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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
JCSSE 2012
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](https://image.slidesharecdn.com/usingumltomodelimmunesystem-120718095523-phpapp02/85/Using-uml-to-model-immune-system-13-320.jpg)
![BIO-INSPIRED COMPUTATION?
JCSSE 2012
As computers and Researchers are
the tasks they looking to nature—
perform become as model and as
increasingly metaphor—for
complex. inspiration [1]
17](https://image.slidesharecdn.com/usingumltomodelimmunesystem-120718095523-phpapp02/85/Using-uml-to-model-immune-system-17-320.jpg)
![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]](https://image.slidesharecdn.com/usingumltomodelimmunesystem-120718095523-phpapp02/85/Using-uml-to-model-immune-system-18-320.jpg)
![BIO-INSPIRED COMPUTATION
Biologically Inspired Computation
JCSSE 2012
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](https://image.slidesharecdn.com/usingumltomodelimmunesystem-120718095523-phpapp02/85/Using-uml-to-model-immune-system-21-320.jpg)
![AIS AS A RESEARCH
References Year Dissertation Master AIS’s Research Area
Dispankar 2009 26 32
Dasgupta [7]
Jason 2007 27 36
JCSSE 2012
Brownlee [8]
Thesis’s Years
23](https://image.slidesharecdn.com/usingumltomodelimmunesystem-120718095523-phpapp02/85/Using-uml-to-model-immune-system-23-320.jpg)
![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 ‖
JCSSE 2012
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](https://image.slidesharecdn.com/usingumltomodelimmunesystem-120718095523-phpapp02/85/Using-uml-to-model-immune-system-29-320.jpg)
![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.
37](https://image.slidesharecdn.com/usingumltomodelimmunesystem-120718095523-phpapp02/85/Using-uml-to-model-immune-system-37-320.jpg)
Uploaded byAyi Purbasari
Using uml to model immune system
This document discusses modeling the immune system using Unified Modeling Language (UML) from an object-oriented perspective. It was presented at the 9th International Joint Conference on Computer Science and Software Engineering in 2012. The presentation provides an introduction to artificial immune systems and how they are a form of bio-inspired computing, drawing inspiration from biological immune systems to solve complex computational problems. It outlines how UML can be used to model the immune system and its various components as objects.
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Using uml to model immune system
- 1. USING UNIFIED MODELINGLANGUAGE 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 3 • Artificial Immune System
- 4. FROM BANDUNG TOBANGKOK JCSSE 2012 2.429 km 4
- 5. PRESENTATION OUTLINE JCSSE 2012 Research Introduction Purpose and to AIS Methodology Conclusion IS Modeling and Future Using UML Works 5
- 6. JCSSE 2012 INTRODUCTION TO AIS 6
- 7. INTRODUCTION: ARTIFICIAL IMMUNE SYSTEM Artificial Immune JCSSE 2012 System Computational Intelligence Artificial Intelligence 7
- 8. AIS AND COMPUTATIONALINTELLIGENT JCSSE 2012 Computational Intelligent Artificial Evolutionary Swarm Artifical Neural Fuzzy System Immune Computation Intelligent Net System Particle Ant Colony Swarm Optimization 8
- 9. INTRODUCTION: AIS Artificial Immune Systems (AIS) uses the JCSSE 2012 vetebrata immune system metaphors for create new solutions to complex problems -- or Immunology Engineering at least gives new ways of looking at these problems. 9
- 10. INTRODUCTION: AIS immune-inspired algorithms and engineering JCSSE 2012 solutions in software and hardware the understanding of immunology through modeling and simulation of immune system 10 concepts.
