An overview of the use of neural networks for data mining tasks

In the recent years, the area of data mining has been experiencing considerable demand for technologies that extract knowledge from large and complex data sources. There has been substantial commercial interest as well as active research in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from large datasets. Artificial neural networks (NNs) are popular biologically-inspired intelligent methodologies, whose classification, prediction, and pattern recognition capabilities have been utilized successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction, and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks. © 2012 Wiley Periodicals, Inc.

Associating spontaneous with evoked activity in a neural mass model of cat visual cortex

Spontaneous activity of the brain at rest frequently has been considered a mere backdrop to the salient activity evoked by external stimuli or tasks. However, the resting state of the brain consumes most of its energy budget, which suggests a far more important role. An intriguing hint comes from experimental observations of spontaneous activity patterns, which closely resemble those evoked by visual stimulation with oriented gratings, except that cortex appeared to cycle between different orientation maps. Moreover, patterns similar to those evoked by the behaviorally most relevant horizontal and vertical orientations occurred more often than those corresponding to oblique angles. We hypothesize that this kind of spontaneous activity develops at least to some degree autonomously, providing a dynamical reservoir of cortical states, which are then associated with visual stimuli through learning. To test this hypothesis, we use a biologically inspired neural mass model to simulate a patch of cat visual cortex. Spontaneous transitions between orientation states were induced by modest modifications of the neural connectivity, establishing a stable heteroclinic channel. Significantly, the experimentally observed greater frequency of states representing the behaviorally important horizontal and vertical orientations emerged spontaneously from these simulations. We then applied bar-shaped inputs to the model cortex and used Hebbian learning rules to modify the corresponding synaptic strengths. After unsupervised learning, different bar inputs reliably and exclusively evoked their associated orientation state; whereas in the absence of input, the model cortex resumed its spontaneous cycling. We conclude that the experimentally observed similarities between spontaneous and evoked activity in visual cortex can be explained as the outcome of a learning process that associates external stimuli with a preexisting reservoir of autonomous neural activity states. Our findings hence demonstrate how cortical connectivity can link the maintenance of spontaneous activity in the brain mechanistically to its core cognitive functions.

Body-centric modelling, identification, and acceleration tracking control of a quadrotor UAV

This paper presents the mathematical development of a body-centric nonlinear dynamic model of a quadrotor UAV that is suitable for the development of biologically inspired navigation strategies. Analytical approximations are used to find an initial guess of the parameters of the nonlinear model, then parameter estimation methods are used to refine the model parameters using the data obtained from onboard sensors during flight. Due to the unstable nature of the quadrotor model, the identification process is performed with the system in closed-loop control of attitude angles. The obtained model parameters are validated using real unseen experimental data. Based on the identified model, a Linear-Quadratic (LQ) optimal tracker is designed to stabilize the quadrotor and facilitate its translational control by tracking body accelerations. The LQ tracker is tested on an experimental quadrotor UAV and the obtained results are a further means to validate the quality of the estimated model. The unique formulation of the control problem in the body frame makes the controller better suited for bio-inspired navigation and guidance strategies than conventional attitude or position based control systems that can be found in the existing literature.

Utilizzo di dispositivi magnetici nel trattamento della patologia artrosica del ginocchio

