Presentacion Luis Wuhan

8/14/2019 Presentacion Luis Wuhan

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MeCoSim 2.0: a General Purpose Tool

for Simulating by means of P Systems

Luis Valencia Cabrera

Research Group on Natural Computing

Department of Computer Science and Artificial Intelligence

University of Seville

24-09-2010, BIC-TA 2010 - Changsha

Luis Valencia Cabrera (CCIA) A General Purpose Simulator for P Systems 24-09-2010, BIC-TA 2010 - Changsha 1 / 43
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ndice

1 Introduction

2 Modelling and simulating ecosystems by means of P systems

3 MeCoSim 1.0: A family of simulators for specific ecosystems

4 MeCoSim 2.0: A general purpose simulator based on P systems

5 Conclusions and future work

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1 Introduction





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Natural ComputingNew models of computation

Resolution of problems using algorithms

First models of computation (Gdel, Church, Kleene, Turing).

There are problems not resoluble by usual models Non-conventional models

Natural Computing

Idea Nature has made process since millions of yearsTarget Study and simulation of the processess of nature which are susceptible tointerpretation as computing procedures

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Membrane Computing

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Membrane ComputingMain topics under research

Theoretical focus

Computational Complexity Theory on Membrane Computing

Tractability/intractability boundary

Tissuelike P systems

Spiking neural P systems

P automata

Practical focusBiological processess modelling

Software development for MC (simulators)

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Biological processess modellingDifferent approaches

Main classical approach

Ordinary Differential Equations (ODEs)

Other important approaches

Petri Nets (Goss, 1998)

Agent Based Systems (Holcombe, 2003)

Process Algebra, calculus (A. Regev, E. Shapiro, 2004)

Bioambients (L. Cardelli, E.M. Panina, A. Regev, W. Silvermann, E. Shapiro, 2004)

Brane Calculus (L. Cardelli, 2005)

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Biological processess modellingDesirable properties

Desired properties for a good modelling framework

Relevance

Understandability

Extensibility

Computability / tractability

P systemsA computational framework satisfying the previous requirements.

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Biological processess modellingModelling Protocol (I)

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Modelizacin de procesos biolgicosModelling Protocol (II)

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1 Introduction





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Multienvironment functional extended P systemswith active membranes

Definition: Multienvironment functional extended P system with active membranes

A multienvironment functional extended P system with active membranes of degree (q, m,n), withq 1, m 1n 1, takingTtime units,T 1, is a tuple

(G,,,,R,RE,{fr,k: r R,1 k n},{Mi,k: 0 i q 1, 1 k n})

where:

(,,R) = is the skeleton of a functional extended P system with active membranes of degreeq;

REis a finite set of communication rules between environments,

(x)ejp(x,j,j) (y)ej y (k)ej

p(k,j,j) (k)ej

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MeCoSim 1.0 familyAd-hoc Software simulators

General structureMany developed simulatorsa

Several common components

aSoftware for P systems. D Daz Pernil et al. The Oxford Handbook of Membrane Computing. 2010, pp. 437454

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A software framework for Membrane Computing

Main ingredients

P-Lingua: A programming language to define P systems

Tools for compilation and simulation

pLinguaCore: Library implementing simulation algorithms

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MeCoSim 1.0: a family of simulators

Main properties

Simulation of ecosystem models

Experimental validation and virtual experimentation

Different applications developed ad hoc:

MeCoSim 1.0 Tritrophic interactions

MeCoSim 1.0 Bearded vulture

MeCoSim 1.0 Zebra mussel

All of these applications to simulate:

Imply ad-hoc development of its graphical user interfaces (GUIs)

Make use of pLinguaCore library to run the simulations

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MeCoSim 1.0: a family of simulatorsMulti-user system

Users and responsabilities

User A. End user, expert in the problem domain.

Uses the application to make virtual experiments.

User B. Designer user, expert in membrane computing.

Uses the application to design and validate the model.

The application provides different functionality depending on the type of user.



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MeCoSim 1.0: a family of simulatorsUser A. End user, expert in the problem domain

Use cases

No need to know about Membrane Computing

No need to know about programming languages

The simulator acts as a black boxInput: Parameters of the simulation (e. g. in ecosystems):

Number of the individuals of each species

Probabilities of the ecosystem

Output: Results of the simulation after the requested years:

Evolution of the population of each speciesBones, biomass, . . .

Representation with tables and graphics.


M C Si f il f i l
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MeCoSim 1.0: a family of simulatorsUser B. Designer user, expert in membrane computing

Use cases

No need to know programming languages other than PLingua.

The simulator includes all the functionality it includes for tha user A.

Act as a white box

Is possible to monitor the simulation of the P system step by step: debugging.


