37 research outputs found

    Membrane computing: traces, neural inspired models, controls

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    Membrane Computing:Traces, Neural Inspired Models, ControlsAutor: Armand-Mihai IonescuDirectores: Dr. Victor Mitrana (URV)Dr. Takashi Yokomori (Universidad Waseda, Jap贸n)Resumen Castellano:El presente trabajo est谩 dedicado a una 谩rea muy activa del c谩lculo natural (que intenta descubrir la odalidad en la cual la naturaleza calcula, especialmente al nivel biol贸gico), es decir el c谩lculo con membranas, y m谩s preciso, a los modelos de membranas inspirados de la funcionalidad biol贸gica de la neurona.La disertaci贸n contribuye al 谩rea de c谩lculo con membranas en tres direcciones principales. Primero, introducimos una nueva manera de definir el resultado de una computaci贸n siguiendo los rastros de un objeto especificado dentro de una estructura celular o de una estructura neuronal. A continuaci贸n, nos acercamos al 谩mbito de la biolog铆a del cerebro, con el objetivo de obtener varias maneras de controlar la computaci贸n por medio de procesos que inhiben/de-inhiben. Tercero, introducimos e investigamos en detallo - aunque en una fase preliminar porque muchos aspectos tienen que ser clarificados - una clase de sistemas inspirados de la manera en la cual las neuronas cooperan por medio de spikes, pulsos el茅ctricos de formas id茅nticas.English summary:The present work is dedicated to a very active branch of natural computing (which tries to discover the way nature computes, especially at a biological level), namely membrane computing, more precisely, to those models of membrane systems mainly inspired from the functioning of the neural cell.The present dissertation contributes to membrane computing in three main directions. First, we introduce a new way of defining the result of a computation by means of following the traces of a specified object within a cell structure or a neural structure. Then, we get closer to the biology of the brain, considering various ways to control the computation by means of inhibiting/de-inhibiting processes. Third, we introduce and investigate in a great - though preliminary, as many issues remain to be clarified - detail a class of P systems inspired from the way neurons cooperate by means of spikes, electrical pulses of identical shapes

    Communication in membrana Systems with symbol Objects.

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    Esta tesis est谩 dedicada a los sistemas de membranas con objetos-s铆mbolo como marco te贸rico de los sistemas paralelos y distribuidos de procesamiento de multiconjuntos.Una computaci贸n de parada puede aceptar, generar o procesar un n煤mero, un vector o una palabra; por tanto el sistema define globalmente (a trav茅s de los resultados de todas sus computaciones) un conjunto de n煤meros, de vectores, de palabras (es decir, un lenguaje), o bien una funci贸n. En esta tesis estudiamos la capacidad de estos sistemas para resolver problemas particulares, as铆 como su potencia computacional. Por ejemplo, las familias de lenguajes definidas por diversas clases de estos sistemas se comparan con las familias cl谩sicas, esto es, lenguajes regulares, independientes del contexto, generados por sistemas 0L tabulados extendidos, generados por gram谩ticas matriciales sin chequeo de apariciones, recursivamente enumerables, etc. Se prestar谩 especial atenci贸n a la comunicaci贸n de objetos entre regiones y a las distintas formas de cooperaci贸n entre ellos.Se pretende (Secci贸n 3.4) realizar una formalizaci贸n los sistemas de membranas y construir una herramienta tipo software para la variante que usa cooperaci贸n no distribuida, el navegador de configuraciones, es decir, un simulador, en el cual el usuario selecciona la siguiente configuraci贸n entre todas las posibles, estando permitido volver hacia atr谩s. Se considerar谩n diversos modelos distribuidos. En el modelo de evoluci贸n y comunicaci贸n (Cap铆tulo 4) separamos las reglas tipo-reescritura y las reglas de transporte (llamadas symport y antiport). Los sistemas de bombeo de protones (proton pumping, Secciones 4.8, 4.9) constituyen una variante de los sistemas de evoluci贸n y comunicaci贸n con un modo restrictivo de cooperaci贸n. Un modelo especial de computaci贸n con membranas es el modelo puramente comunicativo, en el cual los objetos traspasan juntos una membrana. Estudiamos la potencia computacional de las sistemas de membranas con symport/antiport de 2 o 3 objetos (Cap铆tulo 5) y la potencia computacional de las sistemas de membranas con alfabeto limitado (Cap铆tulo 6).El determinismo (Secciones 4.7, 5.5, etc.) es una caracter铆stica especial (restrictiva) de los sistemas computacionales. Se pondr谩 especial 茅nfasis en analizar si esta restricci贸n reduce o no la potencia computacional de los mismos. Los resultados obtenidos para sistemas de bombeo del protones est谩n transferidos (Secci贸n 7.3) a sistemas con catalizadores bistabiles. Unos ejemplos de aplicaci贸n concreta de los sistemas de membranas (Secciones 7.1, 7.2) son la resoluci贸n de problemas NP-completos en tiempo polinomial y la resoluci贸n de problemas de ordenaci贸n.This thesis deals with membrane systems with symbol objects as a theoretical framework of distributed parallel multiset processing systems.A halting computation can accept, generate or process a number, a vector or a word, so the system globally defines (by the results of all its computations) a set of numbers or a set of vectors or a set of words, (i.e., a language), or a function. The ability of these systems to solve particular problems is investigated, as well as their computational power, e.g., the language families defined by different classes of these systems are compared to the classical ones, i.e., regular, context-free, languages generated by extended tabled 0L systems, languages generated by matrix grammars without appearance checking, recursively enumerable languages, etc. Special attention is paid to communication of objects between the regions and to the ways of cooperation between the objects.An attempt to formalize the membrane systems is made (Section 3.4), and a software tool is constructed for the non-distributed cooperative variant, the configuration browser, i.e., a simulator, where the user chooses the next configuration among the possible ones and can go back. Different distributed models are considered. In the evolution-communication model (Chapter 4) rewriting-like rules are separated from transport rules. Proton pumping systems (Sections 4.8, 4.9) are a variant of the evolution-communication systems with a restricted way of cooperation. A special membrane computing model is a purely communicative one: the objects are moved together through a membrane. We study the computational power of membrane systems with symport/antiport of 2 or 3 objects (Chapter 5) and the computational power of membrane systems with a limited alphabet (Chapter 6).Determinism (Sections 4.7, 5.5, etc.) is a special property of computational systems; the question of whether this restriction reduces the computational power is addressed. The results on proton pumping systems can be carried over (Section 7.3) to the systems with bi-stable catalysts. Some particular examples of membrane systems applications are solving NP-complete problems in polynomial time, and solving the sorting problem

