An Application of Genetic Algorithms to Membrane Computing

The process of designing a P system in order to perform a task is a hard job. The researcher has often only an approximate idea of the design, but finding the exact description of the rules is a heavy hand-made work. In this paper we introduce PSystemEvolver, an evolutionary algorithm based on generative encoding, that could help to design a P system to perform a specific task. We illustrate the use of PSystemEvolver with a simple mathematical problem: the computation of squared numbers.Ministerio de Ciencia e Innovación TIN2008-04487-EMinisterio de Ciencia e Innovación TIN-2009-13192Junta de Andalucía P08-TIC-0420

Applying Membrane Systems in Food Engineering

Food engineering deals with manufacturing, packaging and distributing systems for drug and food products. In this work, we discuss about the applicability of membrane systems to model environmental conditions and their e ects on the produces during storage of fresh fruits and vegetables. In particular, we are interested in abstract molecular interactions that occur between produce, lm and surrounding atmosphere factors involved in fresh fruit and vegetable package designs. We present a basic implementation to simulate the dynamical behaviour of these systems, due to gas exchanges and temperature uctuations. Additionally, we reveal the bene ts of this modelling approach and suggest some extensions as future directions to be considered

Evolving L-systems to capture protein structure native conformations

A protein is a linear chain of amino acids that folds into a unique functional structure, called its native state. In this state, proteins show repeated substructures like alpha helices and beta sheets. This suggests that native structures may be captured by the formalism known as Lindenmayer systems (L-systems). In this paper an evolutionary approach is used as the inference procedure for folded structures on simple lattice models. The algorithm searches the space of L-systems which are then executed to obtain the phenotype, thus our approach is close to Grammatical Evolution. The problem is to find a set of rewriting rules that represents a target native structure on the lattice model. The proposed approach has produced promising results for short sequences. Thus the foundations are set for a novel encoding based on L-systems for evolutionary approaches to both the Protein Structure Prediction and Inverse Folding Problems

N.: Evolving l-systems to capture protein structure native conformations

Abstract. A protein is a linear chain of amino acids that folds into a unique functional structure, called its native state. In this state, proteins show repeated substructures like alpha helices and beta sheets. This suggests that native structures may be captured by the formalism known as Lindenmayer systems (L-systems). In this paper an evolutionary approach is used as the inference procedure for folded structures on simple lattice models. The algorithm searches the space of Lsystems which are then executed to obtain the phenotype, thus our approach is close to Grammatical Evolution. The problem is to find a set of rewriting rules that represents a target native structure on the lattice model. The proposed approach has produced promising results for short sequences. Thus the foundations are set for a novel encoding based on L-systems for evolutionary approaches to both the Protein Structure Prediction and Inverse Folding Problems.

Particular autocatalytic network.

<p>(A) Original network presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045772#pone.0045772-Contreras1" target="_blank">[13]</a> and (B) a generalization, used for our analysis. represent species. Arrows indicate reactions between the species, arrows ending with a circle denote catalysis (to reduce its complexity, in Panel (B) the catalysts but not the arrows are shown). The parameters and represent reaction rates. Observe that and are the external supplies and waste species, and represent transport molecules, whereas conform a cycle, i.e. .</p

Number of species of the reactive organizations.

<p>We number the reactive organizations by size in ascending order from to .</p