* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Collaborative Bioinspired Algorithms
Survey
Document related concepts
Adoptive cell transfer wikipedia , lookup
Monoclonal antibody wikipedia , lookup
Molecular mimicry wikipedia , lookup
Immunocontraception wikipedia , lookup
Vaccination wikipedia , lookup
Herd immunity wikipedia , lookup
Complement system wikipedia , lookup
Autoimmunity wikipedia , lookup
DNA vaccination wikipedia , lookup
Sociality and disease transmission wikipedia , lookup
Adaptive immune system wikipedia , lookup
Immune system wikipedia , lookup
Polyclonal B cell response wikipedia , lookup
Cancer immunotherapy wikipedia , lookup
Social immunity wikipedia , lookup
Innate immune system wikipedia , lookup
Immunosuppressive drug wikipedia , lookup
Transcript
Artificial Immune Systems Razieh Khamseh-Ashari Department of Electrical and Computer Eng Isfahan University of Technology Supervisor: Dr. Abdolreza Mirzaei Outline Introduction to the Immune System Artificial Immune Systems A Framework to Design Artificial Immune Systems (AIS) Representation Schemes Affinity Measures Immune Algorithms Artificial Immune Systems 2 Outline Introduction to the Immune System Artificial Immune Systems A Framework to Design Artificial Immune Systems (AIS) Representation Schemes Affinity Measures Immune Algorithms Artificial Immune Systems 3 1. Immune System 2. AIS Concepts 3. Designing a Framework What is the Immune System ? a complex system of cellular and molecular components having the primary function of distinguishing self from not self and defense against foreign organisms or substances (Dorland's Illustrated Medical Dictionary) The immune system is a cognitive system whose primary role is to provide body maintenance (Cohen) Immune system was evolutionary selected as a consequence of its first and primordial function to provide an ideal inter-cellular communication pathway (Stewart) Artificial Immune Systems 4 1. Immune System Primary lymphoid organs 2. AIS Concepts 3. Designing a Framework Secondary lymphoid organs Tonsils and adenoids Thymus Spleen Peyer’s patches Appendix Bone marrow Lymph nodes Lymphatic vessels Artificial Immune Systems 5 1. Immune System 2. AIS Concepts 3. Designing a Framework Classical Immunity The purpose of the immune system is defence Innate and acquired immunity Innate is the first line of defense. Germ line encoded (passed from parents) and is quite ‘static’ (but not totally static) Adaptive (acquired). Somatic (cellular) and is acquired by the host over the life time. Very dynamic. These two interact and affect each other Artificial Immune Systems 6 1. Immune System 2. AIS Concepts 3. Designing a Framework Multiple layers of the immune system Pathogens Skin Biochemical barriers Phagocyte Innate immune response Lymphocytes Adaptive immune response Artificial Immune Systems 7 1. Immune System 2. AIS Concepts 3. Designing a Framework Innate Immunity May take days to remove an infection, if it fails, then the adaptive response may take over Macrophages and neurophils are actors Other actors such as TLR’s and dendritic cells (next lecture) are essential for recognition Artificial Immune Systems 8 1. Immune System 2. AIS Concepts 3. Designing a Framework Adaptive Immune System Artificial Immune Systems 9 1. Immune System 2. AIS Concepts 3. Designing a Framework Lymphocytes Carry antigen receptors that are specific They are produced in the bone marrow through B and T Cells are the main actors of the adaptive immune system Artificial Immune Systems 10 1. Immune System 2. AIS Concepts 3. Designing a Framework B Cell Pattern Recognition BCR or Antibody B-cell B-cell Receptors (Ab) Epitopes B cells have receptors called antibodies The immune recognition is based on the complementarity between the binding region of the receptor and a