Membrane Computing Models: Implementations | springerprofessional.de

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2021 | Buch

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Membrane Computing Models: Implementations

verfasst von: Prof. Gexiang Zhang, Prof. Mario J. Pérez-Jiménez, Dr. Agustín Riscos-Núñez, Dr. Sergey Verlan, Dr. Savas Konur, Prof. Dr. Thomas Hinze, Prof. Marian Gheorghe

Verlag: Springer Singapore

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

The theoretical basis of membrane computing was established in the early 2000s with fundamental research into the computational power, complexity aspects and relationships with other (un)conventional computing paradigms. Although this core theoretical research has continued to grow rapidly and vigorously, another area of investigation has since been added, focusing on the applications of this model in many areas, most prominently in systems and synthetic biology, engineering optimization, power system fault diagnosis and mobile robot controller design. The further development of these applications and their broad adoption by other researchers, as well as the expansion of the membrane computing modelling paradigm to other applications, call for a set of robust, efficient, reliable and easy-to-use tools supporting the most significant membrane computing models. This work provides comprehensive descriptions of such tools, making it a valuable resource for anyone interested in membrane computing models.

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Inhaltsverzeichnis

Frontmatter

1. Introduction

Abstract

To offer a general view of this book with respect to aims, topics, ideas, organization, and layout, this chapter first provides an overview of membrane computing and then brief summaries of software and hardware implementation of P system models, respectively. Subsequently, the challenging problems of P systems implementation are discussed. Finally, a summary of this book is presented.

Gexiang Zhang, Mario J. Pérez-Jiménez, Agustín Riscos-Núñez, Sergey Verlan, Savas Konur, Thomas Hinze, Marian Gheorghe

2. P Systems Implementation on P-Lingua Framework

Abstract

Membrane computing is a broad and flexible paradigm, where many types of computing models have been conceived since the inception of the field—around 20 years ago. It was clear from its early days that a deep understanding of the dynamics of the computational devices of the discipline (so-called P systems) required the development of support simulation tools. Different approaches have been followed in this context, but a historical overview is out of scope here. This chapter mainly focuses on describing P-Lingua framework, probably the most widely used product for the specification and simulation of different types of P systems, consequently leading to a very prolific scientific production. A high-level tool for virtual experiments, MeCoSim, is also presented.

Gexiang Zhang, Mario J. Pérez-Jiménez, Agustín Riscos-Núñez, Sergey Verlan, Savas Konur, Thomas Hinze, Marian Gheorghe

3. Applications of Software Implementations of P Systems

Abstract

This chapter presents several representative applications implemented with P systems software simulators, including automatic design of cell-like and spiking neural P systems with P-Lingua, modelling real ecosystems with MeCoSim, and robot motion planning. The automatic design of P systems uses evolutionary computing approaches to heuristically select the best models satisfying the predefined requirements from a population of candidate P systems, where each P system is evaluated on the P-Lingua platform according to an evaluation function. The models for investigating population dynamics of real ecosystems are realized with MeCoSim. Robot motion planning is implemented by simulating rapidly exploring random trees algorithm within the P system framework.

Gexiang Zhang, Mario J. Pérez-Jiménez, Agustín Riscos-Núñez, Sergey Verlan, Savas Konur, Thomas Hinze, Marian Gheorghe

4. Infobiotics Workbench: An In Silico Software Suite for Computational Systems Biology

Abstract

This chapter presents the Infobiotics Workbench (IBW), an integrated software suite developed for computational systems biology. The tool is built upon stochastic P systems, a probabilistic extension of P systems, as modelling framework. The platform utilizes computer-aided modelling and analysis of biological systems through simulation, verification, and optimization. IBW allows modelling and analyzing not only cell-level behavior but also multicompartmental population dynamics. This enables comparing between macroscopic and mesoscopic interpretations of molecular interaction networks and investigating temporospatial phenomena in multicellular systems. These capabilities make IBW a useful, coherent, and comprehensive in silico tool for systems biology research.

