Artificial Immune Systems: A typical 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] But I think this might be a bit limiting in terms of definition..
A bit of history … Developed from the field of theoretical immunology in the mid 1980s. Suggested we might look at the IS 1990 – Ishida first use of immune algorithms to solve problems Forrest et al – Computer Security mid 1990s Hunt et al, mid 1990s – Machine learning ICARIS conference series, ARTIST network
History (cont.) Started quite immunologically grounded Bersinis work with Varela Forrest's work with Perelson Kind of moved away from that, and abstracted more Now there seems to be a move to go back to the roots of immunology and greater interaction … but how do we manage this interaction to make it worth while for all concerned …. ?
What does engineering have to do with immune systems? Unique to individuals Distributed Imperfect Detection Anomaly Detection Learning/Adaptation Memory Feature Extraction Diverse..and more Robust Scalable Flexible Exhibit graceful degradation Homeostatic Systems that are:Computational Properties
Example Application Areas Computer Security Optimisation Robotic Control Data-Mining and classification Data-Mining and classification Anomaly Detection Network models Clonal Selection Negative selection Bone Marrow
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)
Immunologists Disagree There is an obvious and dangerous potential for the immune system to kill its host; but it is equally obvious that the best minds in immunology are far from agreement on how the immune system manages to avoid this problem Langman, R. E. and Cohn, M., Editorial Summary, Seminars in Immunology, vol. 12, pp. 343-344, 2000
What is the Immune System ? S/NS Cohen Varela Matzinger The are many different viewpoints Lots of common ingredients (??) All tell us about information processing …
Clonal Selection as an example for information processing
Immune Responses - continual information processing
An `artificial immune system in an engineering context Keeping ATMs working
ATMs High usage machines Dont go wrong that often, but if they do it can be expensive Create logs when they go wrong It is possible to use that data to immunise a system at a number of levels via an Adaptable Error Detection system
Adaptable error detection as a means to improved availability Error detection Improved error detection enhances availability Error detection techniques usually exploit known systems profile for detecting error states and behaviour These error detection techniques are limited to the detection of errors known at design-time of systems Adaptable error detection is aimed at detecting errors that were not known during the design-time of systems
A Framework for AIS Algorithms Affinity Representation Application Solution AIS [De Castro and Timmis, 2002]
Within the AIS Framework Representation Sequence of states --> fatal state Affinity measure Similarity of sequences (weighted) Algorithm Dynamic clonal selection [De Lemos et al, 2007]
Architecture for Immune AED [De Lemos et al, 2007]
Results AISEC v1AISEC v2 Accuracy Mean detection Time interval 85.78%(6) 89.93%(.2) 86.67%(5) 91.53%(.16) 0:11:21:22 (0:5:20:16) 0:01:03:30 (0:0:9:35) 0:12:31:10 (0:3:36:37) 0:02:25:41 (0:0:6:16) [De Lemos et al, 2007]
A bit of time for reflection … Are we really capturing immune system complexity in our AIS? (or should we even care?)
modelling Analytical framework/ principle A Framework for Thinking about and Developing AIS Biological system Simplifying abstract representation Bio-inspired algorithms Probes, Observations, experiments DC activation, T-cell clonality Mathematical models Construct a computational model Abstract into algorithms suitable for an application Analysable, validated systems that fully exploit the underlying biology [Stepney et al, 2005]
Interdisciplinary interaction via immune modelling What is in it for both sides?
Modelling Approaches Mathematical E.g. Differential equations Computational Various calculi Agent based modelling UML We are investigating a number of different approaches at the moment to see which (if any) are useful (both to us and immunologists)
UML UML = Unified Modelling Language Collection of 13 diagrams for general purpose modelling Mostly used in software engineering for modelling the real world... Diagrams fall into 2 categories Structural Behavioural
Modelling Complex Systems with UML Most of the diagrams in UML we have not found to be that useful Ones that we have: Class diagrams: what things are State diagrams: how things behave Activity diagrams: how things interact
UML Perspectives Conceptual Concepts of the domain Implementing classes are related, but doesn't have to be one-to-one mapping Specification Interfaces Implementation Code specifics
Process Oriented Approaches Processes are again a natural way to think about biological systems Investigating two approaches of modelling this way Current research is investigating the development of a pattern language for complex systems (at many levels) Modelling infrastructure (tool set, and method) for the modelling of complex systems - our drive is the immune system Occam- is our target language which allows us to build large-scale, highly parallel simulation Currently working with the IIU at York on the development of models of expansion and contraction of blood vessels in lymph nodes and also the formation of granulomas under certain infections (also making use of UML in this context)
Extensible Architecture for Homeostasis http://www.bioinspired.com/research/xArcH/index.shtml
-Calculus The -calculus [Milner 1999]. A process calculus designed to model communicating mobile systems. What is mobility?