The use of scaffolds for Tissue Engineering (TE) is increasing due to their efficacy in helping the body rebuild damaged or diseased tissue. Hydroxyapatite (HA) is the most suitable bioactive ceramic to be used in orthopaedic reconstruction since it replicates the mineral component of the hard tissues, and it has therefore excellent biocompatibility properties. The temporal and spatial control of the tissue regeneration process is the limit to be overcome in order to treat large bone and osteochondral defects. In this thesis we describe the realization of a magnetic scaffolds able to attract and take up growth factors or other bio-agents in vivo via a driving magnetic force. This concept involves the use of magnetic nanoparticles (MNP) functionalized with selected growth factors or stem cells. These functionalized MNP act as shuttles transporting the bio-agents towards and inside the scaffold under the effect of the magnetic field, enhancing the control of tissue regeneration processes. This scaffold can be imagined as a fixed “station” that provides a unique possibility to adjust the scaffold activity to the specific needs of the healing tissue. Synthetic bone graft substitutes, made of collagen or biomineralized collagen (i.e. biomimetic Hydroxyapatite/collagen composites) were used as starting materials for the fabrication of magnetic scaffolds. These materials are routinely used clinically to replace damaged or diseased cartilaginous or bone tissue. Our magnetization technique is based on a dip-coating process consisting in the infilling of biologically inspired porous scaffolds with aqueous biocompatible ferrofluids’ suspensions. In this technique, the specific interconnected porosity of the scaffolds allows the ferrofluids to be drawn inside the structure by capillarity. A subsequent freeze-drying process allows the solvent elimination while keeping very nearly the original shape and porosity of the scaffolds. The remaining magnetic nanoparticles, which are trapped in the structure, lead to the magnetization of the HA/Collagen scaffold. We demonstrate here the possibility to magnetize commercially available scaffolds up to magnetization values that are used in drug delivery processes. The preliminary biocompatibility test showed that the investigated scaffolds provide a suitable micro-environment for cells. The biocompatibility of scaffold facilitates the growth and proliferation of osteogenic cells.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

A Model of Emotion as Patterned Metacontrol

Adaptive systems use feedback as a key strategy to cope with uncertainty and change in their environments. The information fed back from the sensorimotor loop into the control architecture can be used to change different elements of the controller at four different levels: parameters of the control model, the control model itself, the functional organization of the agent and the functional components of the agent. The complexity of such a space of potential configurations is daunting. The only viable alternative for the agent ?in practical, economical, evolutionary terms? is the reduction of the dimensionality of the configuration space. This reduction is achieved both by functionalisation —or, to be more precise, by interface minimization— and by patterning, i.e. the selection among a predefined set of organisational configurations. This last analysis let us state the central problem of how autonomy emerges from the integration of the cognitive, emotional and autonomic systems in strict functional terms: autonomy is achieved by the closure of functional dependency. In this paper we will show a general model of how the emotional biological systems operate following this theoretical analysis and how this model is also of applicability to a wide spectrum of artificial systems.

Stress Detection by means of Stress Physiological Template

This paper describes a stress detection system based on fuzzy logic and two physiological signals: Galvanic Skin Response and Heart Rate. Instead of providing a global stress classification, this approach creates an individual stress templates, gathering the behaviour of individuals under situations with different degrees of stress. The proposed method is able to detect stress properly with a rate of 99.5%, being evaluated with a database of 80 individuals. This result improves former approaches in the literature and well-known machine learning techniques like SVM, k-NN, GMM and Linear Discriminant Analysis. Finally, the proposed method is highly suitable for real-time applications

A robustness verification system for mobile phone authentication based on gestures using Linear Discriminant Analysis

This article evaluates an authentication technique for mobiles based on gestures. Users create a remindful identifying gesture to be considered as their in-air signature. This work analyzes a database of 120 gestures of different vulnerability, obtaining an Equal Error Rate (EER) of 9.19% when robustness of gestures is not verified. Most of the errors in this EER come from very simple and easily forgeable gestures that should be discarded at enrollment phase. Therefore, an in-air signature robustness verification system using Linear Discriminant Analysis is proposed to infer automatically whether the gesture is secure or not. Different configurations have been tested obtaining a lowest EER of 4.01% when 45.02% of gestures were discarded, and an optimal compromise of EER of 4.82% when 19.19% of gestures were automatically rejected.

Gaussian Multiscale Aggregation oriented to Hand Biometric Segmentation in Mobile Devices

New trends in biometrics are oriented to mobile devices in order to increase the overall security in daily actions like bank account access, e-commerce or even document protection within the mobile. However, applying biometrics to mobile devices imply challenging aspects in biometric data acquisition, feature extraction or private data storage. Concretely, this paper attempts to deal with the problem of hand segmentation given a picture of the hand in an unknown background, requiring an accurate result in terms of hand isolation. For the sake of user acceptability, no restrictions are done on background, and therefore, hand images can be taken without any constraint, resulting segmentation in an exigent task. Multiscale aggregation strategies are proposed in order to solve this problem due to their accurate results in unconstrained and complicated scenarios, together with their properties in time performance. This method is evaluated with a public synthetic database with 480000 images considering different backgrounds and illumination environments. The results obtained in terms of accuracy and time performance highlight their capability of being a suitable solution for the problem of hand segmentation in contact-less environments, outperforming competitive methods in literature like Lossy Data Compression image segmentation (LDC).

