bZIP transcription factors in the regulatory networks controlling seed maturation and germination of Arabidopsis thaliana

Se describe la expresión por RTqPCR de los genes que codifican los factores transcripcionales bZIP44 y bZIP9. Asimismo se establece la interacción entre ambas proteínas en el sistema de 2 híbridos de levadura y in planta por complementación bimolecular fluorescente.

Diseño y desarrollo de un modelo booleano probabilístico de regulación genética en el simulador de colonias bacterianas GRO

Resulta interesante comprender como microorganismos sencillos como la bacteria Escherichia coli poseen mecanismos no tan simples para responder al entorno en el que está gestionada por complicadas redes de regulación formadas por genes y proteínas, donde cada elemento de la red genética debe tomar parte en armonía, en el momento justo y la cantidad adecuada para dar lugar a la respuesta celular apropiada. La biología sintética es un nuevo área de la biología y la tecnología que fusiona la biolog ía molecular, la ingeniería genética y las herramientas computacionales, para crear sistemas biológicos con funcionalidades novedosas. Los sistemas creados sintéticamente son ya una realidad, y cada vez se acumulan más trabajos alrededor del mundo que muestran su factibilidad. En este campo no solo se hacen pequeñas modificaciones en la información genética, sino que también se diseñan, manipulan e introducen circuitos genéticos a los organismos. Actualmente, se hace un gran esfuerzo para construir circuitos genéticos formados por numerosos genes y caracterizar la interacción de los mismos con otras moléculas, su regulaci ón, expresión y funcionalidad en diferentes organismos. La mayoría de los proyectos de biología sintética que se han desarrollado hasta ahora, se basan en el conocimiento actual del funcionamiento de los organismos vivos. Sin embargo, la información es numerosa y creciente, por lo que se requiere de herramientas computacionales y matem áticas para integrar y hacer manejable esta gran cantidad de información. El simulador de colonias bacterianas GRO posee la capacidad de representar las dinámicas más simples del comportamiento celular, tales como crecimiento, división y comunicación intercelular mediante conjugación, pero carece de la capacidad de simular el comportamiento de la colonia en presencia de un circuito genético. Para ello, se ha creado un nuevo módulo de regulación genética que maneja las interaciones entre genes y proteínas de cada célula ejecutando respuestas celulares específicas. Dado que en la mayoría de los experimentos intervienen colonias del orden de 105 individuos, es necesario un módulo de regulación genética simplificado que permita representar de la forma más precisa posible este proceso en colonias de tales magnitudes. El módulo genético integrado en GRO se basa en una red booleana, en la que un gen puede transitar entre dos estados, on (expresado) o off (reprimido), y cuya transición viene dada por una serie de reglas lógicas.---ABSTRACT---It is interesting to understand how simple organisms such as Escherichia coli do not have simple mechanisms to respond to the environment in which they find themselves. This response is managed by complicated regulatory networks formed by genes and proteins, where each element of the genetic network should take part in harmony, at the right time and with the right amount to give rise to the appropriate cellular response. Synthetic biology is a new area of biology and technology that combines molecular biology, genetic engineering and computational tools to create biological systems with novel features. The synthetically created systems are already a reality, and increasingly accumulate work around the world showing their feasibility. In this field not only minor changes are made in the genetic information but also genetic circuits designed, manipulated and introduced into the organisms. Currently, it takes great effort to build genetic circuits formed by numerous genes and characterize their interaction with other molecules, their regulation, their expression and their function in different organisms. Most synthetic biology projects that have been developed so far are based on the current knowledge of the functioning of living organisms. However, there is a lot of information and it keeps accumulating, so it requires computational and mathematical tools to integrate and manage this wealth of information. The bacterial colonies simulator, GRO, has the ability to represent the simplest dynamics of cell behavior, such as growth, division and intercellular communication by conjugation, but lacks the ability to simulate the behavior of the colony in the presence of a genetic circuit. To this end, a new genetic regulation module that handles interactions between genes and proteins for each cell running specific cellular responses has been created. Since most experiments involve colonies of about 105 individuals, a simplified genetic module which represent cell dynamics as accurately and simply as possible is needed. The integrated genetic GRO module is based on a Boolean network, in which a gene can be in either of two states, on (expressed) or off (repressed), and whose transition is given by a set of logical rules.