portion of the antigen called the epitope. Recognition is not just by a single antibody, but a collection of them Antigen Learn not through a single agent, but multiple ones Artificial Immune Systems 11 1. Immune System 2. AIS Concepts 3. Designing a Framework Processes within the Immune System (very basically) Negative Selection Censoring of T-cells in the thymus gland of T-cells that recognise self • Defining normal system behavior Clonal Selection Proliferation and differentiation of cells when they have recognised something • Generalise and learn Self vs Non-Self Artificial Immune Systems 12 1. Immune System 2. AIS Concepts 3. Designing a Framework Clonal Selection Artificial Immune Systems 13 1. Immune System 2. AIS Concepts 3. Designing a Framework Clonal Selection Clonal deletion (negative selection) Self-antigen Proliferation (Cloning) M M Antibody Memory cells Selection Differentiation Plasma cells Foreign antigens Self-antigen Clonal deletion (negative selection) Artificial Immune Systems 14 1. Immune System 2. AIS Concepts 3. Designing a Framework Affinity Maturation Responses mediated by T cells improve with experience Mutation on receptors (hypermutation and receptor editing) During the clonal expansion, mutation can lead to increased affinity, these new ones are selected to enter a ‘pool’ of memory cells • Can also lead to bad ones and these are deleted Artificial Immune Systems 15 1. Immune System 2. AIS Concepts 3. Designing a Framework Immune Responses Antibody Concentration Cross-Reactive Response Secondary Response Primary Response Lag Lag Response to Ag1 Lag Response to Ag1 Response to Ag2 ... ... Antigen Ag1 ... Response to Ag1 + Ag3 ... Antigens Ag1, Ag2 Artificial Immune Systems Antigen Ag1 + Ag3 Time 16 1. Immune System 2. AIS Concepts 3. Designing a Framework Summary Innate and adaptive immunity Focused on adaptive here Lymphocytes Negative selection Clonal selection Immune memory and learning Artificial Immune Systems 17 1. Immune System 2. AIS Concepts 3. Designing a Framework Further Immunology and Modelling 1. Immune System 2. AIS Concepts 3. Designing a Framework What is the Immune System ? • The are many different viewpoints • These views are not mutually exclusive classical • Lots of common ingredients danger network Artificial Immune Systems 19 1. Immune System 2. AIS Concepts 3. Designing a Framework Problems with the classical view What happens if self changes? What about things that are “not harmful” The Danger model was proposed Artificial Immune Systems 20 1. Immune System 2. AIS Concepts 3. Designing a Framework Danger theory (Matzinger 1994) it is not “non-self”, but “danger” that the IS recognises dangerous invaders cause cell death or stress these cells generate “danger signal” molecules • unlike natural cell death these stimulate an immune response local to the danger • to identify the “culprit” Artificial Immune Systems 21 1. Immune System 2. AIS Concepts 3. Designing a Framework Immune Network Theory Idiotypic network (Jerne, 1974) B cells co-stimulate each other Treat each other a bit like antigens Creates an immunological memory Suppression Negative response Paratope Ag 1 2 Idiotope 3 Antibody Activation Positive response Artificial Immune Systems 22 Outline Introduction to the Immune System Artificial Immune Systems Remarkable Immune Properties Concepts, Scope and Applications Brief History of AIS A Framework to Design Artificial Immune Systems (AIS) Representation Schemes Affinity Measures Immune Algorithms Artificial Immune Systems 23 1. Immune System 2. AIS Concepts 3. Designing a Framework Artificial Immune Systems: A Definition AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models, which are applied to complex problem domains [De Castro and Timmis,2002] Artificial Immune Systems 24 1. Immune System 2. AIS