A Model of Emotion as Patterned Metacontrol

Adaptive agents use feedback as a key strategy to cope with un- certainty and change in their environments. The information fed back from the sensorimotor loop into the control subsystem can be used to change four different elements of the controller: parameters associated to the control model, the control model itself, the functional organization of the agent and the functional realization of the agent. There are many change alternatives and hence the complexity of the agent’s space of potential configurations is daunting. The only viable alternative for space- and time-constrained agents —in practical, economical, evolutionary terms— is to achieve a reduction of the dimensionality of this configuration space. Emotions play a critical role in this reduction. The reduction is achieved by func- tionalization, interface minimization and by patterning, i.e. by selection among a predefined set of organizational configurations. This analysis lets us state how autonomy emerges from the integration of cognitive, emotional and autonomic systems in strict functional terms: autonomy is achieved by the closure of functional dependency. Emotion-based morphofunctional systems are able to exhibit complex adaptation patterns at a reduced cognitive cost. In this article we show a general model of how emotion supports functional adaptation and how the emotional biological systems operate following this theoretical model. We will also show how this model is also of applicability to the construction of a wide spectrum of artificial systems1.

Towards architectural foundations for cognitive self-aware systems

In this talk we address a proposal concerning a methodology for extracting universal, domain neutral, architectural design patterns from the analysis of biological cognition. This will render a set of design principles and design patterns oriented towards the construction of better machines. Bio- inspiration cannot be a one step process if we we are going to to build robust, dependable autonomous agents; we must build solid theories first, departing from natural systems, and supporting our designs of artificial ones.

Application of self-organizing techniques for the distribution of heterogeneous multi-tasks in multi-robot systems

This paper focuses on the general problem of coordinating of multi-robot systems, more specifically, it addresses the self-election of heterogeneous and specialized tasks by autonomous robots. In this regard, it has proposed experimenting with two different techniques based chiefly on selforganization and emergence biologically inspired, by applying response threshold models as well as ant colony optimization. Under this approach it can speak of multi-tasks selection instead of multi-tasks allocation, that means, as the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. It has evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Model-based Self-awareness Patterns for Autonomy

