Aerodynamic Optimization of High-Speed Trains Nose using a Genetic Algorithm and Artificial Neural Network

An aerodynamic optimization of the train aerodynamic characteristics in term of front wind action sensitivity is carried out in this paper. In particular, a genetic algorithm (GA) is used to perform a shape optimization study of a high-speed train nose. The nose is parametrically defined via Bézier Curves, including a wider range of geometries in the design space as possible optimal solutions. Using a GA, the main disadvantage to deal with is the large number of evaluations need before finding such optimal. Here it is proposed the use of metamodels to replace Navier-Stokes solver. Among all the posibilities, Rsponse Surface Models and Artificial Neural Networks (ANN) are considered. Best results of prediction and generalization are obtained with ANN and those are applied in GA code. The paper shows the feasibility of using GA in combination with ANN for this problem, and solutions achieved are included.

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

Oscillatory genetic circuits: new designs and mathematical models

Introduction and motivation: A wide variety of organisms have developed in-ternal biomolecular clocks in order to adapt to cyclic changes of the environment. Clock operation involves genetic networks. These genetic networks have to be mod¬eled in order to understand the underlying mechanism of oscillations and to design new synthetic cellular clocks. This doctoral thesis has resulted in two contributions to the fields of genetic clocks and systems and synthetic biology, generally. The first contribution is a new genetic circuit model that exhibits an oscillatory behav¬ior through catalytic RNA molecules. The second and major contribution is a new genetic circuit model demonstrating that a repressor molecule acting on the positive feedback of a self-activating gene produces reliable oscillations. First contribution: A new model of a synthetic genetic oscillator based on a typical two-gene motif with one positive and one negative feedback loop is pre¬sented. The originality is that the repressor is a catalytic RNA molecule rather than a protein or a non-catalytic RNA molecule. This catalytic RNA is a ribozyme that acts post-transcriptionally by binding to and cleaving target mRNA molecules. This genetic clock involves just two genes, a mRNA and an activator protein, apart from the ribozyme. Parameter values that produce a circadian period in both determin¬istic and stochastic simulations have been chosen as an example of clock operation. The effects of the stochastic fluctuations are quantified by a period histogram and autocorrelation function. The conclusion is that catalytic RNA molecules can act as repressor proteins and simplify the design of genetic oscillators. Second and major contribution: It is demonstrated that a self-activating gene in conjunction with a simple negative interaction can easily produce robust matically validated. This model is comprised of two clearly distinct parts. The first is a positive feedback created by a protein that binds to the promoter of its own gene and activates the transcription. The second is a negative interaction in which a repressor molecule prevents this protein from binding to its promoter. A stochastic study shows that the system is robust to noise. A deterministic study identifies that the oscillator dynamics are mainly driven by two types of biomolecules: the protein, and the complex formed by the repressor and this protein. The main conclusion of this study is that a simple and usual negative interaction, such as degradation, se¬questration or inhibition, acting on the positive transcriptional feedback of a single gene is a sufficient condition to produce reliable oscillations. One gene is enough and the positive transcriptional feedback signal does not need to activate a second repressor gene. At the genetic level, this means that an explicit negative feedback loop is not necessary. Unlike many genetic oscillators, this model needs neither cooperative binding reactions nor the formation of protein multimers. Applications and future research directions: Recently, RNA molecules have been found to play many new catalytic roles. The first oscillatory genetic model proposed in this thesis uses ribozymes as repressor molecules. This could provide new synthetic biology design principles and a better understanding of cel¬lular clocks regulated by RNA molecules. The second genetic model proposed here involves only a repression acting on a self-activating gene and produces robust oscil¬lations. Unlike current two-gene oscillators, this model surprisingly does not require a second repressor gene. This result could help to clarify the design principles of cellular clocks and constitute a new efficient tool for engineering synthetic genetic oscillators. Possible follow-on research directions are: validate models in vivo and in vitro, research the potential of second model as a genetic memory, investigate new