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P systems: A Modelling Language

This paper discusses the innovative approach of using P-systems to model bio-communities, focusing on the behavioral dynamics of social insects, such as ants and bees. By representing biological processes through state machines, we explore how these models can aid in understanding complex interactions within communities. The work emphasizes the need for robust modeling frameworks and examines the implications of biological rules on computational systems. By integrating empirical research with bio-inspired computing, we aim to enhance methodologies for simulating biological entities.

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P systems: A Modelling Language

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  1. P systems: A Modelling Language Marian Gheorghe Department of Computer Science University of Sheffield Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  2. Summary Modelling bio-communities State machines & P systems Experiments P systems – modelling paradigm Future work Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  3. What is a model? A simplified description of a complex entity or process www.cogsci.princeton.edu/cgi-bin/webwn A representation of a set of components of a process, system, or subject area, generally developed for understanding, analysis, improvement, and/or replacement of the process www.ichnet.org/glossary.htm A representation of reality used to simulate a process, understand a situation, predict an outcome, or analyze a problem www.epa.gov/maia/html/glossary.html Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  4. What to model? Bio-communities: social insects (ants, bees, wasps), bacterium communities, cells Component description/behaviour: structure, rules, Interactions: type, dynamicity Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  5. Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel Integrative Bio-research Testing – Specifications Assumptions – Requirements Robust biosystem rules General biological theory Abstract Modelling Empirical Research Verification Bioinspired computing Holistic view Parameters Hypotheses

  6. Modelling Bio-Communities Multi-agent systems: social insect communities provide an accessible model of requisites in their design e.g. minimal rule set and population size. Biological system simulation: methods of modelling insect societies should be of utility when simulating other organisms e.g. bacteria, human cells, tissues etc. Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  7. Modelling Social insects Top down Probabilistic models of whole population dynamics e.g. fluid flow modelling of army ant traffic. Bottom up Agent-based models utilising individual rule sets. Population dynamic emerges when sufficient agents interact. Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  8. Model Organism – The Pharaoh’s Ant Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  9. The Pharaoh’s Ant - Foraging Exploration Food Discovery and Return Recruitment Trail Dynamics / Traffic Decision Selection Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  10. Trail Formation A strong trunk trail and a network of minor trails emerges. A preliminary set of rules underlying this process has been estimated Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  11. Nest activities Feeding (larvae, ants) Looking for food Moving around Foraging Doing … nothing (inactive) Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  12. q0 initial state q1 next state Ant + M1 Ant + M2 Output Γ Behaviour elicited e.g. trail following, recruitment Input  e.g. pheromones, food, social and environmental stimuli etc. X-machine model Functions Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  13. Why X-machines ? State machine model widespread in man-made systems’ construction Well-developed verification and testing methods Easy to model Modularity Graphical representation Tools Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  14. Simulation results (Nest) 3cm x 3cm nest size 100 workers + 100 larvae worker model: 7 states; 22 transitions foraging happens in cycles (alterations may occur) no specialisation problem: tuning different parameters Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  15. Limitations Communication model rather ad-hoc No real formalism of functions associated with transitions No tool for interacting components … Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  16. A new modelling paradigm Biologically motivated Fully formal model Genuinely distributed Dynamic structure … Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  17. P systems Cellular biology A hierarchical arrangement Each membrane delimits a region Each region contains a multiset of elements (simple molecules, DNA sequences, other regions…) The chemicals/bio-elements evolve in time according to some (rewriting/combination) rules specific to each region or may be moved across the membranes The rules may also dissolve/create/move regions http://psystems.disco.unimib.it Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  18. P systems a model of bio-communities Initially an abstract model of cell structure and functioning Tissue P systems Population P systems http://psystems.disco.unimib.it Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  19. Population P systems A population of bio-units The units evolve Dynamic structure Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  20. Population P systems (2) Usual bio-units components (P systems) Tissues P systems communication rules Dynamic structure Components Links (bonds) Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  21. Population P Systems: a Modelling paradigm Rule types: transformation, communication (exchange of elements) – and a combination of both, bond making rules Each rule has a guard and refers to local elements Bio-units created/removed dynamically Bio-units: change their type, divide, die Each bio-unit has a type Environment Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  22. Code example foodL>=0: foodL--> foodL-FoodDecayRate next(this.pos, pos): <target=Env; out=pos; in=pos> foodL>HungryL: <target=Worker; out=Food from foodL; in=> forager: forager --> inactive; pos; pher; foodL Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  23. Advantages Fully formal Easy/Natural to model Easy to extend/reuse (bacteria, tissue) Adequate for a bottom-up approach An underlying graphical representation Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  24. Further developments Further investigations New features More complex case studies Tools Environment builder Handling of data generated Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  25. Conclusions • Two modelling approaches • Bottom-up/local modelling strategy • Local – global (individual – social) • Modelling – (small) case studies • … programming; hmmm Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

  26. Thanks Jean-Pierre Banâtre Jean-Louis Giavitto Pascal Fradet Olivier Michel Mike Holcombe Duncan Jackson Francesco Bernardini Fei Luo James Clarke Peter Langton Taihong Wu Yang Yang Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel

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