Los sistemas técnicos son cada vez más complejos, incorporan funciones más avanzadas, están más integrados con otros sistemas y trabajan en entornos menos controlados. Todo esto supone unas condiciones más exigentes y con mayor incertidumbre para los sistemas de control, a los que además se demanda un comportamiento más autónomo y fiable. La adaptabilidad de manera autónoma es un reto para tecnologías de control actualmente. El proyecto de investigación ASys propone abordarlo trasladando la responsabilidad de la capacidad de adaptación del sistema de los ingenieros en tiempo de diseño al propio sistema en operación. Esta tesis pretende avanzar en la formulación y materialización técnica de los principios de ASys de cognición y auto-consciencia basadas en modelos y autogestión de los sistemas en tiempo de operación para una autonomía robusta. Para ello el trabajo se ha centrado en la capacidad de auto-conciencia, inspirada en los sistemas biológicos, y se ha explorado la posibilidad de integrarla en la arquitectura de los sistemas de control. Además de la auto-consciencia, se han explorado otros temas relevantes: modelado funcional, modelado de software, tecnología de los patrones, tecnología de componentes, tolerancia a fallos. Se ha analizado el estado de la técnica en los ámbitos pertinentes para las cuestiones de la auto-consciencia y la adaptabilidad en sistemas técnicos: arquitecturas cognitivas, control tolerante a fallos, y arquitecturas software dinámicas y computación autonómica. El marco teórico de ASys existente de sistemas autónomos cognitivos ha sido adaptado para servir de base para este análisis de autoconsciencia y adaptación y para dar sustento conceptual al posterior desarrollo de la solución. La tesis propone una solución general de diseño para la construcción de sistemas autónomos auto-conscientes. La idea central es la integración de un meta-controlador en la arquitectura de control del sistema autónomo, capaz de percibir la estado funcional del sistema de control y, si es necesario, reconfigurarlo en tiempo de operación. Esta solución de metacontrol se ha formalizado en cuatro patrones de diseño: i) el Patrón Metacontrol, que define la integración de un subsistema de metacontrol, responsable de controlar al propio sistema de control a través de la interfaz proporcionada por su plataforma de componentes, ii) el patrón Bucle de Control Epistémico, que define un bucle de control cognitivo basado en el modelos y que se puede aplicar al diseño del metacontrol, iii) el patrón de Reflexión basada en Modelo Profundo propone una solución para construir el modelo ejecutable utilizado por el meta-controlador mediante una transformación de modelo a modelo a partir del modelo de ingeniería del sistema, y, finalmente, iv) el Patrón Metacontrol Funcional, que estructura el meta-controlador en dos bucles, uno para el control de la configuración de los componentes del sistema de control, y otro sobre éste, controlando las funciones que realiza dicha configuración de componentes; de esta manera las consideraciones funcionales y estructurales se desacoplan. La Arquitectura OM y el metamodelo TOMASys son las piezas centrales del marco arquitectónico desarrollado para materializar la solución compuesta de los patrones anteriores. El metamodelo TOMASys ha sido desarrollado para la representación de la estructura y su relación con los requisitos funcionales de cualquier sistema autónomo. La Arquitectura OM es un patrón de referencia para la construcción de una metacontrolador integrando los patrones de diseño propuestos. Este meta-controlador se puede integrar en la arquitectura de cualquier sistema control basado en componentes. El elemento clave de su funcionamiento es un modelo TOMASys del sistema decontrol, que el meta-controlador usa para monitorizarlo y calcular las acciones de reconfiguración necesarias para adaptarlo a las circunstancias en cada momento. Un proceso de ingeniería, complementado con otros recursos, ha sido elaborado para guiar la aplicación del marco arquitectónico OM. Dicho Proceso de Ingeniería OM define la metodología a seguir para construir el subsistema de metacontrol para un sistema autónomo a partir del modelo funcional del mismo. La librería OMJava proporciona una implementación del meta-controlador OM que se puede integrar en el control de cualquier sistema autónomo, independientemente del dominio de la aplicación o de su tecnología de implementación. Para concluir, la solución completa ha sido validada con el desarrollo de un robot móvil autónomo que incorpora un meta-controlador