- 11. AIS, AN INTRODUCTION JCSSE 2012 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 INAIS machine learning JCSSE 2012 applying immunological computer optimization principles to security computational problems Data mining 12
- 13. BIO-INSPIRED ALGORITHMS FRAMEWORK To capture the complexity and richness that the immune system offers is a difficult part for AIS JCSSE 2012 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
- 14. BIO-INSPIRED ALGORITHMS FRAMEWORK JCSSE 2012 14
- 15. JCSSE 2012 AIS AS BIO-INSPIRED COMPUTING 15 Introduction to AIS
- 16. AIS AS BIO-INSPIREDCOMPUTING Biological JCSSE 2012 System Bio- Inspired Computing Computing / Computation 16
- 17. BIO-INSPIRED COMPUTATION? JCSSE 2012 As computers and Researchers are the tasks they looking to nature— perform become as model and as increasingly metaphor—for complex. inspiration [1] 17
- 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 JCSSE 2012 Intelligent Natural Computing Neat AI Scruffy AI Biology Computationall Computing Inspired y Motivated Biology with Biology Computing 19
- 20. COMPUTATIONAL INTELLIGENT JCSSE 2012 Computational Intelligent Artificial Evolutionary Swarm Artifical Neural Fuzzy System Immune Computation Intelligent Net System Particle Ant Colony Swarm Optimization 20
- 21. BIO-INSPIRED COMPUTATION Biologically InspiredComputation JCSSE 2012 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 SYSTEMAS BIO-INSPIRED COMPUTATION AIS: adaptive JCSSE 2012 systems, inspired by theoretical immunology and observed immunological functions, principles and models, which are applied to problem solving 22
- 23. AIS AS ARESEARCH References Year Dissertation Master AIS’s Research Area Dispankar 2009 26 32 Dasgupta [7] Jason 2007 27 36 JCSSE 2012 Brownlee [8] Thesis’s Years 23
- 24. INTERNATIONAL CONFERENCES OFARTIFICIA IMMUNE SYSTEM (ICARIS) ICARIS 2003-2011 40 35 JCSSE 2012 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 OFAIS ALGORITHM Clonal selection algorithm Inspired by clonal selection theory JCSSE 2012 CSA is used in optimization domain problem E.g: Travelling Salesperson Problem (TSP) 25
- 26. AN EXAMPLE OFAIS ALGORITHM Travelling Salesperson Problem (TSP) JCSSE 2012 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 26
- 27. JCSSE 2012 RESEARCH PROBLEM, PURPOSE, AND METHODOLOGY 27
- 28. PROBLEM IDENTIFICATION: MODELINGAT AIS One of the main problems involved in designing bio-inspired algorithms, is deciding which JCSSE 2012 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. 28
- 29. PROBLEM IDENTIFICATION: MODELINGAT 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 ‖ JCSSE 2012 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
- 30. RESEARCH PURPOSE Tomodel the immune system from different view with object oriented perspective, JCSSE 2012 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. 30
- 31. METHODOLOGY JCSSE 2012 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
- 32. JCSSE 2012 IS MODELING USING UML 32
- 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‖ JCSSE 2012 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
- 34. IMMUNE SYSTEM ..(2) B-Cells JCSSE 2012 Antigen Antibody 34
- 35. IMMUNE SYSTEM ..(4) 7/18/2012 Main element at IS: Antigen and Antibody 332 09 011 Antibody 35
- 36. IMMUNE SYSTEM ..(3) Immune Cell Categories Receptor JCSSE 2012 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 36
- 37. OO’S PERSPECTIVE What isan 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. 37
- 38. OO’S PERSPECTIVE Why UML? Why Using UML? JCSSE 2012 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
- 39.
- 40. UML DIAGRAM JCSSE 2012 40
- 41. IS AT FUNCTIONAL’SVIEW IS as Use-case, to show IS as a Business Process functionalities at IS JCSSE 2012 Antigen Presenting <<include>> <<include>> Recognition Antigen Immune System <<extend>> Pathogen (from Use Case View) Destruction 41
- 42. STATIC VIEW OFIS Exogenous Antigens Lymphocytes JCSSE 2012 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 42
- 43. IS AT DYNAMICVIEW Exogenous Antigen Presenting JCSSE 2012 Endogenous By B-Cells Functional Recognition By T-Helper Cells By Phagocytes Destruction By T-Killer 43 Cells
- 44. EXOGENOUS ANTIGEN PRESENTINGACTIVITY DIAGRAM JCSSE 2012 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 ACTIVITYDIAGRAM JCSSE 2012 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 45
- 46. B-CELLS RECOGNITION ACTIVITYDIAGRAM T-Helper Cells B-Cells JCSSE 2012 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 JCSSE 2012 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 DESTRUCTIONBY T- KILLER CELLS T-Helper Cells T-Klller Cells Infected Cells JCSSE 2012 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
- 49. JCSSE 2012 CONCLUSION AND FUTURE WORKS 49
- 50. CONCLUSION AND FUTUREWORKS Immune system can be modelled using OO perspectives. It promises the better understanding JCSSE 2012 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. 50
- 51. JCSSE 2012 THANK YOU 51 pbasari@unpas.ac.id pbasari@gmail.com
- 52. JCSSE 2012 IMMUNE SYSTEM 52 Supplementary Slide
- 53. ELEMEN UTAMA SISTEMIMUN 7/18/2012 Elemen Sistem Imun: Antigen dan Antibody 332 09 011 Struktur Antibody 55
- 54. LYMPHOCITE: SEL PEMBENTUKANTIBODY T-Helper Cell B-Cell (CD4/T4) Lymphocite T-Cell T-Killer Cell T Suppresor Cell (CD8). 7/18/2012 332 09 011 56
- 55. CARA KERJA SISTEMIMUN 7/18/2012 332 09 011 60
- 56. SISTEM IMUN: CLONALSELECTION Conal Selection: 7/18/2012 332 09 011 63
Editor's Notes
- #8 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.
- #10 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.
- #11 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.
- #15 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]
- #18 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]
- #19 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]
- #22 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].
- #54 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.
- #55 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.
- #57 —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).
- #62 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.
- #63 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.