Germán Terrazas

An environment aware p-system model of quorum sensing

“Quorum Sensing” has been identified as one of the most consequential microbiology discoveries of the last 10 years. Using Quorum Sensing bacterial colonies synchronize gene expression and phenotype change allowing them, among other things, to protect their niche, coordinate host invasion and bio-film formation. In this contribution we briefly describe the elementary microbiology background and present a P-systems based model for Quorum Sensing which includes environmental rules and a topological representation.

Evolving tiles for automated self-assembly design

Self-assembly is a distributed, asynchronous mechanism that is pervasive across natural systems where hierarchical complex structures are built from the bottom-up. The lack of a centralised master plan, no external intervention, and preprogrammed interactions among entities are within its most relevant and technologically appealing properties. This paper tackles the self-assembly Wang tiles designability problem by means of artificial evolution. This research is centred in the use of tiles that are extended with rotation and probabilistic motion, and an evolutionary algorithm using the Morphological Image Analyses method as a fitness function. The obtained results support this approach as a successful engineering mechanism for the computer-aided design of self-assembled patterns.

Exploring programmable self-assembly in non-DNA based molecular computing

Self-assembly is a phenomenon observed in nature at all scales where autonomous entities build complex structures, without external influences nor centralised master plan. Modelling such entities and programming correct interactions among them is crucial for controlling the manufacture of desired complex structures at the molecular and supramolecular scale. This work focuses on a programmability model for non DNA-based molecules and complex behaviour analysis of their self-assembled conformations. In particular, we look into modelling, programming and simulation of porphyrin molecules self-assembly and apply Kolgomorov complexity-based techniques to classify and assess simulation results in terms of information content. The analysis focuses on phase transition, clustering, variability and parameter discovery which as a whole pave the way to the notion of complex systems programmability.

Thematic issue on advances in nature inspired cooperative strategies for optimization

Researchers have long turn to nature as a source of inspiration for new optimization techniques, developing metaphors that mimic natural behaviour or processes that can be used to solve real-world problems. Such techniques—e.g., from genetic algorithms emulating Darwin’s principle of natural evolution, ant colony optimization using a population of artificial ants that collaborate by means of pheromone to artificial immune systems inspired by the principals and function of the (natural) immune system, to mention a few—have proven successful in different optimization problems, which in many cases produce state-of-the-art solutions. This thematic issue is the result of an open call for contributions. At the same time, authors of selected papers were invited to submit significantly extended versions of papers presented at the recent International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO) held on September 2013.1 The first paper in this thematic issue is presented by Goñi-Moreno, where the design of biological logic circuits is explored. Building up on the recent advances of genetic manipulation, the paper explores how engineered DNA sequences and the connectivity of molecular compo-

Towards a big data platform for managing machine generated data in the cloud

Industry 4.0 proposes the integration of the new generation of ICT solutions for the monitoring, adaptation, simulation, and optimisation of factories. With the democratization of sensors and actuators, factories and machine tools can now be sensorized and the data generated by these devices can be exploited, for instance, to optimize the utilization of the machines as well as their operation and maintenance. However, analysing the vast amount of data generated is resource demanding both in term of computing power and network bandwidth, thus requiring highly scalable solutions. This paper presents a novel big data platform for the management of machine generated data in the cloud. It brings together standard open source technologies which can be adapted to and deployed on different cloud infrastructures, hence reducing costs, minimising deployment difficulty and providing on-demand access to a virtually infinite set of computing, storage and network resources.

Complexity measurement based on information theory and kolmogorov complexity

In the past decades many definitions of complexity have been proposed. Most of these definitions are based either on Shannons information theory or on Kolmogorov complexity; these two are often compared, but very few studies integrate the two ideas. In this article we introduce a new measure of complexity that builds on both of these theories. As a demonstration of the concept, the technique is applied to elementary cellular automata and simulations of the self-organization of porphyrin molecules.

A genotype-phenotype-fitness assessment protocol for evolutionary self-assembly Wang tiles design

In a previous work we have reported on the evolutionary design optimisation of self-assembling Wang tiles capable of arranging themselves together into a target structure. Apart from the significant findings on how self-assembly is achieved, nothing has been yet said about the efficiency by which individuals were evolved. Specially in light that the mapping from genotype to phenotype and from this to fitness is clearly a complex, stochastic and non-linear relationship. One of the most common procedures would suggest running many experiments for different configurations followed by a fitness comparison, which is not only time-consuming but also inaccurate for such intricate mappings. In this paper we aim to report on a complementary dual assessment protocol to analyse whether our genetic algorithm, using morphological image analyses as fitness function, is an effective methodology. Thus, we present here fitness distance correlation to measure how effectively the fitness of an individual correlates to its genotypic distance to a known optimum, and introduce clustering as a mechanism to verify how the objective function can effectively differentiate between dissimilar phenotypes and classify similar ones for the purpose of selection.

Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics

The current research trends on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically generate a good performing heuristic for specific problems, that is, the input to the algorithm are problems and the output are problem-tailored heuristics. This can be done, for example, by automatically selecting and combining different low-level heuristics into a problem specific and effective strategy. Thus, hyper-heuristics raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem in hand. Some approaches like genetic programming have been proposed for this. In this paper, we report on an alternative methodology that sheds light on simple methodologies that efficiently cooperate by means of local interactions. These entities are seen as building blocks, the combination of which is employed for the automated manufacture of good performing heuristic search strategies.We present proof-of-concept results of applying this methodology to instances of the well-known symmetric TSP. The goal here is to demonstrate feasibility rather than compete with state of the art TSP solvers. This TSP is chosen only because it is an easy to state and well known problem.

Chapter 13 Automated Self-Assembling Programming

In this chapter we explore various facets of the interplay between natural computing and self-assembly as they pertain to automated self-assembling programming. In particular we focus on two complementary research issues, namely, the automated control and programming of model systems that self-assemble into specific configurations and, on the other hand, the use of self-assembling metaphors and model systems to implement new ways of performing computation. These “two sides of the self-assembly/computation coin” are tightly linked together, and advances in one of them could help to pave the way for advances in the other. Thus, we hope that this chapter will serve as an inspiration to other computer scientists to immerse themselves in the wealth of opportunities and problems begging for solutions [L. Adleman, Q. Cheng, A. Goel, M. Huang, D. Kempe, P. Moisset de Espanes, P.W.K. Rothemund, Combinatorial optimization problems in self-assembly, in: Proceedings of the Annual ACM Symposium on Theory of Computing (STOC), ACM Press, 2002], that self-assembly related research presents.

Membrane computing — current results and future problems

In the last decade and especially after Adlemans experiment [1] a number of computational paradigms, inspired or gleaned from biochemical phenomena, are becoming of growing interest building a wealth of models, called generically Molecular Computing. New advances in, on the one hand, molecular and theoretical biology, and on the other hand, mathematical and computational sciences promise to make it possible in the near future to have accurate systemic models of complex biological phenomena. Recent advances in cellular Biology led to new models, hierarchically organised, defining a new emergent research area called Cellular Computing.