con la Arquitectura OM. Las propiedades de auto-consciencia y adaptación proporcionadas por el meta-controlador han sido validadas en diferentes escenarios de operación del robot, en los que el sistema era capaz de sobreponerse a fallos en el sistema de control mediante reconfiguraciones orquestadas por el metacontrolador. ABSTRACT Technical systems are becoming more complex, they incorporate more advanced functionalities, they are more integrated with other systems and they are deployed in less controlled environments. All this supposes a more demanding and uncertain scenario for control systems, which are also required to be more autonomous and dependable. Autonomous adaptivity is a current challenge for extant control technologies. The ASys research project proposes to address it by moving the responsibility for adaptivity from the engineers at design time to the system at run-time. This thesis has intended to advance in the formulation and technical reification of ASys principles of model-based self-cognition and having systems self-handle at runtime for robust autonomy. For that it has focused on the biologically inspired capability of self-awareness, and explored the possibilities to embed it into the very architecture of control systems. Besides self-awareness, other themes related to the envisioned solution have been explored: functional modeling, software modeling, patterns technology, components technology, fault tolerance. The state of the art in fields relevant for the issues of self-awareness and adaptivity has been analysed: cognitive architectures, fault-tolerant control, and software architectural reflection and autonomic computing. The extant and evolving ASys Theoretical Framework for cognitive autonomous systems has been adapted to provide a basement for this selfhood-centred analysis and to conceptually support the subsequent development of our solution. The thesis proposes a general design solution for building self-aware autonomous systems. Its central idea is the integration of a metacontroller in the control architecture of the autonomous system, capable of perceiving the functional state of the control system and reconfiguring it if necessary at run-time. This metacontrol solution has been formalised into four design patterns: i) the Metacontrol Pattern, which defines the integration of a metacontrol subsystem, controlling the domain control system through an interface provided by its implementation component platform, ii) the Epistemic Control Loop pattern, which defines a modelbased cognitive control loop that can be applied to the design of such a metacontroller, iii) the Deep Model Reflection pattern proposes a solution to produce the online executable model used by the metacontroller by model-to-model transformation from the engineering model, and, finally, iv) the Functional Metacontrol pattern, which proposes to structure the metacontroller in two loops, one for controlling the configuration of components of the controller, and another one on top of the former, controlling the functions being realised by that configuration; this way the functional and structural concerns become decoupled. The OM Architecture and the TOMASys metamodel are the core pieces of the architectural framework developed to reify this patterned solution. The TOMASys metamodel has been developed for representing the structure and its relation to the functional requirements of any autonomous system. The OM architecture is a blueprint for building a metacontroller according to the patterns. This metacontroller can be integrated on top of any component-based control architecture. At the core of its operation lies a TOMASys model of the control system. An engineering process and accompanying assets have been constructed to complete and exploit the architectural framework. The OM Engineering Process defines the process to follow to develop the metacontrol subsystem from the functional model of the controller of the autonomous system. The OMJava library provides a domain and application-independent implementation of an OM Metacontroller than can be used in the implementation phase of OMEP. Finally, the complete solution has been validated in the development of an autonomous mobile robot that incorporates an OM metacontroller. The functional selfawareness and adaptivity properties achieved thanks to the metacontrol system have been validated in different scenarios. In these scenarios the robot was able to overcome failures in the control system thanks to reconfigurations performed by the metacontroller.

