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Introduction to AIS Emma Hart Napier University, Scotland ICARIS 2005 Content • What is immunology anyway – And why do we care ? • Immunology for computer scientists – Some relevant biological details • From in-vivo IS to in-silico IS – Making the step scientifically • A decade of AIS research – a snapshot of existing AIS algorithms • The future of AIS – What should we be doing next ? 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) What is the Immune System ? Classical Cohen Matzinger Varela • The are many different viewpoints • These views are not mutually exclusive • Lots of common ingredients From a computational perspective: Computational Properties • • • • • • • • • Unique to individuals Distributed Imperfect Detection Anomaly Detection Learning/Adaption Memory Feature Extraction Diverse ..and more Systems that are: • • • • Robust Scalable Flexible Exhibit graceful degradation • Homeostatic A High Level Overview The Innate Immune System • The part of the immune system with which we are born • Doesn’t change or adapt • Complement system • Macrophages • Cytokines, natural killer cells http://www.aids-info.ch/e_te/aas-e-imm.htm The Adaptive Immune System http://altmed.creighton.edu/pelvic2/blyphocy tes.htm www.hwscience.com/ Bio/HAP/Lymphocyte.jpg • Adapts to recognise specific pathogens • Retains a memory which speeds up future responses • Consists primarily of lymphocytes which circulate around the body • Distributed detection system without any central control Adaptive Immunity epitope receptor lymphocyte • A detection event occurs when the receptor of a lymphocyte binds to an epitope on a pathogen • Strength of the bond is termed the affinity Low affinity structurally similar – high affinity • Each lymphocyte can have multiple, identical receptors Generating Receptor Diversity Adaptation – Affinity Maturation 1. activation 2. proliferation High affinity selected 3. differentiation antibody plasma cell memory cell cell death Low affinity no selection Affinity Maturation – A Darwinian process of variation and selection Initial B-cell population Clonal selection and hypermutation Adapted B-Cell population The Flaw in the Argument So Far… • Receptors generated at random or via somatic hypermutation might bind to self proteins – This would lead to autoimmunity • Tolerance is provided by a set of lymphocytes called T-helper cells Immature T-cells in thymus Ubiquitous self-proteins Self-tolerant T-cell death Matzinger’s View • Self/Non-self discrimination is not sufficient! – There is no immune reaction to foreign bacteria in the gut – No reaction to food! – Definition requires self to be confined to the subset actually seen by lymphocytes during maturation – Self changes throughout a body’s lifetime – Medically: • We can perform transplants (no attack against non-self) • Certain tumours are fought by the immune system (attack against self!) Matzinger’s View: Danger Theory • The immune system responds to danger, rather than non-self • It still fundamentally supports the need for discrimination,but now – There is no need to attack everything foreign – Just attack those things that are dangerous • Danger is measured by damage to cells that send out a distress signal when the cells die an un-natural death Jerne’s View: The Idiotypic Network • The cells in the immune system can recognise each other as well as pathogens! • An antibody has a paratope and an idiotope • Paratope-idiotope interactions leads to networks of interconnected antibodies Jerne’s View • There is no essential difference between the “recognized” and the “recognizer,” • Any given antibody might serve either, or both, functions. • Immune regulation is based on the reactivity of antibody with its own repertoire forming a set of self-reactive, self-reflective, self-defining immune activities Varela’s View • “From the immune system's perspective, it only “knows” itself “(Varela et al 1988). • Sustaining autonomous activity of the network gradually defines the system • Impacts (without pre-labelling) either – Smoothly integrate the system – Are rejected from the system • This is known as self-assertion self non-self Stewart’s View • The network provides a cellular communication pathway which allows the system to preserve a perfect integrity and maintain homeostasis • Defensive role of the immune system is secondary • Immune system was evolutionary selected as a consequence of its primordial function to provide