 91–104 (2012)
Qian, L., Winfree, E.: Scaling up digital circuit computation with DNA strand displacement cascades. Science 332(6034), 1196–1201 (2011)
Qian, L., Winfree, E., Bruck, J.: Neural network computation with DNA strand displacement cascades. Nature 475(7356), 368–372 (2011)
Soloveichik, D., Seelig, G., Winfree, E.: DNA as a universal substrate for chemical kinetics. Proceedings of the National Academy of Sciences 107(12), 5393–5398 (2010)
Rothemund, P.W.K.: Folding DNA to create nanoscale shapes and patterns. Nature 440(7082), 297–302 (2006)
Kjelstrup, S., Bedeaux, D.: Non-Equilibrium Thermodynamics of Heterogeneous Systems. Series on Advances in Statistical Mechanics. World Scientific (2008)
Benenson, Y.: Synthetic biology with RNA: progress report. Current Opinion in Chemical Biology 16(3-4), 278–284 (2012)
Weitz, M., Simmel, F.C.: Synthetic in vitro transcription circuits. Transcription 3(2), 87–91 (2012)
Amos, M.: Theoretical and Experimental DNA Computation. Natural computing series. Springer, Heidelberg (2005)
Walker, G.T., Little, M.C., Nadeau, J.G., Shank, D.D.: Isothermal in vitro amplification of DNA by a restriction enzyme/DNA polymerase system. Proceedings of the National Academy of Sciences 89(1), 392–396 (1992)
Reif, J., Majumder, U.: Isothermal reactivating whiplash PCR for locally programmable molecular computation. Natural Computing 9, 183–206 (2010)
Montagne, K., Plasson, R., Sakai, Y., Fujii, T., Rondelez, Y.: Programming an in vitro DNA oscillator using a molecular networking strategy. Molecular Systems Biology 7(1) (2011)
Padirac, A., Fujii, T., Rondelez, Y.: Bottom-up construction of in vitro switchable memories. Proceedings of the National Academy of Sciences 109(47), E3212–E3220 (2012)
Fujii, T., Rondelez, Y.: Predator-prey molecular ecosystems. ACS Nano 7(1), 27–34 (2013)
Shortliffe, E.H., Buchanan, B.G.: A model of inexact reasoning in medicine. Mathematical Biosciences 23(3-4), 351–379 (1975)
Sainz de Murieta, I., Rodríguez-Patón, A.: Probabilistic reasoning with a bayesian DNA device based on strand displacement. In: Stefanovic, D., Turberfield, A. (eds.) DNA 2012. LNCS, vol. 7433, pp. 110–122. Springer, Heidelberg (2012)
Johnson, K.A., Goody, R.S.: The original michaelis constant: Translation of the, michaelis-menten paper. Biochemistry 50(39), 8264–8269 (1913)
Minsky, M.: Steps toward artificial intelligence. Proceedings of the IRE 49(1), 8–30 (1961)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sainz de Murieta, I., Rodríguez-Patón, A. (2013). Probabilistic Reasoning with an Enzyme-Driven DNA Device. In: Soloveichik, D., Yurke, B. (eds) DNA Computing and Molecular Programming. DNA 2013. Lecture Notes in Computer Science, vol 8141. Springer, Cham. https://doi.org/10.1007/978-3-319-01928-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-01928-4_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01927-7
Online ISBN: 978-3-319-01928-4
eBook Packages: Computer ScienceComputer Science (R0)