Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Modeling the Immune System Using UML
1. 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)
2. AUTHORS
Ayi Purbasari
School of Electrical Engineering and Informatics
Bandung Institute Technolog
JCSSE 2012
Bandung, Indonesia
pbasari@unpas.ac.id
Iping Supriana S
School of Electrical Engineering and Informatics
Bandung Institute Technology
Bandung, Indonesia
iping@stei.itb.ac.id
Oerip S. Santoso
School of Electrical Engineering and Informatics
Bandung Institute Technology
Bandung, Indonesia
oerip@stei.itb.ac.id 2
3. PRESENTER
Ayi Purbasari
JCSSE 2012
• Bandung, Indonesia
Lecturer at Pasundan
University, Bandung, Indonesia
• Software Engineering, Computational
Intelligence, Object Oriiented Paradigms
Graduate Student at Bandung Institute of
Technology, Bandung, Indonesia
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• Artificial Immune System
7. INTRODUCTION:
ARTIFICIAL IMMUNE SYSTEM
Artificial
Immune
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System
Computational
Intelligence
Artificial Intelligence
7
8. AIS AND COMPUTATIONAL INTELLIGENT
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Computational
Intelligent
Artificial
Evolutionary Swarm Artifical Neural
Fuzzy System Immune
Computation Intelligent Net
System
Particle Ant Colony
Swarm Optimization
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9. INTRODUCTION: AIS
Artificial Immune
Systems (AIS) uses the
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vetebrata immune
system metaphors for
create new solutions to
complex problems -- or
Immunology Engineering
at least gives new
ways of looking at
these problems.
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10. INTRODUCTION: AIS
immune-inspired
algorithms and
engineering
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solutions in
software and
hardware
the understanding
of immunology
through modeling
and simulation of
immune system
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concepts.
11. AIS, AN INTRODUCTION
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2002 - De Castro &
Von Zuben and
Nicosia & Cutello:
1995 – Hunt & Cooke Clonal selection
and Timmis & Neal:
Immune Network
models
1994 - Forrest et al.
Kephart, Dasgupta:
negative selection.
1990 - Bersini and
Varela: immune
networks.
1986 -
Farmer, Packard
& Perelson.
11
12. COMMON RESEARCH IN AIS
machine
learning
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applying
immunological
computer
optimization principles to security
computational
problems
Data mining
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13. BIO-INSPIRED ALGORITHMS FRAMEWORK
To capture the complexity and richness that the
immune system offers is a difficult part for AIS
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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.
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15. JCSSE 2012
AIS AS BIO-INSPIRED COMPUTING
15 Introduction to AIS
16. AIS AS BIO-INSPIRED COMPUTING
Biological
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System
Bio-
Inspired
Computing
Computing /
Computation
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17. BIO-INSPIRED COMPUTATION?
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As computers and Researchers are
the tasks they looking to nature—
perform become as model and as
increasingly metaphor—for
complex. inspiration [1]
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18. BIO-INSPIRED COMPUTATION
The motivation of this field is primarily to extract
useful metaphors from natural biological systems, in
JCSSE 2012
order to create effective computational solutions to
complex problems in a wide range of domain areas.
The more notable developments:
the neural networks inspired the evolutionary algorithms
by the working of the inspired by neo-Darwinian 18
brain, and theory of evolution. [Timmis]
19. BIO-INSPIRED COMPUTING
Artificial
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Intelligent
Natural
Computing
Neat AI Scruffy AI
Biology Computationall
Computing
Inspired y Motivated
Biology with Biology
Computing 19
20. COMPUTATIONAL INTELLIGENT
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Computational
Intelligent
Artificial
Evolutionary Swarm Artifical Neural
Fuzzy System Immune
Computation Intelligent Net
System
Particle Ant Colony
Swarm Optimization
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21. BIO-INSPIRED COMPUTATION
Biologically Inspired Computation
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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
22. ARTIFICIAL IMMUNE SYSTEM AS BIO-INSPIRED
COMPUTATION
AIS: adaptive
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systems, inspired by
theoretical immunology and
observed immunological
functions, principles and
models, which are applied
to problem solving 22
23. AIS AS A RESEARCH
References Year Dissertation Master AIS’s Research Area
Dispankar 2009 26 32
Dasgupta [7]
Jason 2007 27 36
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Brownlee [8]
Thesis’s Years
23
24. INTERNATIONAL CONFERENCES OF ARTIFICIA
IMMUNE SYSTEM (ICARIS)
ICARIS 2003-2011
40
35
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30
Number of papers
25
20
37 36 37 36 Papers #
35 34
32 Groups
15 30
28
10
10
8 8
7
5 4 4 4
3 3
0
2003 2004 2005 2006 2007 2008 2009 2010 2011 24
Years
25. AN EXAMPLE OF AIS ALGORITHM
Clonal selection algorithm
Inspired by clonal selection theory
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CSA is used in optimization domain problem
E.g: Travelling Salesperson Problem (TSP)
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26. AN EXAMPLE OF AIS ALGORITHM
Travelling Salesperson Problem (TSP)
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CSA is compared to GA and Ant
Colony System
6000000
5000000
4000000
Best Score
3000000 CSA
GA
2000000
ACS
1000000
0
100
101
130
150
202
225
280
442
666
14
22
52
76
96
1002
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27. JCSSE 2012
RESEARCH
PROBLEM, PURPOSE, AND
METHODOLOGY
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28. PROBLEM IDENTIFICATION: MODELING AT AIS
One of the main problems involved in designing
bio-inspired algorithms,
is deciding which
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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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29. 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 ‖
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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
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30. RESEARCH PURPOSE
To model the immune system from different view
with object oriented perspective,
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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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31. METHODOLOGY
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Object oriented
perspective
• Why are computer modeling • How to model
scientists IS using UML
interested in the • Why OO? Why
immune system? UML? (Static view and
Dynamic view)?