this pathway Cohen’s View • IS is a cognitive system • It’s role is body maintenance • Significant departure from self/non-self view Cohen’s View • IS is a cognitive system • It’s role is body maintenance • Significant departure from self/non-self view • Forms an internal history • Self-organises • Learns • Memory • Makes deterministic decisions Cohen’s View • IS is a cognitive system • It’s role is body maintenance • Significant departure from self/non-self view • Detect current state of body’s tissues • Elicit an appropriate response – Co-respondence and patterns of activity • Response is result of collaboration of innate and adaptive components Cohen’s View • IS is a cognitive system • It’s role is body maintenance • Significant departure from self/non-self view • Defence against pathogen is just a special case of maintenance • Autoimmunity is required! – Body needs to recognise state of its own tissues – Antigen receptors exists to achieve this What is the Immune System ? - A lot of things! Classical Cohen Matzinger Varela AIS • There are many different viewpoints • They are not mutually exclusive! •AIS can pick and choose! In-vivo IS to In-silico IS • How do we make the jump ? • Historically, AIS was rooted in real immunology • Now, there is a tendency to observe-implement • (but lots of simple and effective algorithms developed) An Interdisciplinary Approach Mathematics Biology Computer Science Engineering Analysable, validated systems that fully exploit the underlying biology A Conceptual Framework Danger Signals modelling Probes, Observations, experiments Biological system Simplifying abstract representation Analytical framework/ principle Bio-inspired algorithms A Conceptual Framework modelling Probes, Observations, experiments Biological system Simplifying abstract representation Analytical framework/ principle Mathematical models Bio-inspired algorithms A Conceptual Framework modelling Probes, Observations, experiments Biological system Simplifying abstract representation Analytical framework/ principle Construct a computational model Bio-inspired algorithms A Conceptual Framework modelling Probes, Observations, experiments Biological system Simplifying abstract representation Abstract into algorithms suitable for an application Analytical framework/ principle Bio-inspired algorithms A Higher-Level Framework Cross-domain modelling Meta-probes Metarepresentation Meta-framework Novel-unified Algorithms A Meta Framework • Asking meta-questions of the computational framework will give attention to interesting properties: – Openness – Diversity – Interaction – Structure – Scale A Meta Framework • Asking meta-questions of the computational framework will give attention to interesting properties: – Openness – Diversity – Interaction – Structure – Scale How much continual growth or development is required in the system ? A Meta Framework • Asking meta-questions of the computational framework will give attention to interesting properties: – Openness – Diversity – Interaction – Structure – Scale How much is necessary, what does it cost , does it combat fragility ? A Meta Framework • Asking meta-questions of the computational framework will give attention to interesting properties: – Openness – Diversity – Interaction – Structure – Scale What are the levels of interaction between agents ? A Meta Framework • Asking meta-questions of the computational framework will give attention to interesting properties: – Openness – Diversity – Interaction – Structure – Scale Are different levels required between agents ? A Meta Framework • Asking meta-questions of the computational framework will give attention to interesting properties: – Openness – Diversity – Interaction – Structure – Scale How many agents are required ? When does “more” become “different” ? An Engineering Framework for AIS Solution Algorithms AIS Affinity Representation Application Timmis, De Castro A Framework for AIS Solution Shape-Space Algorithms AIS Affinity Binary Integer Real-valued Representation Application Symbolic A Framework for AIS Solution Algorithms AIS Euclidean Affinity Manhattan Hamming Representation Application A Framework for AIS Solution Algorithms Bone Marrow Models Clonal Selection AIS Affinity Negative Selection Positive Selection Representation Application Immune Network Models The Representation Layer • Real antibodies and antigens consist of molecules which interact via forces such as Van der Waals, electrostatic, hydrogen bonding etc. • How