Automatic design of deterministic and non-halting membrane systems by tuning syntactical ingredients

To solve the programmability issue of membrane computing models, the automatic design of membrane systems is a newly initiated and promising research direction. In this paper, we propose an automatic design method, Permutation Penalty Genetic Algorithm (PPGA), for a deterministic and non-halting membrane system by tuning membrane structures, initial objects and evolution rules. The main ideas of PPGA are the introduction of the permutation encoding technique for a membrane system, a penalty function evaluation approach for a candidate membrane system and a genetic algorithm for evolving a population of membrane systems toward a successful one fulfilling a given computational task. Experimental results show that PPGA can successfully accomplish the automatic design of a cell-like membrane system for computing the square of n ( n >/= 1 is a natural number) and can find the minimal membrane systems with respect to their membrane structures, alphabet, initial objects, and evolution rules for fulfilling the given task. We also provide the guidelines on how to set the parameters of PPGA

GUBS, a Behavior-based Language for Open System Dedicated to Synthetic Biology

In this article, we propose a domain specific language, GUBS (Genomic Unified Behavior Specification), dedicated to the behavioral specification of synthetic biological devices, viewed as discrete open dynamical systems. GUBS is a rule-based declarative language. By contrast to a closed system, a program is always a partial description of the behavior of the system. The semantics of the language accounts the existence of some hidden non-specified actions possibly altering the behavior of the programmed device. The compilation framework follows a scheme similar to automatic theorem proving, aiming at improving synthetic biological design safety.Comment: In Proceedings MeCBIC 2012, arXiv:1211.347

A Membrane-Inspired Evolutionary Algorithm with a Population P System and its Application to Distribution System Recon guration

This paper develops a membrane-inspired evolutionary algorithm, PSMA, which is designed by using a population P system and a quantum-inspired evolutionary algorithm (QIEA). We use a population P system with three cells to organize three types of QIEAs, where communications between cells are performed at the level of genes, instead of the level of individuals reported in the existing membrane algorithms in the literature. Knapsack problems are applied to discuss the parameter setting and to test the effectiveness of PSMA. Experimental results show that PSMA is superior to four representative QIEAs and our previous work with respect to the quality of solutions and the elapsed time. We also use PSMA to solve the optimal distribution system reconfiguration problem in power systems for minimizing the power loss.Junta de Andalucía P08-TIC-04200Ministerio de Ciencia e Innovación TIN-2009-1319

A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

Biomimetic Engineering

Humankind is a privileged animal species for many reasons. A remarkable one is its ability to conceive and manufacture objects. Human industry is indeed leading the various winning strategies (along with language and culture) that has permitted this primate to extraordinarily increase its life expectancy and proliferation rate. (It is indeed so successful, that it now threatens the whole planet.) The design of this industry kicks off in the brain, a computing machine particularly good at storing, recognizing and associating patterns. Even in a time when human beings tend to populate non-natural, man-made environments, the many forms, colorings, textures and behaviors of nature continuously excite our senses and blend in our thoughts, even more deeply during childhood. Then, it would be exaggerated to say that Biomimetics is a brand new strategy. As long as human creation is based on previously acquired knowledge and experiences, it is not surprising that engineering, the arts, and any form of expression, is influenced by nature’s way to some extent. The design of human industry has evolved from very simple tools, to complex engineering devices. Nature has always provided us with a rich catalog of excellent materials and inspiring designs. Now, equipped with new machinery and techniques, we look again at Nature. We aim at mimicking not only its best products, but also its design principles. Organic life, as we know it, is indeed a vast pool of diversity. Living matter inhabits almost every corner of the terrestrial ecosphere. From warm open-air ecosystems to the extreme conditions of hot salt ponds, living cells have found ways to metabolize the sources of energy, and get organized in complex organisms of specialized tissues and organs that adapt themselves to the environment, and can modify the environment to their own needs as well. Life on Earth has evolved such a diverse portfolio of species that the number of designs, mechanisms and strategies that can actually be abstracted is astonishing. As August Krogh put it: "For a large number of problems there will be some animal of choice, on which it can be most conveniently studied". The scientific method starts with a meticulous observation of natural phenomena, and humans are particularly good at that game. In principle, the aim of science is to understand the physical world, but an observer’s mind can behave either as an engineer or as a scientist. The minute examination of the many living forms that surround us has led to the understanding of new organizational principles, some of which can be imported in our production processes. In practice, bio-inspiration can arise at very different levels of observation: be it social organization, the shape of an organism, the structure and functioning of organs, tissular composition, cellular form and behavior, or the detailed structure of molecules. Our direct experience of the wide portfolio of species found in nature, and their particular organs, have clearly favored that the initial models would come from the organism and organ levels. But the development of new techniques (on one hand to observe the micro- and nanostructure of living beings, and on the other to simulate the complex behavior of social communities) have significantly extended the domain of interest

Evolving cell models for systems and synthetic biology

This paper proposes a new methodology for the automated design of cell models for systems and synthetic biology. Our modelling framework is based on P systems, a discrete, stochastic and modular formal modelling language. The automated design of biological models comprising the optimization of the model structure and its stochastic kinetic constants is performed using an evolutionary algorithm. The evolutionary algorithm evolves model structures by combining different modules taken from a predefined module library and then it fine-tunes the associated stochastic kinetic constants. We investigate four alternative objective functions for the fitness calculation within the evolutionary algorithm: (1) equally weighted sum method, (2) normalization method, (3) randomly weighted sum method, and (4) equally weighted product method. The effectiveness of the methodology is tested on four case studies of increasing complexity including negative and positive autoregulation as well as two gene networks implementing a pulse generator and a bandwidth detector. We provide a systematic analysis of the evolutionary algorithm’s results as well as of the resulting evolved cell models

The GPU on the simulation of cellular computing models

Membrane Computing is a discipline aiming to abstract formal computing models, called membrane systems or P systems, from the structure and functioning of the living cells as well as from the cooperation of cells in tissues, organs, and other higher order structures. This framework provides polynomial time solutions to NP-complete problems by trading space for time, and whose efficient simulation poses challenges in three different aspects: an intrinsic massively parallelism of P systems, an exponential computational workspace, and a non-intensive floating point nature. In this paper, we analyze the simulation of a family of recognizer P systems with active membranes that solves the Satisfiability problem in linear time on different instances of Graphics Processing Units (GPUs). For an efficient handling of the exponential workspace created by the P systems computation, we enable different data policies to increase memory bandwidth and exploit data locality through tiling and dynamic queues. Parallelism inherent to the target P system is also managed to demonstrate that GPUs offer a valid alternative for high-performance computing at a considerably lower cost. Furthermore, scalability is demonstrated on the way to the largest problem size we were able to run, and considering the new hardware generation from Nvidia, Fermi, for a total speed-up exceeding four orders of magnitude when running our simulations on the Tesla S2050 server.Agencia Regional de Ciencia y Tecnología - Murcia 00001/CS/2007Ministerio de Ciencia e Innovación TIN2009–13192Ministerio de Ciencia e Innovación TIN2009-14475-C04European Commission Consolider Ingenio-2010 CSD2006-0004