On discrete models and immunological algorithms for protein structure prediction

Abstract  Discrete models for protein structure prediction embed the protein amino acid sequence into a discrete spatial structure,
usually a lattice, where an optimal tertiary structure is predicted on the basis of simple assumptions relati…

Abstract  

Discrete models for protein structure prediction embed the protein amino acid sequence into a discrete spatial structure,
usually a lattice, where an optimal tertiary structure is predicted on the basis of simple assumptions relating to the hydrophobic–hydrophilic
character of amino acids in the sequence and to relevant interactions for free energy minimization. While the prediction problem
is known to be NP complete even in the simple setting of Dill’s model with a 2D-lattice, a variety of bio-inspired algorithms
for this problem have been proposed in the literature. Immunological algorithms are inspired by the kind of optimization that
immune systems perform when identifying and promoting the replication of the most effective antibodies against given antigens.
A quick, state-of-the-art survey of discrete models and immunological algorithms for protein structure prediction is presented
in this paper, and the main design and performance features of an immunological algorithm for this problem are illustrated
in a tutorial fashion.

  • Content Type Journal Article
  • Pages 91-102
  • DOI 10.1007/s11047-010-9196-y
  • Authors
    • Vincenzo Cutello, Department of Mathematics and Computer Science, University of Catania, Catania, Italy
    • Giuseppe Morelli, Department of Mathematics and Computer Science, University of Catania, Catania, Italy
    • Giuseppe Nicosia, Department of Mathematics and Computer Science, University of Catania, Catania, Italy
    • Mario Pavone, Department of Mathematics and Computer Science, University of Catania, Catania, Italy
    • Giuseppe Scollo, Department of Mathematics and Computer Science, University of Catania, Catania, Italy

GP for self-replication in cellular automata

A new piece on the use of genetic programming for self-replication in cellular automata, by Zhijian Pan and James A. Reggia, has been published in the journal Artificial Life. Previous work on this topic using non-GP genetic algorithms was intriguing, …

A new piece on the use of genetic programming for self-replication in cellular automata, by Zhijian Pan and James A. Reggia, has been published in the journal Artificial Life. Previous work on this topic using non-GP genetic algorithms was intriguing, but I always thought that GP could produce even more interesting results. This new article appears to bear that out.

Output concepts for accelerated Turing machines

Abstract  The accelerated Turing machine (ATM) is the work-horse of hypercomputation. In certain cases, a machine having run through
a countably infinite number of steps is supposed to have decided some interesting question such as the Twin …

Abstract  

The accelerated Turing machine (ATM) is the work-horse of hypercomputation. In certain cases, a machine having run through
a countably infinite number of steps is supposed to have decided some interesting question such as the Twin Prime conjecture.
One is, however, careful to avoid unnecessary discussion of either the possible actual use by such a machine of an infinite
amount of space, or the difficulty (even if only a finite amount of space is used) of defining an outcome for machines acting
like Thomson’s lamp. It is the authors’ impression that insufficient attention has been paid to introducing a clearly defined
counterpart for ATMs of the halting/non-halting dichotomy for classical Turing computation. This paper tackles the problem
of defining the output, or final message, of a machine which has run for a countably infinite number of steps. Non-standard
integers appear quite useful in this regard and we describe several models of computation using filters.

  • Content Type Journal Article
  • DOI 10.1007/s11047-010-9197-x
  • Authors
    • Petrus H. Potgieter, Department of Decision Sciences, University of South Africa (Unisa), P.O. Box 392, Pretoria, 0003 South Africa
    • Elemér E. Rosinger, Department of Mathematics and Applied Mathematics, University of Pretoria, Pretoria, 0002 South Africa

A software tool for generating graphics by means of P systems

Abstract  The hand-made graphical representation of the configuration of a P system becomes a hard task when the number of membranes
and objects increases. In this paper we present a new software tool, called JPLANT, for computing and repres…

Abstract  

The hand-made graphical representation of the configuration of a P system becomes a hard task when the number of membranes
and objects increases. In this paper we present a new software tool, called JPLANT, for computing and representing the evolution
of a P system model with membrane creation. We also present some experiments performed with JPLANT and point out new lines
for the research in computer graphics with membrane systems.

  • Content Type Journal Article
  • Pages 879-890
  • DOI 10.1007/s11047-010-9198-9
  • Authors
    • Elena Rivero-Gil, Departamento de Ciencias de la Computación e Inteligencia Artificial, Escuela Técnica Superior de Ingeniería Informática, Universidad de Sevilla, Avda. Reina Mercedes, s/n., 41012 Sevilla, Spain
    • Miguel Á. Gutiérrez-Naranjo, Departamento de Ciencias de la Computación e Inteligencia Artificial, Escuela Técnica Superior de Ingeniería Informática, Universidad de Sevilla, Avda. Reina Mercedes, s/n., 41012 Sevilla, Spain
    • Álvaro Romero-Jiménez, Departamento de Ciencias de la Computación e Inteligencia Artificial, Escuela Técnica Superior de Ingeniería Informática, Universidad de Sevilla, Avda. Reina Mercedes, s/n., 41012 Sevilla, Spain
    • Agustín Riscos-Núñez, Departamento de Ciencias de la Computación e Inteligencia Artificial, Escuela Técnica Superior de Ingeniería Informática, Universidad de Sevilla, Avda. Reina Mercedes, s/n., 41012 Sevilla, Spain