Finite Models of Splicing and Their Complexity

Durante las dos últimas décadas ha surgido una colaboración estrecha entre informáticos, bioquímicos y biólogos moleculares, que ha dado lugar a la investigación en un área conocida como la computación biomolecular. El trabajo en esta tesis pertenece a este área, y estudia un modelo de cómputo llamado sistema de empalme (splicing system). El empalme es el modelo formal del corte y de la recombinación de las moléculas de ADN bajo la influencia de las enzimas de la restricción.Esta tesis presenta el trabajo original en el campo de los sistemas de empalme, que, como ya indica el título, se puede dividir en dos partes. La primera parte introduce y estudia nuevos modelos finitos de empalme. La segunda investiga aspectos de complejidad (tanto computacional como descripcional) de los sistema de empalme. La principal contribución de la primera parte es que pone en duda la asunción general que una definición finita, más realista de sistemas de empalme es necesariamente débil desde un punto de vista computacional. Estudiamos varios modelos alternativos y demostramos que en muchos casos tienen más poder computacional. La segunda parte de la tesis explora otro territorio. El modelo de empalme se ha estudiado mucho respecto a su poder computacional, pero las consideraciones de complejidad no se han tratado apenas. Introducimos una noción de la complejidad temporal y espacial para los sistemas de empalme. Estas definiciones son utilizadas para definir y para caracterizar las clases de complejidad para los sistemas de empalme. Entre otros resultados, presentamos unas caracterizaciones exactas de las clases de empalme en términos de clases de máquina de Turing conocidas. Después, usando una nueva variante de sistemas de empalme, que acepta lenguajes en lugar de generarlos, demostramos que los sistemas de empalme se pueden usar para resolver problemas. Por último, definimos medidas de complejidad descriptional para los sistemas de empalme. Demostramos que en este respecto los sistemas de empalme finitos tienen buenas propiedades comparadosOver the last two decades, a tight collaboration has emerged between computer scientists, biochemists and molecular biologists, which has spurred research into an area known as DNAComputing (also biomolecular computing). The work in this thesis belongs to this field, and studies a computational model called splicing system. Splicing is the formal model of the cutting and recombination of DNA molecules under the influence of restriction enzymes.This thesis presents original work in the field of splicing systems, which, as the title already indicates, can be roughly divided into two parts: 'Finite models of splicing' on the onehand and 'their complexity' on the other. The main contribution of the first part is that it challenges the general assumption that a finite, more realistic definition of splicing is necessarily weal from a computational point of view. We propose and study various alternative models and show that in most cases they have more computational power, often reaching computational completeness. The second part explores other territory. Splicing research has been mainly focused on computational power, but complexity considerations have hardly been addressed. Here we introduce notions of time and space complexity for splicing systems. These definitions are used to characterize splicing complexity classes in terms of well known Turing machine classes. Then, using a new accepting variant of splicing systems, we show that they can also be used as problem solvers. Finally, we study descriptional complexity. We define measures of descriptional complexity for splicing systems and show that for representing regular languages they have good properties with respect to finite automata, especially in the accepting variant

Complexity theory for splicing systems

This paper proposes a notion of time complexity in splicing systems. The time complexity of a splicing system at length n is defined to be the smallest integer t such that all the words of the system having length n are produced within t rounds. For a function t from the set of natural numbers to itself, the class of languages with splicing system time complexity t ( n ) is denoted by SPLTIME [ f ( n ) ] . This paper presents fundamental properties of SPLTIME and explores its relation to classes based on standard computational models, both in terms of upper bounds and in terms of lower bounds. As to upper bounds, it is shown that for any function t ( n ) SPLTIME [ t ( n ) ] is included in 1 - NSPACE [ t ( n ) ] ; i.e., the class of languages accepted by a t ( n ) -space-bounded non-deterministic Turing machine with one-way input head. Expanding on this result, it is shown that 1 - NSPACE [ t ( n ) ] is characterized in terms of splicing systems: it is the class of languages accepted by a t ( n ) -space uniform family of extended splicing systems having production time O ( t ( n ) ) with the additional property that each finite automaton appearing in the family of splicing systems has at most a constant number of states. As to lower bounds, it is shown that for all functions t ( n ) ≥ log n , all languages accepted by a pushdown automaton with maximal stack height t ( | x | ) for a word x are in SPLTIME [ t ( n ) ] . From this result, it follows that the regular languages are in SPLTIME [ O ( log n ) ] and that the context-free languages are in SPLTIME [ O ( n ) ]

Complexity Theory for Splicing Systems

This paper proposes a notion of time complexity in splicing systems and presents fundamental properties of SPLTIME, the class of languages with splicing system time complexity t(n). Its relations to classes based on standard computational models are explored. It is shown that for any function t(n), SPLTIME[t(n)] is included in 1NSPACE[t(n)]. Expanding on this result, 1NSPACE[t(n)] is characterized in terms of splicing systems: it is the class of languages accepted by a t(n)-space uniform family of extended splicing systems having production time O(t(n)) with regular rules described by finite automata with at most a constant number of states. As to lower bounds, it is shown that for all functions t(n) ≥ logn, all languages accepted by a pushdown automaton with maximal stack height t(|x|) for a word x are in SPLTIME[t(n)]. From this result, it follows that the regular languages are in SPLTIME[O(log(n))] and that the context-free languages are in SPLTIME[O(n)]