Concepts 3. Designing a Framework Concepts Specificity Diversity Clonal selection Affinity maturation Immunity memory Positive and negative selection Distributing ability Multi-layering Self-organization Anomaly detection Artificial Immune Systems 25 1. Immune System 2. AIS Concepts 3. Designing a Framework Scope of AIS Pattern recognition Fault and anomaly detection Data analysis (classification, clustering, etc.) Agent-based systems Search and optimization Machine-learning Autonomous navigation and control Artificial life Security of information Artificial systems Immune Systems 26 1. Immune System 2. AIS Concepts 3. Designing a Framework Scope of AIS 20 10 0 Artificial Immune Systems 27 1. Immune System 2. AIS Concepts 3. Designing a Framework The Early Days: Developed from the field of theoretical immunology in the mid 1980’s. 1990 – Bersini first use of immune algorithms to solve problems Forrest et al – Computer Security mid 1990’s Work by IBM on virus detection Hunt et al, mid 1990’s – Machine learning Artificial Immune Systems 28 Outline Introduction to the Immune System Artificial Immune Systems Remarkable Immune Properties Concepts, Scope and Applications Brief History of AIS A Framework to Design Artificial Immune Systems (AIS) Representation Schemes Affinity Measures Immune Algorithms Artificial Immune Systems 29 1. Immune System 2. AIS Concepts 3. Designing a Framework A Framework for AIS Solution Shape-Space Algorithms AIS Binary Integer Affinity Real-valued Symbolic Representation Application [De Castro and Timmis, 2002] Artificial Immune Systems 30 1. Immune System 2. AIS Concepts 3. Designing a Framework Shape-Space V An antibody can recognise any antigen whose complement lies within a small surrounding region of width 𝞮(the cross-reactivity threshold) This results in a volume v𝞮 known as the recognition region of the antibody v𝞮 𝞮 𝞮 v𝞮 𝞮 v𝞮 S Shape space (or solution space) Artificial Immune Systems [Perelson,1989] 31 1. Immune System 2. AIS Concepts 3. Designing a Framework Choice of Representation Assume the general case: Ab = Ab1, Ab2, ..., AbL Ag = Ag1, Ag2, ..., AgL Binary representation Continuous (numeric) Matching by bits Real or Integer, typically Euclidian Categorical (nominal) E.g female or male of the attribute Gender. Artificial Immune Systems 32 1. Immune System 2. AIS Concepts 3. Designing a Framework Representation – Shape Space Used for modeling antibody and antigen Determine a measure to calculate affinity Antigen Antibody Hamming shape space(binary) if Abi != Agi: 0 otherwise (XOR operator) Artificial Immune Systems 33 1. Immune System 2. AIS Concepts 3. Designing a Framework A Framework for AIS Solution Algorithms AIS Euclidean Affinity Manhattan Hamming Representation Application Artificial Immune Systems 34 1. Immune System 2. AIS Concepts 3. Designing a Framework Affinity Affinities: related to distance/similarity Examples of affinity measures Euclidean L ( Ab Ag ) D i 1 i 2 i L Manhattan D Abi Agi i 1 L Hamming D δ, where i 1 1 if Abi Agi δ 0 otherwise Artificial Immune Systems 35 1. Immune System 2. AIS Concepts 3. Designing a Framework Affinities in Hamming Shape-Space (a) Hamming distance 0 0 1 1 0 0 1 1 1 1 1 0 1 1 0 1 XOR 1 1 0 1 1 1 1 0 : Affinity: 6 (b) r-contiguous bits rule XOR : 0 0 1 1 0 0 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1 1 1 0 Affinity: 4 Artificial Immune Systems 36 1. Immune System 2. AIS Concepts 3. Designing a Framework Mutation - Binary Single point mutation Multi-point mutation Artificial Immune Systems 37 1. Immune System 2. AIS Concepts 3. Designing a Framework Affinity Proportional Mutation 1 0.9 0.8 0.7 ت=ت5 0.6 0.5 ت=ت10 0.4 Affinity maturation is controlled Proportional to antigenic affinity (D*) = exp(-D*) =mutation rate D*= affinity =control parameter ت=ت20 0.3 0.2 