Marco computacional para la definición de un modelo bio-inspirado en la señalización celular

Las Redes de Procesadores Evolutivos-NEP propuestas en [Mitrana et al., 2001], son un modelo computacional bio-inspirado a partir de la evolución de poblaciones de células, definiendo a nivel sintáctico algunas propiedades biológicas. En este modelo, las células están representadas por medio de palabras que describen secuencias de ADN. Informalmente, en algún instante de tiempo, el sistema evolutivo está representado por una colección de palabras cada una de las cuales representa una célula. El espacio genotipo de las especies, es un conjunto que recoge aquellas palabras que son aceptadas como sobrevivientes (es decir, como \correctas"). Desde el punto de vista de la evolución, las células pertenecen a especies y su comunidad evoluciona de acuerdo a procesos biológicos como la mutación y la división celular. éstos procesos representan el proceso natural de evolución y ponen de manifiesto una característica intrínseca de la naturaleza: el paralelismo. En este modelo, estos procesos son vistos como operaciones sobre palabras. Formalmente, el modelo de las NEP constituyen una arquitectura paralela y distribuida de procesamiento simbólico inspirada en la Máquina de conexión [Hillis, 1981], en el Paradigma de Flujo Lógico [Errico and Jesshope, 1994] y en las Redes de Procesadores Paralelos de Lenguajes (RPPL) [Csuhaj-Varju and Salomaa, 1997]. Al modelo NEP se han ido agregando nuevas y novedosas extensiones hasta el punto que actualmente podemos hablar de una familia de Redes de Procesadores Bio-inspirados (NBP) [Mitrana et al., 2012b]. Un considerable número de trabajos a lo largo de los últimos años han demostrado la potencia computacional de la familia NBP. En general, éstos modelos son computacionalmente completos, universales y eficientes [Manea et al., 2007], [Manea et al., 2010b], [Mitrana and Martín-Vide, 2005]. De acuerdo a lo anterior, se puede afirmar que el modelo NEP ha adquirido hasta el momento un nivel de madurez considerable. Sin embargo, aunque el modelo es de inspiración biológica, sus metas siguen estando motivadas en la Teoría de Lenguajes Formales y las Ciencias de la Computación. En este sentido, los aspectos biológicos han sido abordados desde una perspectiva cualitativa y el acercamiento a la realidad biológica es de forma meramente sintáctica. Para considerar estos aspectos y lograr dicho acercamiento es necesario que el modelo NEP tenga una perspectiva más amplia que incorpore la interacción de aspectos tanto cualitativos como cuantitativos. La contribución de esta Tesis puede considerarse como un paso hacia adelante en una nueva etapa de los NEPs, donde el carácter cuantitativo del modelo es de primordial interés y donde existen posibilidades de un cambio visible en el enfoque de interés del dominio de los problemas a considerar: de las ciencias de la computación hacia la simulación/modelado biológico y viceversa, entre otros. El marco computacional que proponemos en esta Tesis extiende el modelo de las Redes de Procesadores Evolutivos (NEP) y define arquitectura inspirada en la definición de bloques funcionales del proceso de señalización celular para la solución de problemas computacionales complejos y el modelado de fenómenos celulares desde una perspectiva discreta. En particular, se proponen dos extensiones: (1) los Transductores basados en Redes de Procesadores Evolutivos (NEPT), y (2) las Redes Parametrizadas de Procesadores Evolutivos Polarizados (PNPEP). La conservación de las propiedades y el poder computacional tanto de NEPT como de PNPEP se demuestra formalmente. Varias simulaciones de procesos relacionados con la señalización celular son abordadas sintáctica y computacionalmente, con el _n de mostrar la aplicabilidad e idoneidad de estas dos extensiones. ABSTRACT Network of Evolutionary Processors -NEP was proposed in [Mitrana et al., 2001], as a computational model inspired by the evolution of cell populations, which might model some properties of evolving cell communities at the syntactical level. In this model, cells are represented by words which encode their DNA sequences. Informally, at any moment of time, the evolutionary system is described by a collection of words, where each word represents one cell. Cells belong to species and their community evolves according to mutations and division which are defined by operations on words. Only those cells accepted as survivors (correct) are represented by a word in a given set of words, called the genotype space of the species. This feature is analogous with the natural process of evolution. Formally, NEP is based on an architecture for parallel and distributed processing inspired from the Connection Machine [Hillis, 1981], the Flow Logic Paradigm [Errico and Jesshope, 1994] and the Networks of Parallel Language Processors (RPPL) [Csuhaj-Varju and Salomaa, 1997]. Since the date when NEP was proposed, several extensions and variants have appeared engendering a new set of models named Networks of Bio-inspired Processors (NBP) [Mitrana et al., 2012b]. During this time, several works have proved the computational power of NBP. Specifically, their efficiency, universality, and computational completeness have been thoroughly investigated [Manea et al., 2007, Manea et al., 2010b, Mitrana and Martín-Vide, 2005]. Therefore, we can say that the NEP model has reached its maturity. Nevertheless, although the NEP model is biologically inspired, this model is mainly motivated by mathematical and computer science goals. In this context, the biological aspects are only considered from a qualitative and syntactical perspective. In view of this lack, it is important to try to keep the NEP theory as close as possible to the biological reality, extending their perspective incorporating the interplay of qualitative and quantitative aspects. The contribution of this Thesis, can be considered as a starting point in a new era of the NEP model. Then, the quantitative character of the NEP model is mandatory and it can address completely new different types of problems with respect to the classical computational domain (e.g. from the computer science to system biology). Therefore, the computational framework that we propose extends the NEP model and defines an architecture inspired by the functional blocks from cellular signaling in order to solve complex computational problems and cellular phenomena modeled from a discrete perspective. Particularly, we propose two extensions, namely: (1) Transducers based on Network of Evolutionary Processors (NEPT), and (2) Parametrized Network of Polarized Evolutionary Processors (PNPEP). Additionally, we have formally proved that the properties and computational power of NEP is kept in both extensions. Several simulations about processes related with cellular signaling both syntactical and computationally have been considered to show the model suitability.

A multi-scaled approach for simulating chemical reaction systems

In this paper we give an overview of some very recent work, as well as presenting a new approach, on the stochastic simulation of multi-scaled systems involving chemical reactions. In many biological systems (such as genetic regulation and cellular dynamics) there is a mix between small numbers of key regulatory proteins, and medium and large numbers of molecules. In addition, it is important to be able to follow the trajectories of individual molecules by taking proper account of the randomness inherent in such a system. We describe different types of simulation techniques (including the stochastic simulation algorithm, Poisson Runge–Kutta methods and the balanced Euler method) for treating simulations in the three different reaction regimes: slow, medium and fast. We then review some recent techniques on the treatment of coupled slow and fast reactions for stochastic chemical kinetics and present a new approach which couples the three regimes mentioned above. We then apply this approach to a biologically inspired problem involving the expression and activity of LacZ and LacY proteins in E. coli, and conclude with a discussion on the significance of this work.