genetic oscillators regulated by non-coding RNAs and design a biosensor of positive feedbacks in genetic networks based on the operation of the second model Resumen Introduccion y motivacion: Una amplia variedad de organismos han desarro-llado relojes biomoleculares internos con el fin de adaptarse a los cambios ciclicos del entorno. El funcionamiento de estos relojes involucra redes geneticas. El mo delado de estas redes geneticas es esencial tanto para entender los mecanismos que producen las oscilaciones como para diseiiar nuevos circuitos sinteticos en celulas. Esta tesis doctoral ha dado lugar a dos contribuciones dentro de los campos de los circuitos geneticos en particular, y biologia de sistemas y sintetica en general. La primera contribucion es un nuevo modelo de circuito genetico que muestra un comportamiento oscilatorio usando moleculas de ARN cataliticas. La segunda y principal contribucion es un nuevo modelo de circuito genetico que demuestra que una molecula represora actuando sobre el lazo de un gen auto-activado produce oscilaciones robustas. Primera contribucion: Es un nuevo modelo de oscilador genetico sintetico basado en una tipica red genetica compuesta por dos genes con dos lazos de retroa-limentacion, uno positivo y otro negativo. La novedad de este modelo es que el represor es una molecula de ARN catalftica, en lugar de una protefna o una molecula de ARN no-catalitica. Este ARN catalitico es una ribozima que actua despues de la transcription genetica uniendose y cortando moleculas de ARN mensajero (ARNm). Este reloj genetico involucra solo dos genes, un ARNm y una proteina activadora, aparte de la ribozima. Como ejemplo de funcionamiento, se han escogido valores de los parametros que producen oscilaciones con periodo circadiano (24 horas) tanto en simulaciones deterministas como estocasticas. El efecto de las fluctuaciones es-tocasticas ha sido cuantificado mediante un histograma del periodo y la función de auto-correlacion. La conclusion es que las moleculas de ARN con propiedades cataliticas pueden jugar el misnio papel que las protemas represoras, y por lo tanto, simplificar el diseno de los osciladores geneticos. Segunda y principal contribucion: Es un nuevo modelo de oscilador genetico que demuestra que un gen auto-activado junto con una simple interaction negativa puede producir oscilaciones robustas. Este modelo ha sido estudiado y validado matematicamente. El modelo esta compuesto de dos partes bien diferenciadas. La primera parte es un lazo de retroalimentacion positiva creado por una proteina que se une al promotor de su propio gen activando la transcription. La segunda parte es una interaction negativa en la que una molecula represora evita la union de la proteina con el promotor. Un estudio estocastico muestra que el sistema es robusto al ruido. Un estudio determinista muestra que la dinamica del sistema es debida principalmente a dos tipos de biomoleculas: la proteina, y el complejo formado por el represor y esta proteina. La conclusion principal de este estudio es que una simple y usual interaction negativa, tal como una degradation, un secuestro o una inhibition, actuando sobre el lazo de retroalimentacion positiva de un solo gen es una condition suficiente para producir oscilaciones robustas. Un gen es suficiente y el lazo de retroalimentacion positiva no necesita activar a un segundo gen represor, tal y como ocurre en los relojes actuales con dos genes. Esto significa que a nivel genetico un lazo de retroalimentacion negativa no es necesario de forma explicita. Ademas, este modelo no necesita reacciones cooperativas ni la formation de multimeros proteicos, al contrario que en muchos osciladores geneticos. Aplicaciones y futuras lineas de investigacion: En los liltimos anos, se han descubierto muchas moleculas de ARN con capacidad catalitica. El primer modelo de oscilador genetico propuesto en esta tesis usa ribozimas como moleculas repre¬soras. Esto podria proporcionar nuevos principios de diseno en biologia sintetica y una mejor comprension de los relojes celulares regulados por moleculas de ARN. El segundo modelo de oscilador genetico propuesto aqui involucra solo una represion actuando sobre un gen auto-activado y produce oscilaciones robustas. Sorprendente-mente, un segundo gen represor no es necesario al contrario que en los bien conocidos osciladores con dos genes. Este resultado podria ayudar a clarificar los principios de diseno de los relojes celulares naturales y constituir una nueva y eficiente he-rramienta para crear osciladores geneticos sinteticos. Algunas de las futuras lineas de investigation abiertas tras esta tesis son: (1) la validation in vivo e in vitro de ambos modelos, (2) el estudio del potential del segundo modelo como circuito base para la construction de una memoria genetica, (3) el estudio de nuevos osciladores geneticos regulados por ARN no codificante y, por ultimo, (4) el rediseno del se¬gundo modelo de oscilador genetico para su uso como biosensor capaz de detectar genes auto-activados en redes geneticas.

Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Artificial Neural Networks still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning ANN parameters. In recent years the use of hybrid technologies, combining Artificial Neural Networks and Genetic Algorithms, has been utilized to. In this work, several ANN topologies were trained and tested using Artificial Neural Networks and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out.

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.

Maximizing the number of polychronous groups in spiking networks

In this paper we investigate the effect of biasing the axonal connection delay values in the number of polychronous groups produced for a spiking neuron network model. We use an estimation of distribution algorithm (EDA) that learns tree models to search for optimal delay configurations. Our results indicate that the introduced approach can be used to considerably increase the number of such groups.

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%.

Neuronanatomy, neurology and Bayesian networks

Bayesian networks are data mining models with clear semantics and a sound theoretical foundation. In this keynote talk we will pinpoint a number of neuroscience problems that can be addressed using Bayesian networks. In neuroanatomy, we will show computer simulation models of dendritic trees and classification of neuron types, both based on morphological features. In neurology, we will present the search for genetic biomarkers in Alzheimer's disease and the prediction of health-related quality of life in Parkinson's disease. Most of these challenging problems posed by neuroscience involve new Bayesian network designs that can cope with multiple class variables, small sample sizes, or labels annotated by several experts.

The underlying pathway structure of biochemical reaction networks

Bioinformatics is yielding extensive, and in some cases complete, genetic and biochemical information about individual cell types and cellular processes, providing the composition of living cells and the molecular structure of its components. These components together perform integrated cellular functions that now need to be analyzed. In particular, the functional definition of biochemical pathways and their role in the context of the whole cell is lacking. In this study, we show how the mass balance constraints that govern the function of biochemical reaction networks lead to the translation of this problem into the realm of linear algebra. The functional capabilities of biochemical reaction networks, and thus the choices that cells can make, are reflected in the null space of their stoichiometric matrix. The null space is spanned by a finite number of basis vectors. We present an algorithm for the synthesis of a set of basis vectors for spanning the null space of the stoichiometric matrix, in which these basis vectors represent the underlying biochemical pathways that are fundamental to the corresponding biochemical reaction network. In other words, all possible flux distributions achievable by a defined set of biochemical reactions are represented by a linear combination of these basis pathways. These basis pathways thus represent the underlying pathway structure of the defined biochemical reaction network. This development is significant from a fundamental and conceptual standpoint because it yields a holistic definition of biochemical pathways in contrast to definitions that have arisen from the historical development of our knowledge about biochemical processes. Additionally, this new conceptual framework will be important in defining, characterizing, and studying biochemical pathways from the rapidly growing information on cellular function.

A Late Mitotic Regulatory Network Controlling Cyclin Destruction in Saccharomyces cerevisiae

Exit from mitosis requires the inactivation of mitotic cyclin-dependent kinase–cyclin complexes, primarily by ubiquitin-dependent cyclin proteolysis. Cyclin destruction is regulated by a ubiquitin ligase known as the anaphase-promoting complex (APC). In the budding yeast Saccharomyces cerevisiae, members of a large class of late mitotic mutants, including cdc15, cdc5, cdc14, dbf2, and tem1, arrest in anaphase with a phenotype similar to that of cells expressing nondegradable forms of mitotic cyclins. We addressed the possibility that the products of these genes are components of a regulatory network that governs cyclin proteolysis. We identified a complex array of genetic interactions among these mutants and found that the growth defect in most of the mutants is suppressed by overexpression of SPO12, YAK1, and SIC1 and is exacerbated by overproduction of the mitotic cyclin Clb2. When arrested in late mitosis, the mutants exhibit a defect in cyclin-specific APC activity that is accompanied by high Clb2 levels and low levels of the anaphase inhibitor Pds1. Mutant cells arrested in G1 contain normal APC activity. We conclude that Cdc15, Cdc5, Cdc14, Dbf2, and Tem1 cooperate in the activation of the APC in late mitosis but are not required for maintenance of that activity in G1.