An Automatic Quantification and Registration Strategy to Create a Gene Expression Atlas of Zebrafish Embryogenesis

In order to properly understand and model the gene regulatory networks in animals development, it is crucial to obtain detailed measurements, both in time and space, about their gene expression domains. In this paper, we propose a complete computational framework to fulfill this task and create a 3D Atlas of the early zebrafish embryogenesis annotated with both the cellular localizations and the level of expression of different genes at different developmental stages. The strategy to construct such an Atlas is described here with the expression pattern of 5 different genes at 6 hours of development post fertilization.

An Architecture forRepresenting Biological Processes based on Networks of Bio-inspired Processors

n this paper we propose the use of Networks of Bio-inspired Processors (NBP) to model some biological phenomena within a computational framework. In particular, we propose the use of an extension of NBP named Network Evolutionary Processors Transducers to simulate chemical transformations of substances. Within a biological process, chemical transformations of substances are basic operations in the change of the state of the cell. Previously, it has been proved that NBP are computationally complete, that is, they are able to solve NP complete problems in linear time, using massively parallel computations. In addition, we propose a multilayer architecture that will allow us to design models of biological processes related to cellular communication as well as their implications in the metabolic pathways. Subsequently, these models can be applied not only to biological-cellular instances but, possibly, also to configure instances of interactive processes in many other fields like population interactions, ecological trophic networks, in dustrial ecosystems, etc.

An Analysis of Next Generation Access Networks Deployment in rural areas

Next generation access networks (NGAN) will support a renewed electronic communication market where main opportunities lie in the provision of ubiquitous broadband connectivity, applications and content. From their deployment it is expected a wealth of innovations. Within this framework, the project reviews the variety of NGAN deployment options available for rural environments, derives a simple method for approximate cost calculations, and then discusses and compares the results obtained. Data for Spain are used for practical calculations, but the model is applicable with minor modifications to most of the rural areas of European countries. The final part of the paper is devoted to review the techno-economic implications of a network deployment in a rural environment as well as the adequacy and possible developments of the regulatory framework involved

On the Biological Plausibility of Artificial Metaplasticity

The training algorithm studied in this paper is inspired by the biological metaplasticity property of neurons. Tested on different multidisciplinary applications, it achieves a more efficient training and improves Artificial Neural Network Performance. The algorithm has been recently proposed for Artificial Neural Networks in general, although for the purpose of discussing its biological plausibility, a Multilayer Perceptron has been used. During the training phase, the artificial metaplasticity multilayer perceptron could be considered a new probabilistic version of the presynaptic rule, as during the training phase the algorithm assigns higher values for updating the weights in the less probable activations than in the ones with higher probability

Ensemble transcript interaction networks: A case study on Alzheimer's disease

Systems biology techniques are a topic of recent interest within the neurological field. Computational intelligence (CI) addresses this holistic perspective by means of consensus or ensemble techniques ultimately capable of uncovering new and relevant findings. In this paper, we propose the application of a CI approach based on ensemble Bayesian network classifiers and multivariate feature subset selection to induce probabilistic dependences that could match or unveil biological relationships. The research focuses on the analysis of high-throughput Alzheimer's disease (AD) transcript profiling. The analysis is conducted from two perspectives. First, we compare the expression profiles of hippocampus subregion entorhinal cortex (EC) samples of AD patients and controls. Second, we use the ensemble approach to study four types of samples: EC and dentate gyrus (DG) samples from both patients and controls. Results disclose transcript interaction networks with remarkable structures and genes not directly related to AD by previous studies. The ensemble is able to identify a variety of transcripts that play key roles in other neurological pathologies. Classical statistical assessment by means of non-parametric tests confirms the relevance of the majority of the transcripts. The ensemble approach pinpoints key metabolic mechanisms that could lead to new findings in the pathogenesis and development of AD

Polynomial approximation using particle swarm optimization of lineal enhanced neural networks with no hidden layers.