Using UML to
Immune system as
model immune
literatur study
system
31
33. 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‖
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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
35. IMMUNE SYSTEM .. (4)
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Main element at IS: Antigen and Antibody
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Antibody 35
36. IMMUNE SYSTEM .. (3)
Immune Cell Categories
Receptor
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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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37. OO’S PERSPECTIVE
What is an Objek? Why using UML?
JCSSE 2012
An Object is an UML can help you
entity that has: specify, visualize, and
document models of
state,
other non-software
Behaviour, and systems (such as IS)
identity [Booch94]. UML has thirteen
standard diagram
types.
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38. OO’S PERSPECTIVE
Why UML? Why Using UML?
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UML is built Structure of IS
Behavior of IS
/ Dynamic
upon fundamental OO / Static View
View
concepts
including class and
operation, it's a natural
fit for object-oriented
languages and
environments such as
C++, Java, and the
recent C# 38
41. IS AT FUNCTIONAL’S VIEW
IS as Use-case, to show
IS as a Business Process functionalities at IS
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Antigen Presenting
<<include>>
<<include>>
Recognition
Antigen
Immune System <<extend>>
Pathogen
(from Use Case View)
Destruction
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42. STATIC VIEW OF IS
Exogenous Antigens
Lymphocytes
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Antigens MHC
B-Cells T-Cells
Endogenous Antigens
class MHC II
class MHC I
Combination II T-Killer Cells
Combination I
Phagocytes
T-Helper Cells
Macrophages Granulocytes Dendrit Cells
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43. IS AT DYNAMIC VIEW
Exogenous
Antigen
Presenting
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Endogenous
By B-Cells
Functional Recognition
By T-Helper
Cells
By
Phagocytes
Destruction
By T-Killer 43
Cells
44. EXOGENOUS ANTIGEN PRESENTING ACTIVITY
DIAGRAM
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Phatogens Phagocytes T-Helper Cells
entering the digest some of the pathogens, broke down
body into fragment
combining MHC class I with antigent fragment and Recognizing
display antigen fragments on their cell surfaces antigen fragment
release a chemical alarm response interleukin-1
signal / Interleukin-1 and activated
secrete
interleukin-2
44
45. ENDOGENOUS ANTIGEN PRESENTING
ACTIVITY DIAGRAM
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T-Helper Cells T-Klller Cells Infected Cells
response interleukin-1 digest some of the pathogens, broke down
and activated into fragment
secrete response interleukin-2 combining MHC class I with antigent fragment and
interleukin-2 and activated display antigen fragments on their cell surfaces
recognize the antigen displayed
on the surfaces of infected cells
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46. B-CELLS RECOGNITION ACTIVITY DIAGRAM
T-Helper Cells B-Cells
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response recognize the
interleukin-1 antigen fragment
secrete binding to antigen
interleukin-2 fragment
response interleukin-2
and activated
differentiate into become a
plasma cells memory cell
release
antibodies
recognize and bind to the antigens on the
surfaces of the pathogens
marking them for desctruction
by macrophages
46
47. DESTRUCTION BY PHAGOCYTES
Phagocytes B-Cells
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response interleukin-2
and activated
become a
differentiate into
memory cell
plasma cells
release
antibodies
recognize and bind to the antigens on the
surfaces of the pathogens
recognize marking marking them for desctruction
antibody-antigen by macrophages
eat the
antigens
47
48. RECOGNITION AND DESTRUCTION BY T-
KILLER CELLS
T-Helper Cells T-Klller Cells Infected Cells
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digest some of the pathogens and display
response
antigen fragments on their cell surfaces
interleukin-1
secrete response interleukin-2
interleukin-2 and activated
recognize the antigen displayed on
the surfaces of infected cells
bind to the infected cells
and produce chemicals attacked by
that kill the infected cell chemcals
48
50. CONCLUSION AND FUTURE WORKS
Immune system can be modelled using OO
perspectives. It promises the better understanding
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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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51. JCSSE 2012
THANK YOU
51 pbasari@unpas.ac.id
pbasari@gmail.com
52. JCSSE 2012
IMMUNE SYSTEM
52 Supplementary Slide
53. ELEMEN UTAMA SISTEM IMUN
7/18/2012
Elemen Sistem Imun: Antigen dan Antibody
332 09 011
Struktur Antibody 55