Altered recognition mutants of the response regulator PhoB: A new genetic strategy for studying protein–protein interactions

Two-component regulatory systems require highly specific interactions between histidine kinase (transmitter) and response regulator (receiver) proteins. We have developed a novel genetic strategy that is based on tightly regulated synthesis of a given protein to identify domains and residues of an interacting protein that are critical for interactions between them. Using a reporter strain synthesizing the nonpartner kinase VanS under tight arabinose control and carrying a promoter-lacZ fusion activated by phospho-PhoB, we isolated altered recognition (AR) mutants of PhoB showing enhanced activation (phosphorylation) by VanS as arabinose-dependent Lac+ mutants. Changes in the PhoBAR mutants cluster in a “patch” near the proposed helix 4 of PhoB based on the CheY crystal structure (a homolog of the PhoB receiver domain) providing further evidence that helix 4 lies in the kinase-regulator interface. Based on the CheY structure, one mutant has an additional change in a region that may propagate a conformational change to helix 4. The overall genetic strategy described here may also be useful for studying interactions of other components of the vancomycin resistance and Pi signal transduction pathways, other two-component regulatory systems, and other interacting proteins. Conditionally replicative oriRR6Kγ attP “genome targeting” suicide plasmids carrying mutagenized phoB coding regions were integrated into the chromosome of a reporter strain to create mutant libraries; plasmids encoding mutant PhoB proteins were subsequently retrieved by P1-Int-Xis cloning. Finally, the use of similar genome targeting plasmids and P1-Int-Xis cloning should be generally useful for constructing genomic libraries from a wide array of organisms.

A functional genetic screen identifies regions at the C-terminal tail and death-domain of death-associated protein kinase that are critical for its proapoptotic activity

Death-associated protein kinase (DAP-kinase) is a Ca+2/calmodulin-regulated serine/threonine kinase with a multidomain structure that participates in apoptosis induced by a variety of signals. To identify regions in this protein that are critical for its proapoptotic activity, we performed a genetic screen on the basis of functional selection of short DAP-kinase-derived fragments that could protect cells from apoptosis by acting in a dominant-negative manner. We expressed a library of randomly fragmented DAP-kinase cDNA in HeLa cells and treated these cells with IFN-γ to induce apoptosis. Functional cDNA fragments were recovered from cells that survived the selection, and those in the sense orientation were examined further in a secondary screen for their ability to protect cells from DAP-kinase-dependent tumor necrosis factor-α-induced apoptosis. We isolated four biologically active peptides that mapped to the ankyrin repeats, the “linker” region, the death domain, and the C-terminal tail of DAP-kinase. Molecular modeling of the complete death domain provided a structural basis for the function of the death-domain-derived fragment by suggesting that the protective fragment constitutes a distinct substructure. The last fragment, spanning the C-terminal serine-rich tail, defined a new regulatory region. Ectopic expression of the tail peptide (17 amino acids) inhibited the function of DAP-kinase, whereas removal of this region from the complete protein caused enhancement of the killing activity, indicating that the C-terminal tail normally plays a negative regulatory role. Altogether, this unbiased screen highlighted functionally important regions in the protein and revealed an additional level of regulation of DAP-kinase apoptotic function that does not affect the catalytic activity.