    35th Symposium on Theoretical Aspects of Computer Science: STACS 2018, February 28-March 3, 2018, Caen, France

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    Verifying polymer reaction networks using bisimulation

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    The Chemical Reaction Network model has been proposed as a programming language for molecular programming. Methods to implement arbitrary CRNs using DNA strand displacement circuits have been investigated, as have methods to prove the correctness of those or other implementations. However, the stochastic Chemical Reaction Network model is provably not deterministically Turing-universal, that is, it is impossible to create a stochastic CRN where a given output molecule is produced if and only if an arbitrary Turing machine accepts. A DNA stack machine that can simulate arbitrary Turing machines with minimal slowdown deterministically has been proposed, but it uses unbounded polymers that cannot be modeled as a Chemical Reaction Network. We propose an extended version of a Chemical Reaction Network that models unbounded linear polymers made from a finite number of monomers. This Polymer Reaction Network model covers the DNA stack machine, as well as copy-tolerant Turing machines and some examples from biochemistry. We adapt the bisimulation method of verifying DNA implementations of Chemical Reaction Networks to our model, and use it to prove the correctness of the DNA stack machine implementation. We define a subclass of single-locus Polymer Reaction Networks and show that any member of that class can be bisimulated by a network using only four primitives, suggesting a method of DNA implementation. Finally, we prove that deciding whether an implementation is a bisimulation is 螤鈦扳倐-complete, and thus undecidable in the general case, although it is tractable in many special cases of interest. We hope that the ability to model and verify implementations of Polymer Reaction Networks will aid in the rational design of molecular systems

    Verifying polymer reaction networks using bisimulation

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    The Chemical Reaction Network model has been proposed as a programming language for molecular programming. Methods to implement arbitrary CRNs using DNA strand displacement circuits have been investigated, as have methods to prove the correctness of those or other implementations. However, the stochastic Chemical Reaction Network model is provably not deterministically Turing-universal, that is, it is impossible to create a stochastic CRN where a given output molecule is produced if and only if an arbitrary Turing machine accepts. A DNA stack machine that can simulate arbitrary Turing machines with minimal slowdown deterministically has been proposed, but it uses unbounded polymers that cannot be modeled as a Chemical Reaction Network. We propose an extended version of a Chemical Reaction Network that models unbounded linear polymers made from a finite number of monomers. This Polymer Reaction Network model covers the DNA stack machine, as well as copy-tolerant Turing machines and some examples from biochemistry. We adapt the bisimulation method of verifying DNA implementations of Chemical Reaction Networks to our model, and use it to prove the correctness of the DNA stack machine implementation. We define a subclass of single-locus Polymer Reaction Networks and show that any member of that class can be bisimulated by a network using only four primitives, suggesting a method of DNA implementation. Finally, we prove that deciding whether an implementation is a bisimulation is 螤鈦扳倐-complete, and thus undecidable in the general case, although it is tractable in many special cases of interest. We hope that the ability to model and verify implementations of Polymer Reaction Networks will aid in the rational design of molecular systems
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