can we represent this computationally ? The Representation Layer The Representation Layer • Any molecule can be described a generalised shape (Perelson) – defines its chemical groups,shape and charge distributions • The generalised shape can be described as L parameters – Height,length,width of bumps on combining sites • An attribute string m=<m1,m2,..mL> consisting of L attributes can be regarded as a point in LL dimensional shape-space, m S The Representation Layer Shape-Space • For an animal possessing N antibodies – The shape-space S contains N points – The N points lie in a finite volume V – (there is only a restricted range of widths, lengths etc. that a site can assume) – Antigens are also characterized by generalised shapes whose complement lies in V The Representation Layer Shape-Space • An antibody can recognise any antigen whose complement lies within a small surrouding region of width ε (the cross-reactivity threshold) • This results in a volume vε known as the recognition region of the antibody V vε ε ε S vε The Representation Layer vε ε Not all balls are round…. • The shape of the recognition region doesn’t have to be spherical • Different shapes will endow a system with different properties: The Representation Layer Shape Space • The type of attributes in an application domain will define the type of shape-space adopted: – Real-valued shape-space (real-valued vectors) – Integer shape-space (integer values) – Hamming shape-space (attributes taken from finite alphabet of size k – Symbolic shape-space (different attribute strings, e.g “name”, “colour”) The Representation Layer Affinity Layer • Computationally, the degree of interaction of an antibody-antigen or antibody-antibody can be evaluated by a distance or affinity measure • The choice of affinity measure is crucial: • It alters the shape-space topology • It will introduce an inductive bias into the algorithm • It needs to take into account the data-set used and the problem you are trying to solve The Affinity Layer Hamming Shape-Spaces Distance measured by: • The number of complementary bits • R-contiguous bits • R-chunks • Rogers and Tanimoto • Multiple contiguous bits The Affinity Layer 00111 10110 x d i i i r Hamming Shape-Spaces Distance measured by: • The number of complementary bits • R-contiguous bits • R-chunks • Rogers and Tanimoto • Multiple contiguous bits 00111 10110 String and detector match if there is a sequence of size r where all bits are identical The Affinity Layer Introduces positional bias…. Hamming Shape-Spaces Distance measured by: • The number of complementary bits • R-contiguous bits • R-chunks • Rogers and Tanimoto • Multiple contiguous bits String 00111 Detector 10111 R-Chunk detector 1 101 R-Chunk detector 2 R-Chunk detector 3 011 111 Detector matches string if all the bits of d[i] are equal to the r symbols of the string in the window specified by the detector i The Affinity Layer Hamming Shape-Spaces Distance measured by: • The number of complementary bits • R-contiguous bits • R-chunks • Rogers and Tanimoto • Multiple contiguous bits String 00111 Detector 10111 R-Chunk detector 1 101 R-Chunk detector 2 R-Chunk detector 3 011 111 Detector matches string if all the bits of d[i] are equal to the r symbols of the string in the window specified by the detector i The Affinity Layer Hamming Shape-Spaces Distance measured by: • The number of complementary bits • R-contiguous bits • R-chunks • Rogers and Tanimoto • Multiple contiguous bits String 00111 Detector 10111 R-Chunk detector 1 101 R-Chunk detector 2 R-Chunk detector 3 011 111 Detector matches string if all the bits of d[i] are equal to the r symbols of the string in the window specified by the detector i The Affinity Layer Hamming Shape-Spaces • The number of complementary bits • R-contiguous bits • R-chunks • Rogers and Tanimoto • Multiple contiguous bits x d x d 2 x d i i r i i i i i i More selective than Hamming The Affinity Layer i Hamming Shape-Spaces Distance measured by: • The number of complementary bits • R-contiguous bits • R-chunks • Rogers and Tanimoto • Multiple contiguous bits D DH 2 li i Where DH = Hamming Distance Li is the length of each complementary region with 2 or more consecutive bits 00110011 11101100 11011110 The Affinity Layer 6+22+24=26 Real Valued Shape-Spaces • Euclidean Distance D L 2 ( Ab Ag ) i i i 1 • Manhattan Distance L D Abi Agi i 1 …..beware of the bias introduced…. The Affinity Layer The Algorithms Layer • Bone Marrow models (Hightower, Oprea, Kim) • Clonal