0.1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 D* Artificial Immune Systems 38 1. Immune System 2. AIS Concepts 3. Designing a Framework A Framework for AIS Solution Bone Marrow Models Algorithms Clonal Selection Negative Selection AIS Affinity Representation Positive Selection Immune Network Models Application Artificial Immune Systems 39 1. Immune System 2. AIS Concepts 3. Designing a Framework The Algorithms Layer Bone Marrow Models Clonal Selection CLONALG AIRS Negative Selection Positive Selection Network Models AINE aiNET Artificial Immune Systems 40 1. Immune System 2. AIS Concepts 3. Designing a Framework The Algorithms Layer Bone Marrow Models Clonal Selection CLONALG AIRS Negative Selection Positive Selection Network Models AINE aiNET Artificial Immune Systems 41 1. Immune System 2. AIS Concepts 3. Designing a Framework Bone Marrow Models Gene libraries are used to create antibodies from the bone marrow Use this idea to generate attribute strings that represent receptors Antibody production through a random concatenation from gene libraries An individual genome corresponds to four libraries: Library 1 A1 A2 A3 A4 A5 A6 A7 A8 A3 Library 2 Library 3 B1 B2 B3 B4 B5 B6 B7 B8 Library 4 C1 C2 C3 C4 C5 C6 C7 C8 B2 C8 A3 B2 C8 D1 D2 D3 D4 D5 D6 D7 D8 D5 D5 A3 B2 C8 D5 Expressed Ab molecule Artificial Immune Systems 42 1. Immune System 2. AIS Concepts 3. Designing a Framework Clonal Selection –CLONALG Initialisation Antigenic presentation 1. 2. a. b. c. d. 3. Affinity evaluation Clonal selection and expansion Affinity maturation Metadynamics Cycle Artificial Immune Systems 43 1. Immune System 2. AIS Concepts 3. Designing a Framework CLONALG Initialisation Antigenic presentation 1. 2. a. b. c. d. 3. Affinity evaluation Clonal selection and expansion Affinity maturation Metadynamics Create a random population of individuals (P) Cycle Artificial Immune Systems 44 1. Immune System 2. AIS Concepts 3. Designing a Framework CLONALG Initialisation Antigenic presentation 1. 2. a. b. c. d. 3. Affinity evaluation Clonal selection and expansion Affinity maturation Metadynamics For each antigenic pattern in the data-set S do: Cycle Artificial Immune Systems 45 1. Immune System 2. AIS Concepts 3. Designing a Framework CLONALG Initialisation Antigenic presentation 1. 2. a. Affinity evaluation b. Clonal selection and expansion Affinity maturation Metadynamics c. d. 3. Present it to the population P and determine its affinity with each element of the population Cycle Artificial Immune Systems 46 1. Immune System 2. AIS Concepts 3. Designing a Framework CLONALG Initialisation Antigenic presentation 1. 2. a. b. c. d. 3. Affinity evaluation Clonal selection and expansion Affinity maturation Metadynamics Select n highest affinity elements of P Generate clones proportional to their affinity with the antigen (higher affinity=more clones) Cycle Artificial Immune Systems 47 1. Immune System 2. AIS Concepts 3. Designing a Framework CLONALG Initialisation Antigenic presentation 1. 2. a. b. c. d. 3. Affinity evaluation Clonal selection and expansion Affinity maturation Metadynamics Cycle Mutate each clone High affinity=low mutation rate and vice-versa Add mutated individuals to population P Reselect best individual to be kept as memory m of the antigen presented Artificial Immune Systems 48 1. Immune System 2. AIS Concepts 3. Designing a Framework CLONALG Initialisation Antigenic presentation 1. 2. a. b. c. d. 3. Affinity evaluation Clonal selection and expansion Affinity maturation Metadynamics Replace a number r of individuals with low affinity with randomly generated new ones Cycle Artificial Immune Systems 49 1. Immune System 2. AIS Concepts 3. Designing a Framework CLONALG Initialisation Antigenic presentation 1. 