Propuesta de libro de texto: fundamentos de bases de datos, saber y hacer

Through the years, we have detected a problem in the academic program of Information and Communication Technologies of our University, a recurrent problem in the teaching learning process, accentuated with the associated paradigm to the construction of knowledge by the own pupil. We are specifically referring to the search and assimilation of content inside the book texts about Digital Databases. The work exposed in this paper represents an effort for contributing in the reduction of educational slump in areas related to good design and construction of data banks. The textbook of this research, treats all the thematical content in this area, which are studied in the whole academic program. These and another relevant subjects in the database area are retaken from a simple but fundamentally practical theorical focus, allowing the studying on acquiring a significative learning in an easier and single source way. As a result, we present the almost definitive version of the book which is been tested on pilot groups

Probabilistic reasoning with a bayesian DNA device based on strand displacement

We present a computing model based on the DNA strand displacement technique which performs Bayesian inference. The model will take single stranded DNA as input data, representing the presence or absence of a specific molecular signal (evidence). The program logic encodes the prior probability of a disease and the conditional probability of a signal given the disease playing with a set of different DNA complexes and their ratios. When the input and program molecules interact, they release a different pair of single stranded DNA species whose relative proportion represents the application of Bayes? Law: the conditional probability of the disease given the signal. The models presented in this paper can empower the application of probabilistic reasoning in genetic diagnosis in vitro.

Probabilistic reasoning with an enzyme-driven DNA device

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.