Gexiang Zhang, Mario J. Pérez-Jiménez, Agustín Riscos-Núñez, Sergey Verlan, Savas Konur, Thomas Hinze, Marian Gheorghe

5. Molecular Physics and Chemistry in Membranes: The Java Environment for Nature-Inspired Approaches (JENA)

Abstract

Biological information processing is based on natural laws at a molecular level. Resulting principles make use of dedicated chemical reactions, mechanisms for transportation of biomolecules, and forces among molecules and their environment mainly induced by electric charges and by movement in local space. Since these interactions are typically located in a liquid surrounded by a membrane or a barrier, a dynamical molecular system emerges whose behavior follows the elementary rules of thermodynamics and molecular mechanics. For fine-grained modelling and simulation of processes and implementations of membrane computing, we strive for a corresponding software tool able to manage a multiple particle system in time and space at a medium level of abstraction and with no need of abstract parameter fitting. To this end, we provide the Java Environment for Nature-inspired Approaches (JENA) as a modular, configurable, and extendable platform toward a virtual laboratory and a virtual cell complementing more abstract and more idealized approaches in membrane computing. We give an introduction to JENA and its features and capabilities from the user’s perspective and from a technical point of view. Four illustrative case studies (chemical Lotka-Volterra oscillator, electrophoresis, centrifugation, neural signal transduction across synaptic cleft) demonstrate JENA’s practicability and descriptive capacity.

Gexiang Zhang, Mario J. Pérez-Jiménez, Agustín Riscos-Núñez, Sergey Verlan, Savas Konur, Thomas Hinze, Marian Gheorghe

6. P Systems Implementation on GPUs

Abstract

The performance of P system simulators is key when deploying these models in real applications, or when pushing the limits of efficiency. In this concern, HPC is the key technology to accelerate the simulation of P systems, and specially, GPU computing is a good alternative since they provide a highly parallel device with a shared memory system and a flexible programming. GPUs are currently the enabling technology for trending areas such as Deep Learning. So far, many GPU simulators have been developed using CUDA, the first general-purpose programming model for GPUs. In this chapter, we introduce the concepts behind GPU computing and a taxonomy of GPU-based simulators: generic, specific, and adaptive simulation.

Gexiang Zhang, Mario J. Pérez-Jiménez, Agustín Riscos-Núñez, Sergey Verlan, Savas Konur, Thomas Hinze, Marian Gheorghe

7. P Systems Implementation on FPGA

Abstract

This chapter presents different existing implementations of P systems using FPGA hardware. It gives the strong and the weak points of each implementation. A particular attention is given to the latest implementation of generalized numerical P systems that considers many advanced techniques. At the end of the chapter, a discussion about the challenges and the necessity of an FPGA implementation is performed.

Gexiang Zhang, Mario J. Pérez-Jiménez, Agustín Riscos-Núñez, Sergey Verlan, Savas Konur, Thomas Hinze, Marian Gheorghe

8. Applications of Hardware Implementation of P Systems

Abstract

It is a long-cherished wish to implement numerical P systems (NPS) on a parallel architecture so that its large-scale parallelism can be exploited to speedup computation tremendously. Field-programmable gate array (FPGA) is a reconfigurable hardware in which operations are triggered so synchronized by edge or level of activating signals, making it an eligible platform to implement NPS and its variant, enzymatic numerical P system (ENPS). In this chapter, (E)NPS-based robot controllers and path planning algorithm are implemented in FPGA, achieving a speedup of 10⁵ and 10⁴ order of magnitude compared to software simulation. FPGA hardened (E)NPS in this research can be regarded as a heterogeneous multicore processor since membranes inside work as processing units which possess different functions.

Gexiang Zhang, Mario J. Pérez-Jiménez, Agustín Riscos-Núñez, Sergey Verlan, Savas Konur, Thomas Hinze, Marian Gheorghe

9. Correction to: Membrane Computing Models: Implementations

Gexiang Zhang, Mario J. Pérez-Jiménez, Agustín Riscos-Núñez, Sergey Verlan, Savas Konur, Thomas Hinze, Marian Gheorghe

Backmatter

Titel: Membrane Computing Models: Implementations
verfasst von: Prof. Gexiang Zhang
Prof. Mario J. Pérez-Jiménez
Dr. Agustín Riscos-Núñez
Dr. Sergey Verlan
Dr. Savas Konur
Prof. Dr. Thomas Hinze
Prof. Marian Gheorghe
Copyright-Jahr: 2021
Verlag: Springer Singapore
Electronic ISBN: 978-981-16-1566-5
Print ISBN: 978-981-16-1565-8
DOI: https://doi.org/10.1007/978-981-16-1566-5

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