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P systems: A Modelling Language

P systems: A Modelling Language. Marian Gheorghe Department of Computer Science University of Sheffield. Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel. Summary. Modelling bio-communities State machines & P systems Experiments

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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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