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.
Author(s): Gexiang Zhang, Mario J. Pérez-Jiménez, Agustín Riscos-Núñez, Sergey Verlan, Savas Konur, Thomas Hinze, Marian Gheorghe
Publisher: Springer
Year: 2021
Language: English
Pages: 301
City: Singapore
Foreword
Preface
Acknowledgments
Contents
Acronyms
1 Introduction
1.1 Membrane Computing Overview
1.2 Software Implementation of P Systems
1.3 Hardware Implementation of P Systems
1.4 Challenges of P Systems Implementation
1.5 Concluding Remarks
References
2 P Systems Implementation on P-Lingua Framework
2.1 Introduction
2.2 P-Lingua Language
2.2.1 P System Models
2.2.2 Membrane Structure
2.2.3 Initial Multisets
2.2.4 P System Rules
2.3 Simulation Algorithms
2.4 Membrane Computing Simulator (MeCoSim)
2.4.1 Primary goals
2.4.2 Main Functional Components
2.5 Conclusion
References
3 Applications of Software Implementations of P Systems
3.1 Introduction
3.2 Automatic Design of Cell-Like P Systems with P-Lingua
3.2.1 Preliminaries
3.2.1.1 Alphabet and Multisets
3.2.1.2 Rooted Tree
3.2.1.3 Cell-Like P System/Transition P System
3.2.2 Automatic Design of P Systems with an Elitist Genetic Algorithm
3.2.2.1 Problem Statement
3.2.2.2 Design Method
3.2.3 Automatic Design of P Systems with a Permutation Penalty Genetic Algorithm
3.3 Automatic Design of Spiking Neural P Systems with P-Lingua
3.4 Modelling Real Ecosystems with MeCoSim
3.4.1 Problem Description
3.5 Robot Motion Planning
3.5.1 Problem Definition
3.5.2 Path Planning for Mobile Robots
3.5.3 Rapidly-Exploring Random Tree (RRT) Algorithm
3.6 Conclusion
References
4 Infobiotics Workbench: An In Silico Software Suite for Computational Systems Biology
4.1 Introduction
4.2 Stochastic P Systems
4.3 Software Description
4.3.1 Simulation
4.3.2 Verification
4.3.3 Optimization
4.4 Case Studies
4.4.1 Pulse generator
4.4.2 Repressilator
4.5 KPWorkbench: A Qualitative Analysis Tool
4.6 Next-Generation Infobiotics for Synthetic Biology
4.7 Conclusion
References
5 Molecular Physics and Chemistry in Membranes: The Java Environment for Nature-Inspired Approaches (JENA)
5.1 Introduction
5.2 JENA at a Glance and Its Descriptive Capacity
5.2.1 Atoms, Ions, Molecules, and Particles
5.2.2 Vessels and Delimiters
5.2.3 Brownian Motion and Thermodynamics
5.2.4 Chemical Reactions by Effective Collisions and by Spontaneous Decay
5.2.5 Applying External Forces
5.2.6 Active Membranes and Dynamical Delimiters
5.2.7 Simulation, Monitoring, Logging, and Analyses
5.3 JENA Source Code Design
5.4 Selection of JENA Case Studies
5.4.1 Chemical Lotka-Volterra Oscillator
5.4.2 Electrophoresis
5.4.3 Centrifugation
5.4.4 Neural Signal Transduction Across Synaptic Cleft
5.5 Conclusions and Prospectives
References
6 P Systems Implementation on GPUs
6.1 Introduction
6.2 GPU Computing
6.2.1 The Graphics Processing Unit
6.2.2 CUDA Programming Model
6.2.3 GPU Architecture
6.2.4 Good Practices
6.3 Generic Simulations
6.3.1 Definition
6.3.2 Simulating P Systems with Active Membranes
6.3.2.1 Recognizer P Systems with Active Membranes
6.3.2.2 Simulation Algorithm
6.3.2.3 Sequential Simulator
6.3.2.4 Parallel Simulation on CUDA
6.3.2.5 Performance Comparative Analysis
6.3.3 Simulating Population Dynamics P Systems
6.3.3.1 Population Dynamics P Systems
6.3.3.2 Simulation Algorithm
6.3.3.3 Design of the Parallel Simulator
6.3.3.4 GPU Implementation of the DCBA Phases
6.3.3.5 Performance Results of the Simulator
6.4 Specific Simulations
6.4.1 Definition
6.4.2 Simulating a SAT Solution with Active Membrane P Systems
6.4.2.1 SAT Solution with Active Membranes
6.4.2.2 Sequential Simulator and Data Structures
6.4.2.3 Design of the GPU Simulator
6.4.2.4 Performance Analysis
6.4.3 Simulating a SAT Solution with Tissue P Systems
6.4.3.1 Recognizer Tissue P System with Cell Division
6.4.3.2 SAT Solution with Tissue P Systems
6.4.3.3 Sequential Simulation and Data Structure
6.4.3.4 Design of the Parallel Simulator
6.4.3.5 Performance Analysis
6.5 Adaptive Simulations
6.5.1 Definition
6.5.2 Simulating Population Dynamics P Systems
6.5.2.1 Analysis of Performance Results
6.6 Conclusions
References
7 P Systems Implementation on FPGA
7.1 Introduction
7.2 FPGA Hardware
7.3 Generalized Numerical P Systems (GNPS)
7.3.1 Formal Definition
7.3.2 Basic Variant
7.3.3 Historical Remarks
7.4 Implementing GNPS on FPGA
7.5 FPGA Implementations of Other Models of P Systems
7.5.1 Petreska and Teuscher Implementation
7.5.2 Nguyen Implementation
7.5.3 Quiros and Verlan Implementation
7.5.4 Comments
7.6 Discussion
7.7 Conclusion
References
8 Applications of Hardware Implementation of P Systems
8.1 Introduction
8.2 Robot Membrane Controllers with FPGA Implementation
8.2.1 Numerical P Systems-Based Membrane Controllers on FPGA
8.2.2 Enzymatic Numerical P Systems (ENPS)-Based Membrane Controllers on FPGA
8.2.3 GNPS-Based Membrane Controllers on FPGA
8.3 Robot Path Planning with FPGA Implementation
8.3.1 RRT Algorithm
8.3.2 Arithmetic Units Design
8.3.3 Enzymatic Numerical P System Rapid-Exploring Random Tree Register Transfer Level (ENPS-RRT RTL) Model Design
8.3.4 ENPS-RRT on FPGA
8.4 Conclusion
References
Index