A th ti l t d i t t it hi i t ti
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A theoretical case study in ecosystems: tritrophic interactions

Features

Symplification of a real ecosystem, well documented in the literature

Three trophic levels: grass, herbivors and carnivors

Multiple interconnected zones

Herbivors and carnivors move to other zones if there is lack of food

Maximum population density in each zone


A th ti l t d i t t it hi i t ti
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A theoretical case study in ecosystems: tritrophic interactionsModel

= {Xi,1 : 1 i 3}{ai : 1 i 4}{b, c}

r1 X1,1[ ]1k1,1

[X21,1]1

r2 X1,1[ ]11k1,1[X1,1]1

r3 [a1]1 [a2]1

r4 [X2,1, X101,1]1

k2,1b[X22,1, c]

+1

r5 [X2,1, X101,1]1

1k2,1b[X2,1, X

101,1]

+1

r6 [a2]1 b[a3]+1

r7 [X3,1, X2,1,c]+1

k3,1b[X23,1]

1

r8 [X3,1, X2,1,c]+1

1k3,1b[X3,1, X2,1, c]

1

r9 [a3]+1 b[a4]

1


A theoretical case st d in ecos stems tritrophic interactions
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A theoretical case study in ecosystems: tritrophic interactionsModel

r10 [X1,1]1

k1,2b[ ]1

r11 [X1,1]1

1k1,2X1,1[ ]1

r11 [X1,1]1

1k1,2X1,1[ ]1

r12 [X2,1]1k

2,

2b[ ]1

r13 [X2,1]1

1k2,2b[X2,1]1

r14 [X3,1]1

k3,2b[ ]1

r15 [X3,1]1

1k3,2b[X3,1]1

r16 [a4]1 b[a1]1

r17 [b]0 [ ]0


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A theoretical case study in ecosystems: tritrophic interactionsAd hoc interfaces, input data


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A theoretical case study in ecosystems: tritrophic interactionsAd hoc interface, model debbugging


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A theoretical case study in ecosystems: tritrophic interactionsAd hoc interface, graphics results generation


A practical case study in ecosystems: Bearded Vulture


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Features

Real ecosystem of a scavenger bird in danger of extinction

Study of the features of the environment and a lot of species

Multiple interconnected zones

Multidisciplinar group (ecologists, mathematicians, computer sciencists)


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A practical case study in ecosystems: Bearded VultureAd hoc interface, graphics results generation


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1 Introduction






MeCoSim 2.0: A general purpose simulator based on P systems
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MeCoSim 2.0: A general purpose simulator based on P systemsMotivation

Target

The previous applications share common components, but imply ad-hoc developments

Need to avoid the development of different application GUIs for each problem

Develop a general purpose application able to generate ad-hoc simulators and its

associated GUIs.


Challenge
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Challenge

Develop a highly customizable application

Design a uniform mechanism to allow the user to set the specific types of input and

output data

Design a way to permit the user passing the input data to the simulator.


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g p p yFeatures

Keep the same types of users and functionalities of simulation provided by the family

MeCoSim 1.0

Users A (end user) and B (designer user) with the functionality previously described

Use of P-Lingua like the language to define the modeled P systems

Use of pLinguaCore to run the simulations

The application provides a template config file to permit the user filling the necessary

information


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g p p yFeatures

With this config file the user customize the general purpose application to generate a

custom simulator, adapted to the model of the problem under study, setting the folowing

configuration info:

General information of the applicationHierarchy of tabs to arrange the input and output tables

Input and output tables to show/edit, and columns of the tables

Parameters to use in the simulation

This avoid the necessity of a new development for each model of a problem.


Customizing a simulator from the generic structure
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g gGeneral data

Information to provide

Application name and id

Input/output data -> Ec2 data file path

Model data -> PLingua file path and number of steps by year

Simulation parameters -> Number of years and number of simulations by year


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Tabs Hierarchy

Information to provideTab names and ids (id 0 represents the application)

Parent id of each tab to define the hierarchy


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Tables configuration


Table names and ids (id 0 represents the application)

Id of the tab where the table is put up

Number of initial rows and columns of each table

An indicator to save the data of the table to the inner database


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Table Columns configuration


Table column names and ids

Tool-tip to show for each column

An indicator of it is allowed the edition of the data of each column by the user


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P-Lingua parameters configuration


In the parameters tab, the designer lists the sets of parameters to use in the simulation of the

model, with:

Name of the parameter (the name of the prefix shared by the possible indexed

parameters)

Value of the parameter

Up to 4 indexes for each parameter to iterate and generate the final indexed parameters

for the simulation


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1 Introduction






Conclusions and future work
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ConclusionsSystems Biology has quickly emerged Better understanding of biological phenomena

Computational methods to complement experimental research knowledge

Developments of ad-hoc simulators for each problem imply much effort, so:

Researchers better advance requires a general purpose simulation solution

That will avoid to solve the same problems again and again to develop custom software to

each problem time to market for simulating a model for a new problem

We have presented hereMeCoSim 2.0, a first step in this direction that:

Provides a general purpose application to generate custom simulators of computational

models by means of P systems by simply filling a configuration spreadsheet file

Allows to experimentally validates computational models of biological phenomena

Permits simulating virtual experiments over the models

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Thank you very much


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I hope this presentation has been of your interest.

At disposal for any question, advice or suggestion...

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Presentacion Luis Wuhan