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.  

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.  

Chapter 1 Introduction

1.1  General introduction of P systems implementation

1.2  Challenging problems of P systems implementation

1.3  Review of software implementations

1.4  Review of hardware implementations

1.5 Other implementation platforms

 

Chapter 2 P systems Implementation on P-Lingua framework

2.1  Overview

2.2  P-Lingua language for P systems variants

2.3  Simulation algorithms

2.4  MeCoSim

 

Chapter 3 Software implementation for P systems

3.1  Automatic design of cell-like P systems with P-Lingua

3.2  Automatic design of spiking neural P systems with P-Lingua

3.3  Modelling real ecosystems with MeCoSim

3.4  Robot motion planning

 

Chapter 4 Infobiotics Workbench - In Silico Software Suite for Computational Biology

4.1  Introduction

4.2  Stochastic P Systems

4.3  Software Description

4.3.1 Modelling

4.3.2 Simulation

4.3.3 Verification

4.3.4 Parameter Optimization

4.4  Case Studies

4.5  Next Generation Infobiotics

4.5.1 Prediction-based stochastic simulations

4.5.2 High-performance simulation and verification

4.5.3 Biocompilation

4.6  Conclusions and discussions

 

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

5.1  Motivation and Introduction

5.2  JENA at a Glance

5.3  JENA Descriptive Capacity

5.4  JENA Source Code Design

5.5  Selection of JENA Case Studies

 

Chapter 6 P systems Implementation on CUDA

6.1  Overview

6.2  Specific simulations

6.3  Generic simulations

6.4  Adaptative simulations

 

Chapter 7 P systems Implementation on FPGA

7.1  Introduction

7.2  FPGA Hardware

7.3  Generalized Numerical P systems (GNPS)

7.4  Implementing GNPS on FPGA

7.5  FPGA implementations of other models of P systems

7.6  Discussion

 

Chapter 8 Hardware implementations and applications

8.1  Knapsack problems with CUDA implementation

8.2  Robot membrane controllers with FPGA implementation

8.3  Robot path planning with FPGA implementation

8.4  Image processing with FPGA implementation