2. a. b. c. d. 3. Affinity evaluation Clonal selection and expansion Affinity maturation Metadynamics Cycle Repeat step 2 until a certain stopping criteria is met Artificial Immune Systems 50 1. Immune System 2. AIS Concepts 3. Designing a Framework CLONALG nci =round((β*nh )/i) Artificial Immune Systems 51 1. Immune System 2. AIS Concepts 3. Designing a Framework Negative Selection Algorithms Forrest 1994: Idea taken from the negative selection of T-cells in the thymus Applied initially to computer security Developing the detector set Generate random strings (R0) Using the detector set Self strings (S) Match No Detector Set (R) Strings (e.g. credit card use patterns) Yes Detector Set (R) Match No Yes Reject Non-self Detected Artificial Immune Systems 52 1. Immune System 2. AIS Concepts 3. Designing a Framework Training ALCs with negative selection Artificial Immune Systems 53 1. Immune System 2. AIS Concepts 3. Designing a Framework Artificial Immune Network Timmis and Neal,2000 The ALCs interact with each other to learn the structure of a nonself pattern The ALCs in a network co-stimulates and/or co-suppress each other to adapt to the non-self pattern The stimulation level antigen stimulation network stimulation network suppression Artificial Immune Systems 54 1. Immune System 2. AIS Concepts 3. Designing a Framework aiNET Initialisation Antigenic presentation 1. 2. a. b. c. d. e. 3. 4. 5. 6. Affinity evaluation Clonal selection and expansion Affinity maturation Metadynamics Clonal suppression For each antigenic pattern in the data-set S do: Network interactions Network suppression Diversity Cycle Artificial Immune Systems 55 1. Immune System 2. AIS Concepts 3. Designing a Framework aiNET Initialisation Antigenic presentation 1. 2. a. b. c. d. e. 3. 4. 5. 6. Affinity evaluation Clonal selection and expansion Affinity maturation Metadynamics Clonal suppression Present it to the population P and determine its affinity with each element of the population Network interactions Network suppression Diversity Cycle Artificial Immune Systems 56 1. Immune System 2. AIS Concepts 3. Designing a Framework aiNET Initialisation Antigenic presentation 1. 2. a. b. c. d. e. 3. 4. 5. 6. Affinity evaluation Clonal selection and expansion Affinity maturation Metadynamics Clonal suppression Select n highest affinity elements of P Generate clones proportional to their affinity with the antigen (higher affinity=more clones) Network interactions Network suppression Diversity Cycle Artificial Immune Systems 57 1. Immune System 2. AIS Concepts 3. Designing a Framework aiNET Initialisation Antigenic presentation 1. 2. a. b. c. d. e. 3. 4. 5. 6. Affinity evaluation Clonal selection and expansion Affinity maturation Metadynamics Clonal suppression Mutate each clone High affinity=low mutation rate and vice-versa Select h highest affinity cells and place into memory set Network interactions Network suppression Diversity Cycle Artificial Immune Systems 58 1. Immune System 2. AIS Concepts 3. Designing a Framework aiNET Initialisation Antigenic presentation 1. 2. a. b. c. d. e. 3. 4. 5. 6. Affinity evaluation Clonal selection and expansion Affinity maturation Metadynamics Clonal suppression Network interactions Network suppression Diversity Cycle Eliminate all memory clones whose affinity with the antigen is less than a predefined threshold Artificial Immune Systems 59 1. Immune System 2. AIS Concepts 3. Designing a Framework aiNET Initialisation Antigenic presentation 1. 2. a. b. c. d. e. 3. 4. 5. 6. Affinity evaluation Clonal selection and expansion Affinity maturation Metadynamics Clonal suppression Network interactions Network suppression Diversity Cycle Determine similarity between each pair of network antibodies Artificial Immune Systems 60 1. Immune System 2. AIS Concepts 3. Designing a Framework aiNET Initialisation Antigenic presentation 1. 2. a. b. c. d. e. 3. 