Selection – Clonalg(De Castro), B-Cell (Kelsey) • Negative Selection – Forrest, Dasgputa,Kim,…. • Network Models – Continuous models:Jerne,Farmer – Discrete models: RAIN (Timmis), AiNET (De Castro) The Algorithms Layer Bone Marrow Models • Use gene libraries to store elements that can be combined to form antibodies • Elements in library can be evolved using a genetic algorithm • c libraries of length l cl possible antibodies A1 A2 A3 A4 A5 B1 B2 B3 B4 B5 A2 B5 C1 The Algorithms Layer C1 C2 C3 C4 C5 Clonal Selection – the Clonalg Algorithm 1. Initialisation 2. Antigenic presentation a. b. c. d. Affinity evaluation Clonal selection and expansion Affinity maturation Metadynamics 3. Cycle The Algorithms Layer Clonalg • Create a random population of individuals (P) 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics 3. Cycle The Algorithms Layer Clonalg • For each antigenic pattern in the data-set S do: 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics 3. Cycle The Algorithms Layer Clonal Selection 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics • Present it to the population P and determine its affinity with each element of the population 3. Cycle The Algorithms Layer Clonal Selection 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics • Select n highest affinity elements of P • Generate clones proportional to their affinity with the antigen (higher affinity=more clones) 3. Cycle The Algorithms Layer Clonal Selection 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics • 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 3. Cycle The Algorithms Layer Clonal Selection 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics 3. Cycle • Replace a number r of individuals with low affinity with randomly generated new ones The Algorithms Layer Clonal Selection 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics 3. Cycle • Repeat step 2 until a certain stopping criteria is met The Algorithms Layer Clonal Selection • End result of applying clonal selection is a set of antibodies which are representative of the antigenic data set: – Performs data reduction – Used for clustering – Used for classification (e.g new antigens can be presented, highest affinity antibody is indicative of class) Clonalg for Optimisation 1. Initialisation 2. Antigenic presentation a. Affinity evaluation b. Clonal selection and expansion c. Affinity maturation d. Metadynamics • We can use Clonalg for optimisation • Replace the set of patterns S by a function to be optimised f(x) • Affinity=value of the function 3. Cycle The Algorithms Layer Negative Selection Self strings Random Antibody repertoire Recognise ? Yes Reject No Available repertoire • Generate random set of antibodies • Determine affinity of each antibody with all elements in self-set • If affinity is greater than some threshold ε eliminate antibody • Repeat… The Algorithms Layer Negative Selection Self strings Efficient Methods of Antibody generation Recognise ? Yes Reject No Available repertoire • Generate random set of antibodies • Determine affinity of each antibody with all elements in self-set • If affinity is greater than some threshold ε eliminate antibody • Repeat… The Algorithms Layer Real-Valued Negative Selection Random detectors Dasgupta et al For each detector Evaluate quality detector overlap ? Mature detector Sufficient Non-self Coverage? Move detector Covers Self ? Clone Better detectors EXIT The Algorithms Layer Network Algorithm • Network algorithms based on the assumption that the immune system behaves dynamically even in the absence of external antigen • Many continuous versions of this algorithm, mainly due to immunologists – Farmer – Jerne – Varela and Coutinho • These models served as inspiration for discrete network models (Timmis, Neal, De Castro) The Algorithms Layer Network Algorithms • The dynamics can account for emergent properties such as learning, memory etc. • Summarised by Perelson Rate of population variation = Network stimulation Death of Network Influx of new - suppression + elements - unstimulated elements The Algorithms Layer Network Algorithms • Accounts for emergent properties such as learning, memory etc. • Summarised by Perelson Rate of population variation = Network stimulation Death of Network Influx of new - suppression + elements - unstimulated elements Network dynamics The Algorithms Layer Network meta-dynamics Discrete Network Algorithm (Timmis,Neal) 1. Initialisation 2. Antigenic Presentation For each