Manipulating the genetic identity and biochemical surface properties of individual cells with electric-field-induced fusion

A method for cell–cell and cell–liposome fusion at the single-cell level is described. Individual cells or liposomes were first selected and manipulated either by optical trapping or by adhesion to a micromanipulator-controlled ultramicroelectrode. Spatially selective fusion of the cell–cell or cell–liposome pair was achieved by the application of a highly focused electric field through a pair of 5-μm o.d. carbon-fiber ultramicroelectrodes. The ability to fuse together single cells opens new possibilities in the manipulation of the genetic and cellular makeup of individual cells in a controlled manner. In the study of cellular networks, for example, the alteration of the biochemical identity of a selected cell can have a profound effect on the behavior of the entire network. Fusion of a single liposome with a target cell allows the introduction of the liposomal content into the cell interior as well as the addition of lipids and membrane proteins onto the cell surface. This cell–liposome fusion represents an approach to the manipulation of the cytoplasmic contents and surface properties of single cells. As an example, we have introduced a membrane protein (γ-glutamyltransferase) reconstituted in liposomes into the cell plasma membrane.

A yeast genetic system for selecting small molecule inhibitors of protein–protein interactions in nanodroplets

Cellular processes are mediated by complex networks of molecular interactions. Dissection of their role most commonly is achieved by using genetic mutations that alter, for example, protein–protein interactions. Small molecules that accomplish the same result would provide a powerful complement to the genetic approach, but it generally is believed that such molecules are rare. There are several natural products, however, that illustrate the feasibility of this approach. Split-pool synthesis now provides a simple mechanical means to prepare vast numbers of complex, even natural product-like, molecules individually attached to cell-sized polymer beads. Here, we describe a genetic system compatible with split-pool synthesis that allows the detection of cell-permeable, small molecule inhibitors of protein–protein interactions in 100- to 200-nl cell culture droplets, prepared by a recently described technique that arrays large numbers of such droplets. These “nanodroplets” contain defined media, cells, and one or more beads containing ≈100 pmol of a photoreleasable small molecule and a controlled number of cells. The engineered Saccharomyces cerevisiae cells used in this study express two interacting proteins after induction with galactose whose interaction results in cell death in the presence of 5-fluoroorotic acid (inducible reverse two-hybrid assay). Disruption of the interaction by a small molecule allows growth, and the small molecule can be introduced into the system hours before induction of the toxic interaction. We demonstrate that the interaction between the activin receptor R1 and the immunophilin protein FKBP12 can be disrupted by the small molecule FK506 at nanomolar concentrations in nanodroplets. This system should provide a general method for selecting cell-permeable ligands that can be used to study the relevance of protein–protein interactions in living cells or organisms.

Genomic and genetic dissection of an archaeal regulon

The extremely halophilic archaeon Halobacterium sp. NRC-1 can grow phototrophically by means of light-driven proton pumping by bacteriorhodopsin in the purple membrane. Here, we show by genetic analysis of the wild type, and insertion and double-frame shift mutants of Bat that this transcriptional regulator coordinates synthesis of a structural protein and a chromophore for purple membrane biogenesis in response to both light and oxygen. Analysis of the complete Halobacterium sp. NRC-1 genome sequence showed that the regulatory site, upstream activator sequence (UAS), the putative binding site for Bat upstream of the bacterio-opsin gene (bop), is also present upstream to the other Bat-regulated genes. The transcription regulator Bat contains a photoresponsive cGMP-binding (GAF) domain, and a bacterial AraC type helix–turn–helix DNA binding motif. We also provide evidence for involvement of the PAS/PAC domain of Bat in redox-sensing activity by genetic analysis of a purple membrane overproducer. Five additional Bat-like putative regulatory genes were found, which together are likely to be responsible for orchestrating the complex response of this archaeon to light and oxygen. Similarities of the bop-like UAS and transcription factors in diverse organisms, including a plant and a γ-proteobacterium, suggest an ancient origin for this regulon capable of coordinating light and oxygen responses in the three major branches of the evolutionary tree of life. Finally, sensitivity of four of five regulon genes to DNA supercoiling is demonstrated and correlated to presence of alternating purine–pyrimidine sequences (RY boxes) near the regulated promoters.