4. 5. 6. Affinity evaluation Clonal selection and expansion Affinity maturation Metadynamics Clonal suppression Network interactions Network suppression Diversity Cycle Eliminate all network antibodies whose affinity is less than a pre-defined threshold Artificial Immune Systems 61 1. Immune System 2. AIS Concepts 3. Designing a Framework aiNET Initialisation Antigenic presentation 1. 2. a. b. c. d. e. 3. 4. 5. 6. Affinity evaluation Clonal selection and expansion Affinity maturation Metadynamics Clonal suppression Network interactions Network suppression Diversity Cycle Introduce a random number of new antibodies into P Artificial Immune Systems 62 1. Immune System 2. AIS Concepts 3. Designing a Framework aiNET Initialisation Antigenic presentation 1. 2. a. b. c. d. e. 3. 4. 5. 6. Affinity evaluation Clonal selection and expansion Affinity maturation Metadynamics Clonal suppression Network interactions Network suppression Diversity Cycle Repeat 2 - 5 for a pre-defined number of iterations Artificial Immune Systems 63 aiNET Artificial Immune Systems 64 1. Immune System 2. AIS Concepts 3. Designing a Framework Artificial Immune Systems 65 1. Immune System 2. AIS Concepts 3. Designing a Framework aiNET The aiNet model uses the minimal spanning tree of the formed weighted-edge graph or hierarchical agglomerative clustering to determine the structure of the network clusters in the graph. The stopping condition of the while-loop can be one of the following Setting an iteration counter Setting the maximum size of the network Testing for convergence Artificial Immune Systems 66 1. Immune System 2. AIS Concepts 3. Designing a Framework aiNET on Data Mining Limited visualisation Interpret via MST or dendrogram Compression rate of 81% Successfully identifies the clusters Training Pattern Training Patt erns 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 11 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 55 1 1 1 1 1 2 1 2 1 1 1 2 1 1 1 1 2 1 2 2 2 2 5 1 1 5 2 2 1 1 5 5 2 2 5 5 1 1 1 2 2 2 5 5 2 5 5 5 5 5 2 2 1 1 5 5 5 5 2 2 2 2 2 2 5 5 5 2 2 1 5 5 5 5 5 1 2 2 1 1 1 1 5 5 5 5 1 1 1 2 2 66 5 5 1 2 2 2 2 6 6 5 5 5 5 2 2 2 66 6 6 55 5 5 5 2 2 1 1 1 2 2 2 22 2 5 1 1 1 2 5 2 2 5 5 5 2 2 2 2 1 5 5 1 1 1 2 2 5 5 5 5 5 5 1 2 2 2 5 5 5 1 1 2 2 2 1 1 5 2 2 1 5 1 2 2 2 2 5 5 2 1 1 1 5 5 1 5 5 1 5 5 1 5 5 1 2 2 1 1 1 1 1 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0.8 0.6 0.4 0.2 0 4 4 4444 44 444 4 4 4 4 4 4 4 4 4 4 4 44 4 4 4 4 4 4 4 4 4 4 4 4 44 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 44 4 4 4 4 4 4 4 4 444 4 443 3 3 3 3 33 3 3 3 3 3 3 33 33 3 3 3 3 33 3 3 3 3 33 3 3 3 3 3 3 3 3 3 33 3 3 3 3 3 3 3 3 3 3 33 3 3 3 33 3 3 3 3 3 3 3 3 3 3 3 3 3 3 33 3 33 3 3 3 3 3 3 3 33 0 0.2 8 8 8 8 88 8 8 7 8 8 7 7 8 8 7 7 8 7 8 8 7 7 88 8 7 8 8 7 8 777 8 7 7 88 8 77 8 7 8 8 7 7 7 88 8 7 8 7 7 7 8 8 8 7 7 7 8 7 7 8 8 7 8 88 7 8 777 8 8 7 8 8 8 77 8 8 8 8 8 77 8 7 8 77 7 7 7 8 7 7 0.4 0.6 0.8 1 Result immune network Artificial Immune Systems 67 1. Immune System 2. AIS Concepts 3. Designing a Framework aiNET on multimodal optimisation Initial population Final population Artificial Immune Systems 68 1. Immune System 2. AIS Concepts 3. Designing a Framework Results – Multi Function aiNET CLONALG Artificial Immune Systems 69 Reference L. N. de Castro, J. I. Timmis, Artificial immune systems as a novel soft computing paradigm, Soft Computing 7 (2003) 526–544 Adndries P.Engelbrecht ,Computational Intelligence An introduction D. Dasguptaa, S.Yua, et al, Recent Advances in Artificial Immune Systems: Models and Applications, Applied Soft Computing 11 (2011) 1574–1587. J. Zheng ,Y. Chen, A Survey of artificial immune applications, Artif Intell Rev (2010) 34:19–34 Artificial Immune Systems 70 Thank You Artificial Immune Systems 71 Questions? Artificial Immune Systems 72