P systems a modelling language
This presentation is the property of its rightful owner.
Sponsored Links
1 / 26

P systems: A Modelling Language PowerPoint PPT Presentation


  • 69 Views
  • Uploaded on
  • Presentation posted in: General

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

Download Presentation

P systems: A Modelling Language

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


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

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


What is a model

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


What to model

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


P systems a modelling language

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


Modelling bio communities

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


Modelling social insects

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


Model organism the pharaoh s ant

Model Organism – The Pharaoh’s Ant

Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel


The pharaoh s ant foraging

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


Trail formation

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


Nest activities

Nest activities

Feeding (larvae, ants)

Looking for food

Moving around

Foraging

Doing … nothing (inactive)

Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel


X machine model

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


Why x machines

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


Simulation results nest

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


Limitations

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


A new modelling paradigm

A new modelling paradigm

Biologically motivated

Fully formal model

Genuinely distributed

Dynamic structure

Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel


P systems

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


P systems a model of bio communities

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


Population p systems

Population P systems

A population of bio-units

The units evolve

Dynamic structure

Unconventional Programming Paradigms; 15-17 Sept’04 – Mont Saint Michel


Population p systems 2

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


Population p systems a modelling paradigm

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


Code example

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


Advantages

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


Further developments

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


Conclusions

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


Thanks

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


  • Login