using uml to model immune system
TRANSCRIPT
USING UNIFIED MODELING LANGUAGE TO
MODEL THE IMMUNE SYSTEM IN OBJECT
ORIENTED PERSPECTIVE
The 9th International Joint Conference on Computer
Science and Software Engineering (JCSSE 2012)
AUTHORS
Ayi Purbasari
School of Electrical Engineering and Informatics
Bandung Institute Technolog
Bandung, Indonesia
Iping Supriana S
School of Electrical Engineering and Informatics
Bandung Institute Technology
Bandung, Indonesia
Oerip S. Santoso
School of Electrical Engineering and Informatics
Bandung Institute Technology
Bandung, Indonesia
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PRESENTER
Ayi Purbasari
• Bandung, Indonesia
Lecturer at Pasundan University, Bandung, Indonesia
• Software Engineering, Computational Intelligence, Object Oriiented Paradigms
Graduate Student at Bandung Institute of Technology, Bandung, Indonesia
• Artificial Immune System3
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FROM BANDUNG TO BANGKOK
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2.429 km
PRESENTATION OUTLINE
Introduction to AIS
Research Purpose and Methodology
IS Modeling Using UML
Conclusion and Future
Works
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INTRODUCTION TO AIS
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INTRODUCTION:
ARTIFICIAL IMMUNE SYSTEM
Artificial Immune System
Computational Intelligence
Artificial Intelligence7
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AIS AND COMPUTATIONAL INTELLIGENT
Computational Intelligent
Evolutionary Computation
Swarm Intelligent
Particle Swarm
Ant Colony Optimization
Fuzzy SystemArtificial Immune System
Artifical Neural Net
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INTRODUCTION: AIS
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Immunology Engineering
Artificial Immune
Systems (AIS) uses the
vetebrata immune
system metaphors for
create new solutions to
complex problems -- or
at least gives new
ways of looking at
these problems.
INTRODUCTION: AIS
immune-inspired algorithms and
engineering solutions in
software and hardware
the understanding of immunology
through modeling and simulation of immune system
concepts. 10
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AIS, AN INTRODUCTION
1986 -Farmer, Packard & Perelson.
1990 - Bersini and Varela: immune networks.
1994 - Forrest et al. Kephart, Dasgupta: negative selection.
1995 – Hunt & Cookeand Timmis & Neal: Immune Network models
2002 - De Castro & Von Zuben and Nicosia & Cutello: Clonal selection
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COMMON RESEARCH IN AIS
applying immunological principles to
computational problems
machine learning
computer security
Data mining
optimization
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BIO-INSPIRED ALGORITHMS FRAMEWORK
To capture the complexity and richness that the
immune system offers is a difficult part for AIS
practitioners [1].
In order to remedy this, Stepney et., all. suggest a
conceptual framework [2] for developing bio-
inspired algorithms within a more principled
framework that attempts to capture biological
richness and complexity, but at the same time
appreciate the need for engineered systems.
At this framework, modeling is the most
important activity.13
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BIO-INSPIRED ALGORITHMS FRAMEWORK
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AIS AS BIO-INSPIRED COMPUTING
Introduction to AIS
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AIS AS BIO-INSPIRED COMPUTING
Biological System
Computing / Computation
Bio-Inspired
Computing
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BIO-INSPIRED COMPUTATION?
As computers and the tasks they
perform become increasingly
complex.
Researchers are looking to nature—as model and as metaphor—for inspiration [1]
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BIO-INSPIRED COMPUTATION
The more notable developments:
the neural networks inspired by the working of the
brain, and
the evolutionary algorithms inspired by neo-Darwinian
theory of evolution. [Timmis]
The motivation of this field is primarily to extract useful metaphors from natural biological systems, in order to create effective computational solutions to
complex problems in a wide range of domain areas.
