LCS & GBML Central

An emerging trend in DNA computing consists of the algorithmic analysis of new molecular biology technologies, and in general
of more effective tools to tackle computational biology problems. An algorithmic understanding of the interaction between
DNA molecules becomes the focus of some research which was initially addressed to solve mathematical problems by processing
data within biomolecules. In this paper a novel mechanism of DNA recombination is discussed, that turned out to be a good
implementation key to develop new procedures for DNA manipulation (Franco et al., DNA extraction by cross pairing PCR, 2005; Franco et al., DNA recombination by XPCR, 2006; Manca and Franco, Math Biosci 211:282–298, 2008). It is called XPCR as it is a variant of the polymerase chain reaction (PCR), which was a revolution in molecular biology
as a technique for cyclic amplification of DNA segments. A few DNA algorithms are proposed, that were experimentally proven
in different contexts, such as, mutagenesis (Franco, Biomolecular computing—combinatorial algorithms and laboratory experiments,
2006), multiple concatenation, gene driven DNA extraction (Franco et al., DNA extraction by cross pairing PCR, 2005), and generation of DNA libraries (Franco et al., DNA recombination by XPCR, 2006), and some related ongoing work is outlined.

Self-assembly is the process by which objects aggregate independently and form complex structures. One of the theoretical
frameworks in which the process of self-assembly can be embedded and formally studied is that of tile systems. A Wang tile
is a square unit, with glues on its edges, attaching to other tiles which have matching glues, and forming larger and larger
structures. In this paper we concentrate over two basic, but essential, self-assembling structures done by Wang tiles. The
first one, called ribbon, is a non-self-crossing wire-like structure, in which successive tiles are adjacent along an edge,
and where tiles are glued to their predecessor and successor by use of matching glues. The second one, called zipper, is a
similar contiguous structure, only that here, all touching tiles must have matching glues on their abutting edges, independently
of their position in the structure. In case of Wang tiles, it has been shown that these two structures are equivalent. Here
we generalize this result for the case when the tiles have eight glues, four on their edges and four on their corners. Thus
we show that an eight neighborhood dependency, namely the Moore neighborhood, can be simulated by a quasi-linear dependency.

Differential Evolution is known for its simplicity and effectiveness as an evolutionary optimizer. In recent years, many researchers
have focused on the exploration of Differential Evolution (DE). The objective of this paper is to show the evolutionary and
population dynamics for the empirical testing on 3-Parents Differential Evolution (3PDE) for unconstrained function optimization
(Teng et al. 2007). In this paper, 50 repeated evolutionary runs for each of 20 well-known benchmarks were carried out to test the proposed
algorithms against the original 4-parents DE algorithm. As a result of the observed evolutionary dynamics, 3PDE-SAF performed
the best among the preliminary proposed algorithms that included 3PDE-SACr and 3PDE-SACrF. Subsequently, 3PDE-SAF is chosen
for the self-adaptive population size for testing dynamic population sizing methods using the absolute (3PDE-SAF-Abs) and
relative (3PDE-SAF-Rel) population size encodings. The final result shows that 3PDE-SAF-Rel produced a better performance
and convergence overall compared to all the other proposed algorithms, including the original DE. In terms of population dynamics,
the population size in 3PDE-SAF-Abs exhibited disadvantageously high dynamics that caused less efficient results. On the other
hand, the population size in 3PDE-SAF-Rel was observed to be approximately constant at ten times the number of variables being
optimized, hence giving a better and more stable performance.

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.