AIS began in the mid 1980s with Farmer, Packard and Perelson's (1986) and Bersini and Varela's papers on immune networks (1990). However, it was only in the mid 90s that AIS became a subject area in its own right. Forrest et al. (on negative selection) and Kephart et al.[2] published their first papers on AIS in 1994, and Dasgupta conducted extensive studies on Negative Selection Algorithms. Hunt and Cooke started the works on Immune Network models in 1995; Timmis and Neal continued this work and made some improvements. De Castro & Von Zuben's and Nicosia & Cutello's work (on clonal selection) became notable in 2002. The first book on Artificial Immune Systems was edited by Dasgupta in 1999.New ideas, such as danger theory and algorithms inspired by the innate immune system, are also now being explored. Although some doubt that they are yet offering anything over and above existing AIS algorithms, this is hotly debated, and the debate is providing one the main driving forces for AIS development at the moment. Other recent developments involve the exploration of degeneracy in AIS models,[3][4] which is motivated by its hypothesized role in open ended learning and evolution.[5][6]Originally AIS set out to find efficient abstractions of processes found in the immune system but, more recently, it is becoming interested in modelling the biological processes and in applying immune algorithms to bioinformatics problems.In 2008, Dasgupta and Nino [7] published a textbook on Immunological Computation which presents a compendium of up-to-date work related to immunity-based techniques and describes a wide variety of applications.
Artificial Immune Systems (AIS) began in the mid 1980s, that uses the vetebrata immune system metaphors for the create new solutions to complex problems or at least gives new ways of looking at these problems. AIS are class of computationally intelligent systems which is a diverse area of research between immunology and engineering and becomes a bridge between them. Scope of AIS ranges from immune-inspired algorithms and engineering solutions in software and hardware, to the understanding of immunology through modeling and simulation of immune system concepts. The original research in AIS focus on applying immunological principles to computational problems in practical domains in a wide variety of domains, including machine learning, computer security, fault tolerance, bioinformatics, data mining, and optimization. As the field has matured, it has diversified such as formalizing the theoretical properties of earlier approaches, elaborating underlying relationships between applied computational models and those from theoretical immunology. In recent years, the area of AIS has begun to return to immune system modeling, the immunology from which the initial inspiration came. Increasingly, theoretical insight into aspects of artificial and real immune systems has been sought through mathematical and computational modelling and analysis. This vigorous field of research investigates how immunology can assist our technology, and along the way is beginning to help biologists understand their unique problems.
Artificial Immune Systems (AIS) began in the mid 1980s, that uses the vetebrata immune system metaphors for the create new solutions to complex problems or at least gives new ways of looking at these problems. AIS are class of computationally intelligent systems which is a diverse area of research between immunology and engineering and becomes a bridge between them. Scope of AIS ranges from immune-inspired algorithms and engineering solutions in software and hardware, to the understanding of immunology through modeling and simulation of immune system concepts. The original research in AIS focus on applying immunological principles to computational problems in practical domains in a wide variety of domains, including machine learning, computer security, fault tolerance, bioinformatics, data mining, and optimization. As the field has matured, it has diversified such as formalizing the theoretical properties of earlier approaches, elaborating underlying relationships between applied computational models and those from theoretical immunology. In recent years, the area of AIS has begun to return to immune system modeling, the immunology from which the initial inspiration came. Increasingly, theoretical insight into aspects of artificial and real immune systems has been sought through mathematical and computational modelling and analysis. This vigorous field of research investigates how immunology can assist our technology, and along the way is beginning to help biologists understand their unique problems.
To capture the complexity and richness that the immune system offers is a difficult part for AIS practitioners [1]. Many of them were failing. 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. Recently, AIS towards paying more attention to taking time both to develop abstract computational models of the immune system and work closer with immunologists to better understand the biology behind the system. This would be fair to say that AIS is becoming a more inter-disciplinary topic where people are working more on the biological aspects and others on the more engineering aspects [2][3]
As computers and the tasks they perform become increasingly complex, researchers are looking to nature—as model and as metaphor—for inspiration. The organization and behavior of biological organisms present scientists with an invitation to reinvent computing for the complex tasks of the future [Forbes]
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. The more notable developments have been the neural networks inspired by the working of the brain, and the evolutionary algorithms inspired by neo-Darwinian theory of evolution. [Timmis]
Biologically Inspired Computation is computation inspired by biological metaphor [Brownlee]Biologically Inspired Computing is the area of research in the use of biology as a source of inspiration for solving computational problems [Timmis].