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BIO-INSPIRED COMPUTING
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Artificial
Intelligent
Scruffy AINeat AI
Natural
Computing
Biology
Inspired
Computing
Computationall
y Motivated
Biology
Computing
with Biology
COMPUTATIONAL INTELLIGENT
Computational Intelligent
Evolutionary Computation
Swarm Intelligent
Particle Swarm
Ant Colony Optimization
Fuzzy SystemArtificial Immune System
Artifical Neural Net
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BIO-INSPIRED COMPUTATION
Biologically Inspired Computation is computation inspired by biological metaphor [3]
Biologically Inspired Computing is the area of research in the use of biology as a source of inspiration for solving computational problems [4]. 21
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ARTIFICIAL IMMUNE SYSTEM AS BIO-INSPIRED
COMPUTATION
AIS: adaptive systems, inspired by theoretical immunology and observed immunological functions, principles and models, which are applied to problem solving
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AIS AS A RESEARCH
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References Year Dissertation Master
Dispankar
Dasgupta [7]
2009 26 32
Jason
Brownlee [8]
2007 27 36
AIS’s Research Area
Thesis’s Years
INTERNATIONAL CONFERENCES OF ARTIFICIA
IMMUNE SYSTEM (ICARIS)
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3537
3436 37
3032
36
8
10
4
87
43
43
0
5
10
15
20
25
30
35
40
2003 2004 2005 2006 2007 2008 2009 2010 2011
Nu
mb
er
of
pap
ers
Years
ICARIS 2003-2011
Papers #
Groups
AN EXAMPLE OF AIS ALGORITHM
Clonal selection algorithm
Inspired by clonal selection theory
CSA is used in optimization domain problem
E.g: Travelling Salesperson Problem (TSP)
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AN EXAMPLE OF AIS ALGORITHM
Travelling Salesperson Problem (TSP)
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0
1000000
2000000
3000000
4000000
5000000
6000000
14
22
52
76
96
10
0
10
1
13
0
15
0
20
2
22
5
28
0
44
2
66
6
1002
Best
Sco
re
CSA is compared to GA and Ant Colony System
CSA
GA
ACS
RESEARCH
PROBLEM, PURPOSE, AND
METHODOLOGY
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PROBLEM IDENTIFICATION: MODELING AT AIS
One of the main problems involved in designing
bio-inspired algorithms, is deciding which
aspects of the biology are necessary
to generate the required behaviour, and
which aspects are surplus to requirements.
Some of the properties of the immune system show
the richness and complexity of the system that
might be of interest to a computer scientist to
inspire the novel solutions of complex problems.
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PROBLEM IDENTIFICATION: MODELING AT AIS
The crudeness of AIS algorithms such as CLONALG
that ―whilst intuitively appealing, lacks any notion of
interaction of B-cells with T-cells, MHC, or cytokines ‖
Problem Identification: modeling at AIS [3]
the need to consider the accuracy of the
inspiring metaphor, specifically the importance for
computer scientists to grasp the more subtle aspects
of immunology.
that by following a process that lacks the detail of
modeling, one may fall into the trap of reasoning by
metaphor.
The needs of modeling 29
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RESEARCH PURPOSE
To model the immune system from different view
with object oriented perspective,
To get the better understanding of the immune
system at computational aspect,
To use Unified Modeling Language as a standard
language for modeling object, with dynamic and
static behavior.
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METHODOLOGY
• Why are computer scientists interested in the immune system?
Immune system as literatur study
• Why OO? Why UML?
Object oriented perspective modeling • How to model
IS using UML (Static view and Dynamic view)?
Using UML to model immune
system
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IS MODELING USING UML
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IMMUNE SYSTEM .. (1)
The immune system is a network of cells, tissues, and organs that work together to defend the body against attacks by ―foreign‖ invaders.
Immune system involves two main objects:
immune cells that defens, and
pathogens that cause infection.
A pathogen is a microscopic organism that causes sickness. Viruses and bacteria are examples of pathogens.
On the surfaces of bacteria and viruses, there are antigens. An antigen is a foreign substance that stimulates the immune system to response 33
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IMMUNE SYSTEM .. (2)
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B-Cells
Antibody
Antigen
IMMUNE SYSTEM .. (4)
Main element at IS: Antigen and Antibody
Antibody
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IMMUNE SYSTEM .. (3)
Immune Cell Categories
Receptor
The Lymphatic system/ lymph vessels
T Helper Cells
(Th Cell)
T Killer Cells
(Cytotoxic T Lymphocytes – CTLs).
Major Histocompatibility Complex, or MHC. MHC class I
and MHC class II.
B Cells
Cytokines
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OO’S PERSPECTIVE
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What is an Objek? Why using UML?
An Object is an
entity that has:
state,
Behaviour, and
identity [Booch94].
UML can help you
specify, visualize, and
document models of
other non-software
systems (such as IS)
UML has thirteen
standard diagram
types.