antigen, do: a. Clonal selection, network interactions b. Metadynamics c. Clonal Expansion d. Somatic hypermutation e. Network Construction 3. Cycle The Algorithms Layer Network Algorithm 1. Initialisation 2. Antigenic Presentation For each antigen, do: a. Clonal selection, network interactions b. Metadynamics c. Clonal Expansion d. Somatic hypermutation e. Network Construction 3. Cycle The Algorithms Layer Create a network from a random subset of the antigens Network Algorithm 1. Initialisation 2. Antigenic Presentation For each antigen, do: a. Clonal selection, network interactions b. Metadynamics c. Clonal expansion d. Somatic hypermutation e. Network Construction 3. Cycle The Algorithms Layer Determine stimulation of every cell in the network Network Algorithm 1. Initialisation 2. Antigenic Presentation For each antigen, do: a. Clonal selection, network interactions b. Metadynamics c. Clonal Expansion d. Somatic hypermutation e. Network Construction 3. Cycle The Algorithms Layer Eliminate cells with low stimulation levels Network Algorithm 1. Initialisation 2. Antigenic Presentation For each antigen, do: a. Clonal selection, network interactions b. Metadynamics c. Clonal Expansion d. Somatic hypermutation e. Network Construction 3. Cycle The Algorithms Layer Reproduce the most stimulated cells proportional to stimulation level Network Algorithm 1. Initialisation 2. Antigenic Presentation For each antigen, do: a. Clonal selection, network interactions b. Metadynamics c. Clonal Expansion d. Somatic hypermutation e. Network Construction 3. Cycle The Algorithms Layer Mutate each cell inversely proportional to its stimulation Network Algorithm 1. Initialisation 2. Antigenic Presentation For each antigen, do: a. Clonal selection, network interactions b. Metadynamics c. Clonal Expansion d. Somatic hypermutation e. Network Construction 3. Cycle The Algorithms Layer Incorporate successful mutated clones into the network Network Algorithm 1. Initialisation 2. Antigenic Presentation For each antigen, do: a. Clonal selection, network interactions b. Metadynamics c. Clonal Expansion d. Somatic hypermutation e. Network Construction 3. Cycle The Algorithms Layer Network Algorithm • Stimulation of network cells calculated like this, where dis() is the Euclidean distance between two vectors S 1 dis ( p) 1 dis ( x) dis ( x) x 0 x 0 n Excitation based on distance to current pattern p Excitation based on distance to neighbouring cells The Algorithms Layer n Suppression based on distance to neighbours Other Network Algorithms • Many other versions of network algorithms in practice: – AiNET - De Castro (clustering) – Bersini, Hart (simulations) – Ishiguro, Hart (robotic applications) – Neal, Timmis (clustering) • Same basic principles, but variations in methods of calculating stimulation, suppression, metadynamics The Algorithms Layer Example Application Areas Computer Security Network models Data-Mining and classification Anomaly Detection Robotic Control Negative selection Optimisation Clonal Selection Bone Marrow The Future of AIS • • • • Lots of interesting immunology Lots of computational models Lots of algorithms Some great successes… The Future of AIS • • • • Lots of interesting immunology Lots of computational models Lots of algorithms Some great successes… Has it really made a difference ? The Future of AIS • Not as much as it could……… Can it really make a difference ? The Future of AIS • Not as much as it could……… Can it really made a difference ? YES Where do we go from here 1. Interdisciplinary and structured approach – Look back to the fundamental immunology – Look at the interaction of the immune system with other biological systems (neural, endocrine, etc.) – Perform proper theoretical analysis of systems using mathematics – Use proper developmental methodologies to abstract computational models Where do we go from here ? 2. Careful choice of application areas • Don’t just choose problem domains tackled by other biological paradigms • Think carefully about problem features • Consider what AIS can bring to a problem area that other paradigms cannot • Don’t apply AIS for the sake of it… Some Useful References • Previous ICARIS proceedings (2002-2005) • Artificial Immune Systems: A New Computational Intelligence Approach (De Castro and Timmis) • Design Principles for the Immune System and Other Distributed Autonomous Systems (Segel and Cohen) • Tending Adam’s Garden (Cohen) • Artificial Immune Systems and their Applications (Dasgupta)