This paper presents some ideas about a new neural network architecture that can be compared to a Taylor analysis when dealing with patterns. Such architecture is based on lineal activation functions with an axo-axonic architecture. A biological axo-axonic connection between two neurons is defined as the weight in a connection in given by the output of another third neuron. This idea can be implemented in the so called Enhanced Neural Networks in which two Multilayer Perceptrons are used; the first one will output the weights that the second MLP uses to computed the desired output. This kind of neural network has universal approximation properties even with lineal activation functions. There exists a clear difference between cooperative and competitive strategies. The former ones are based on the swarm colonies, in which all individuals share its knowledge about the goal in order to pass such information to other individuals to get optimum solution. The latter ones are based on genetic models, that is, individuals can die and new individuals are created combining information of alive one; or are based on molecular/celular behaviour passing information from one structure to another. A swarm-based model is applied to obtain the Neural Network, training the net with a Particle Swarm algorithm.

Assortative and modular networks are shaped by adaptive synchronization processes

Modular organization and degree-degree correlations are ubiquitous in the connectivity structure of biological, technological, and social interacting systems. So far most studies have concentrated on unveiling both features in real world networks, but a model that succeeds in generating them simultaneously is needed. We consider a network of interacting phase oscillators, and an adaptation mechanism for the coupling that promotes the connection strengths between those elements that are dynamically correlated. We show that, under these circumstances, the dynamical organization of the oscillators shapes the topology of the graph in such a way that modularity and assortativity features emerge spontaneously and simultaneously. In turn, we prove that such an emergent structure is associated with an asymptotic arrangement of the collective dynamical state of the network into cluster synchronization.

Automatic blood glucose classification for gestational diabetes with feature selection: decision trees vs neural networks

Automatic blood glucose classification may help specialists to provide a better interpretation of blood glucose data, downloaded directly from patients glucose meter and will contribute in the development of decision support systems for gestational diabetes. This paper presents an automatic blood glucose classifier for gestational diabetes that compares 6 different feature selection methods for two machine learning algorithms: neural networks and decision trees. Three searching algorithms, Greedy, Best First and Genetic, were combined with two different evaluators, CSF and Wrapper, for the feature selection. The study has been made with 6080 blood glucose measurements from 25 patients. Decision trees with a feature set selected with the Wrapper evaluator and the Best first search algorithm obtained the best accuracy: 95.92%.

AALUMO: a user model ontology for ambient assisted living services supported in next-generation networks

Ambient Assisted Living (AAL) services are emerging as context-awareness solutions to support elderly people?s autonomy. The context-aware paradigm makes applications more user-adaptive. In this way, context and user models expressed in ontologies are employed by applications to describe user and environment characteristics. The rapid advance of technology allows creating context server to relieve applications of context reasoning techniques. Specifically, the Next Generation Networks (NGN) provides by means of the presence service a framework to manage the current user's state as well as the user's profile information extracted from Internet and mobile context. This paper propose a user modeling ontology for AAL services which can be deployed in a NGN environment with the aim at adapting their functionalities to the elderly's context information and state.

Rule Representation in Distributed Environments with Accepting Networks of Splicing Processors.

This paper presents the model named Accepting Networks of Evolutionary Processors as NP-problem solver inspired in the biological DNA operations. A processor has a rules set, splicing rules in this model,an object multiset and a filters set. Rules can be applied in parallel since there exists a large number of copies of objects in the multiset. Processors can form a graph in order to solve a given problem. This paper shows the network configuration in order to solve the SAT problem using linear resources and time. A rule representation arquitecture in distributed environments can be easily implemented using these networks of processors, such as decision support systems, as shown in the paper.

Simulating Metabolic Processes Using an Architecture Based on Networks of Bio-inspired Processors.

In this work, we propose the Networks of Evolutionary Processors (NEP) [2] as a computational model to solve problems related with biological phenomena. In our first approximation, we simulate biological processes related with cellular signaling and their implications in the metabolism, by using an architecture based on NEP (NEP architecture) and their specializations: Networks of Polarized Evolutionary Processors (NPEP) [1] and NEP Transducers (NEPT) [3]. In particular, we use this architecture to simulate the interplay between cellular processes related with the metabolism as the Krebs cycle and the malate-aspartate shuttle pathway (MAS) both being altered by signaling by calcium.