OO’S PERSPECTIVE
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UML is built
upon fundamental OO
concepts
including class and
operation, it's a natural
fit for object-oriented
languages and
environments such as
C++, Java, and the
recent C#
Why UML? Why Using UML?
Structure of IS / Static View
Behavior of IS / Dynamic
View
UML
UML DIAGRAM
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IS AT FUNCTIONAL’S VIEW
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Immune System
(from Use Case View)Pathogen
Destruction
AntigenRecognition
<<extend>>
Antigen Presenting
<<include>>
<<include>>
IS as a Business ProcessIS as Use-case, to show functionalities at IS
STATIC VIEW OF IS
Antigens MHC
Lymphocytes
T-CellsB-Cells
Phagocytes
Macrophages Granulocytes Dendrit Cells
T-Helper Cells
T-Killer Cells
class MHC II
Exogenous Antigens
Combination II
Endogenous Antigens
Combination I
class MHC I
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IS AT DYNAMIC VIEW
Functional
Antigen Presenting
Exogenous
Endogenous
Recognition
By B-Cells
By T-Helper Cells
Destruction
By Phagocytes
By T-Killer Cells
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EXOGENOUS ANTIGEN PRESENTING ACTIVITY
DIAGRAM
secrete
interleukin-2
entering the
body
digest some of the pathogens, broke down
into fragment
release a chemical alarm
signal / Interleukin-1
combining MHC class I with antigent fragment and
display antigen fragments on their cell surfaces
response interleukin-1
and activated
Recognizing
antigen fragment
T-Helper CellsPhagocytesPhatogens
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ENDOGENOUS ANTIGEN PRESENTING
ACTIVITY DIAGRAM
response interleukin-1
and activated
secrete
interleukin-2response interleukin-2
and activated
recognize the antigen displayed
on the surfaces of infected cells
digest some of the pathogens, broke down
into fragment
combining MHC class I with antigent fragment and
display antigen fragments on their cell surfaces
Infected CellsT-Klller CellsT-Helper Cells
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B-CELLS RECOGNITION ACTIVITY DIAGRAM
response
interleukin-1
secrete
interleukin-2
response interleukin-2
and activated
differentiate into
plasma cells
become a
memory cell
release
antibodies
recognize and bind to the antigens on the
surfaces of the pathogens
marking them for desctruction
by macrophages
recognize the
antigen fragment
binding to antigen
fragment
B-CellsT-Helper Cells
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DESTRUCTION BY PHAGOCYTES
recognize marking
antibody-antigen
eat the
antigens
response interleukin-2
and activated
differentiate into
plasma cells
become a
memory cell
release
antibodies
recognize and bind to the antigens on the
surfaces of the pathogens
marking them for desctruction
by macrophages
B-CellsPhagocytes
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RECOGNITION AND DESTRUCTION BY T-
KILLER CELLS
response
interleukin-1
secrete
interleukin-2
response interleukin-2
and activated
recognize the antigen displayed on
the surfaces of infected cells
bind to the infected cells
and produce chemicals
that kill the infected cell
digest some of the pathogens and display
antigen fragments on their cell surfaces
attacked by
chemcals
Infected CellsT-Klller CellsT-Helper Cells
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CONCLUSION AND FUTURE WORKS
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CONCLUSION AND FUTURE WORKS
Immune system can be modelled using OO
perspectives. It promises the better understanding
for complex bio-systems such as immune system.
Especially for sofware engineer who will create
computational solution to solve computer science
problems.
This paper only using three main UML
diagrams, there are some diagrams will helpfull to
represent the detail about immune system, such as
B-cells recognition with their clonning process and
somatic hypermutation.
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IMMUNE SYSTEM
Supplementary Slide
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ELEMEN UTAMA SISTEM IMUN
Elemen Sistem Imun: Antigen dan Antibody
Struktur Antibody
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LYMPHOCITE: SEL PEMBENTUK ANTIBODY
Lymphocite
B-Cell
T-Cell
T-Helper Cell (CD4/T4)
T-Killer Cell
T SuppresorCell (CD8).
7/18/2012 56332 09 011
CARA KERJA SISTEM IMUN
7/18/2012 60332 09 011
SISTEM IMUN: CLONAL SELECTION
Conal Selection:
7/18/2012 63332 09 011