Parallel turing machines with one-head control units and cellular automata

Parallel Turing machines (PTM) can be viewed as a generalization of cellular automata (CA) where an additional measure called processor complexity can be defined which indicates the ``amount of parallelism\u27\u27 used. In this paper PTM are investigated with respect to their power as recognizers of formal languages. A combinatorial approach as well as diagonalization are used to obtain hierarchies of complexity classes for PTM and CA. In some cases it is possible to keep the space complexity of PTM fixed. Thus for the first time it is possible to find hierarchies of complexity classes (though not CA classes) which are completely contained in the class of languages recognizable by CA with space complexity n and in polynomial time. A possible collapse of the time hierarchy for these CA would therefore also imply some unexpected properties of PTM

Research-study of a self-organizing computer

It is shown that a self organizing system has two main components: an organizable physical part, and a programing part. This report presents the organizable part in the form of a programable hardware and its programing language

Considerations in designing a cybernetic simple 'learning' model; and an overview of the problem of modelling learning

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Learning is viewed as a central feature of living systems and must be manifested in any artifact that claims to exhibit general intelligence. The central aims of the thesis are twofold: (1) - To review and critically assess the empirical and theoretical aspects of learning as have been addressed in a multitude of disciplines, with the aim of extracting fundamental features and elements. (2) - To develop a more systematic approach to the cybernetic modelling of learning than has been achieved hitherto. In pursuit of aim (1) above the following discussions are included: Historical and Philosophical backgrounds; Natural learning, both physiological and psychological aspects; Hierarchies of learning identified in the evolutionary, functional and developmental senses; An extensive section on the general problem of modelling of learning and the formal tools, is included as a link between aims (1) and (2). Following this a systematic and historically oriented study of cybernetic and other related approaches to the problem of modelling of learning is presented. This then leads to the development of a state-of-the-art general purpose experimental cybernetic learning model. The programming and use of this model is also fully described, including an elaborate scheme for the manifestation of simple learning

Computational methods for multi-omic models of cell metabolism and their importance for theoretical computer science

To paraphrase Stan Ulam, a Polish mathematician who became a leading figure in the Manhattan Project, in this dissertation I focus not only on how computer science can help biologists, but also on how biology can inspire computer scientists. On one hand, computer science provides powerful abstraction tools for metabolic networks. Cell metabolism is the set of chemical reactions taking place in a cell, with the aim of maintaining the living state of the cell. Due to the intrinsic complexity of metabolic networks, predicting the phenotypic traits resulting from a given genotype and metabolic structure is a challenging task. To this end, mathematical models of metabolic networks, called genome-scale metabolic models, contain all known metabolic reactions in an organism and can be analyzed with computational methods. In this dissertation, I propose a set of methods to investigate models of metabolic networks. These include multi-objective optimization, sensitivity, robustness and identifiability analysis, and are applied to a set of genome-scale models. Then, I augment the framework to predict metabolic adaptation to a changing environment. The adaptation of a microorganism to new environmental conditions involves shifts in its biochemical network and in the gene expression level. However, gene expression profiles do not provide a comprehensive understanding of the cellular behavior. Examples are the cases in which similar profiles may cause different phenotypic outcomes, while different profiles may give rise to similar behaviors. In fact, my idea is to study the metabolic response to diverse environmental conditions by predicting and analyzing changes in the internal molecular environment and in the underlying multi-omic networks. I also adapt statistical and mathematical methods (including principal component analysis and hypervolume) to evaluate short term metabolic evolution and perform comparative analysis of metabolic conditions. On the other hand, my vision is that a biomolecular system can be cast as a “biological computer”, therefore providing insights into computational processes. I therefore study how computation can be performed in a biological system by proposing a map between a biological organism and the von Neumann architecture, where metabolism executes reactions mapped to instructions of a Turing machine. A Boolean string represents the genetic knockout strategy and also the executable program stored in the “memory” of the organism. I use this framework to investigate scenarios of communication among cells, gene duplication, and lateral gene transfer. Remarkably, this mapping allows estimating the computational capability of an organism, taking into account also transmission events and communication outcomes

Proceedings of the 26th International Symposium on Theoretical Aspects of Computer Science (STACS'09)

The Symposium on Theoretical Aspects of Computer Science (STACS) is held alternately in France and in Germany. The conference of February 26-28, 2009, held in Freiburg, is the 26th in this series. Previous meetings took place in Paris (1984), Saarbr¨ucken (1985), Orsay (1986), Passau (1987), Bordeaux (1988), Paderborn (1989), Rouen (1990), Hamburg (1991), Cachan (1992), W¨urzburg (1993), Caen (1994), M¨unchen (1995), Grenoble (1996), L¨ubeck (1997), Paris (1998), Trier (1999), Lille (2000), Dresden (2001), Antibes (2002), Berlin (2003), Montpellier (2004), Stuttgart (2005), Marseille (2006), Aachen (2007), and Bordeaux (2008). ..

Simulation of the spatial structure and cellular organization evolution of cell aggregates arranged in various simple geometries, using a kinetic monte carlo method applied to a lattice model

ilustraciones, graficasEsta tesis trata los modelos de morfogénesis, en particular los modelos de evolución guiada por contacto que son coherentes con la hipótesis de la adhesión diferencial. Se presenta una revisión de algunos modelos, sus principios biológicos subyacentes, la relevancia y aplicaciones en el marco de la bioimpresión, la ingeniería de tejidos y la bioconvergencia. Luego, se presentan los detalles de los modelos basados en métodos de Monte Carlo para profundizar más adelante en el modelo basados en algoritmos Kinetic Monte Carlo (KMC) , más específicamente, se describe en detalle un modelo KMC de autoaprendizaje (SL-KMC). Se presenta y explica la estructura algorítmica del código implementado, se evalúa el rendimiento del modelo y se compara con un modelo KMC tradicional. Finalmente, se realizan los procesos de calibración y validación, se observó que el modelo es capaz de replicar la evolución del sistema multicelular cuando las condiciones de energía interfacial del sistema simulado son similares a las del sistema de calibraciones. (Texto tomado de la fuente)This thesis treats the models for morphogenesis, in particular the contact-guided evolution models that are coherent with the differential adhesion hypothesis. A review of some models, their biological underpinning principles, the relevance and applications in the framework of bioprinting, tissue engineering and bioconvergence are presented. Then the details for the Monte Carlo methods-based models are presented to later deep dive into the Kinetic Monte Carlo (KMC) based model, and more specifically a Self-Learning KMC (SL-KMC) model is described to detail. The algorithmic structure of the implemented code is presented and explained, the model performance is assessed and compared with a traditional KMC model. Finally, the calibration and validation processes have been carried out, it was observed that the model is able to replicate the multicellular system evolution when the interfacial energy conditions of the simulated system are similar to those of the calibrations system.MaestríaMagíster en Ingeniería - Ingeniería Químic