Sistemimunterdiridaribeberapalapisan, yaitu:Lapisanfisik, misalnyakulitLapisanphysiological, misalenzim-enzimInnate Immune System dan Adaptive Immune SystemInnate Immune Systemadalahkekebalan non-spesifik, merupakankekebalantubuh yang dibawasejaklahir. SedangkanAdaptive Immune Systemmerupakankekebalanspesifik, beresponspesifiksesuaidenganpatogen yang memasukitubuh. PadaresponInnate Immune, terdapatPhagocyte yang akanmengenalidanmenghancurkanpatogen yang memasukitubuh. SedangkanpadaresponAdaptive Immune, terdapatLymphocyte yang akanmengenalisecaraspesifik antigen daripatogen yang memasukitubuhdanmengeluarkanantibodi yang bersesuaiandengan antigen tersebut.
Phagocytes -- large white cells that can swallow and digest microbes and other foreign particles. Monocytes are phagocytes that circulate in the blood. When monocytes migrate into tissues, they develop into macrophages. Specialized types of macrophages can be found in many organs, including lungs, kidneys, brain, and liver.lymphocytes—small white blood cells produced in the lymphoid organs and paramount in the immune defenses. B cells and T cells are lymphocytes.
—small white blood cells produced in the lymphoid organs and paramount in the immune defenses. B cells and T cells are lymphocytes.TerdapatduamacamLymphocyte yang berperan, yaituB-CelldanT-Cell. T-Cell terdiridaritigakelompok, yaitu T-Helper Cell (CD4/T4), T-Killer Cell, T Suppersor Cell (CD8).Seluruhselimunbermulasebagaiimmature stem cell yang terdapatdibone marrow. Merekaakanmeresponberbagaisinyaluntuktumbuhmenjadiseltipespesifik (B Cell, T Cell, atauPhagocytes). The T helper cells, or simply Th cells, are essential to the activation of the B cells, other Tcells, macrophages and natural killer (NK) cells. They are also known as CD4 or T4 cells. The killer T cells, or cytotoxic T cells, are capable of eliminating microbial invaders, viruses orcancerous cells. Once activated and bound to their ligands, they inject noxious chemicals into the other cells, perforating their surface membrane and causing their destruction. The suppressor T lymphocytes are vital for the maintenance of the immune response. They are sometimes called CD8 cells, and inhibit the action of other immune cells. Without their activity, immunity would certainly loose control resulting in allergic reactions and autoimmune diseases (Janeway Jr. & Travers, 1997).
Specialized antigen presenting cells (APCs), such as macrophages, roam the body, ingesting and digesting the antigens they find and fragmenting them into antigenic peptides (Nossal, 1993) (I). Pieces of these peptides are joined to major histocompatibility complex (MHC) molecules and are displayed on the surface of the cell. Other white blood cells, called Tcells or Tlymphocytes, have receptor molecules that enable each of them to recognize a different peptide-MHC combination (II). T cells activated by that recognition divide and secrete lymphokines, or chemical signals, that mobilize other components of the immune system (III). The B lymphocytes, which also have receptor molecules of a single specificity on their surface, respond to those signals. Unlike the receptors of T cells, however, those of B cells can recognize parts of antigens free in solution, without MHC molecules (IV). When activated, the B cells divide and differentiate into plasma cells that secrete antibody proteins, which are soluble forms of their receptors (V). By binding to the antigens they find, antibodies can neutralize them (VI) or precipitate their destruction by complement enzymes or by scavenging cells.
Specialized antigen presenting cells (APCs), such as macrophages, roam the body, ingesting and digesting the antigens they find and fragmenting them into antigenic peptides (Nossal, 1993) (I). Pieces of these peptides are joined to major histocompatibility complex (MHC) molecules and are displayed on the surface of the cell. Other white blood cells, called Tcells or Tlymphocytes, have receptor molecules that enable each of them to recognize a different peptide-MHC combination (II). T cells activated by that recognition divide and secrete lymphokines, or chemical signals, that mobilize other components of the immune system (III). The B lymphocytes, which also have receptor molecules of a single specificity on their surface, respond to those signals. Unlike the receptors of T cells, however, those of B cells can recognize parts of antigens free in solution, without MHC molecules (IV). When activated, the B cells divide and differentiate into plasma cells that secrete antibody proteins, which are soluble forms of their receptors (V). By binding to the antigens they find, antibodies can neutralize them (VI) or precipitate their destruction by complement enzymes or by scavenging cells.
