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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
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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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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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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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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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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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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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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.
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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.
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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
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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
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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 interactions
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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 interactions
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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 interactions
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A theoretical case study in ecosystems: tritrophic interactionsAd hoc interface, graphics results generation
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A practical case study in ecosystems: Bearded Vulture
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A practical case study in ecosystems: Bearded Vulture
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 Vulture
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A practical case study in ecosystems: Bearded VultureAd hoc interface, graphics results generation
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ndice
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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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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.
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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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MeCoSim 2.0: A general purpose simulator based on P systems
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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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MeCoSim 2.0: A general purpose simulator based on P systems
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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.
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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
Information to provide
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
Information to provide
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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Customizing a simulator from the generic structure
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P-Lingua parameters configuration
Information to provide
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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ndice
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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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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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