Stochastic -Calculus -calculus is good for qualitative analysis of systems, Stochastic allows quantitative. Associates every activity with a rate parameter r [0, ].
Why use -Calculus? Can model the interactions between biological components directly, possibly more intuitive (in some cases) than ODE modeling. Can perform qualitative analysis through their bi- simulation equivalence. Can perform quantitative analysis through simulation SPiM, BioSpi. Through analysis can hopefully abstract what it is about the biological system that gives it its behaviour.
Some interesting immunology: Tunable T-cell receptors Classic immunology suggests a clear recognition of self/non-self by randomly generated repertoire of cells - how is this possible? Tunable activation threshold (TAT): Proposed by [Grossman, 1992] to help explain mechanisms for self-tolerance. T Cells are mostly discussed and are viewed as having tunable thresholds with which dictate proliferation and differentiation and therefore react only to changes in the environment and not any one specific interaction The implications are: Self-reactive T-cells can exist but …... they require generally higher affinity for antigen, or a higher avidity is required, i.e. the rate and amount by at which peptides are presented is faster for antigen.
One small part … Excitatory and Inhibitory factors are produced when the T cell binds via its T Cell Receptor A war of phosphorylation between a kinase and a phosphatase. If kinase activity is higher than phosphatase causes phosphorylation. If phosphatase activity is higher than kinase causes dephosphorylation.
Why might this TAT idea be useful to engineers? The real-world is hard, and building systems that can cope with a variety of input, that changes over time, is difficult If we could have a system of agents that can tune themselves to tolerate, or not, certain input.. that would be very useful.. It would allow us to to begin to capture homeostasis …. Look at patterns of response
Lymphocyte Entry to the Lymph Node through High Endothelial Venules http://www.cosmos-research.org
On-going modelling work Collaboration with the Infection and Immunology Unit at York Early stages (no simulation as yet, still under development), have some basic models Provide support for the hypothesis: The increase in lymphocyte numbers in lymph node during an immune response is a direct result of migration rather than proliferation of existing lymphocytes in the lymph node
38 Lymph Nodes Immune organs where adaptive immune response initiated and antibodies produced Hundreds throughout body Cells enter though blood or lymphatic system
39 Venules Small blood vessels Bring de-oxygenated blood to the veins from capillary bed
40 High Endothelial Venules (HEV) Certain areas of the lymph node venule network are made up of HEVs HEVs characterised by tall and plump endothelial cells Endothelial Cell
42 Pericytes Cells that wrap around small blood vessels Act as scaffolding Similar to smooth muscle cells Constriction and dilation regulates diameter and blood flow of vessel Endothelial Cell Pericyte
43 Lymphocyte Migration (1) Lymphocytes enter lymph node through HEVs Initiate in a rolling process Under certain conditions, lymphocytes slow and squeeze though between endothelial cells
44 Lymphocyte Migration (2) Rolling, slowing and migration mechanism controlled by cell surface molecules and receptors (selectins, integrins, chemokines)
45 Lymphocyte Migration (3) A chemical signal molecule (chemokine) emitted in HEV crucial to lymphocyte migration HEVs facilitate lymphocytes migration but exclude other leukocytes (white blood cells) Quarter of circulating lymphocytes leave blood after entering HEV Migration through venule takes between 10 and 20 minutes
46 Number of cells in millions Experimental data Our immunologists have measured Number of lymphocytes in a node during response Relationship between pericyte dilation (distance from vessel) and blood vessel size
47 Lumen Size in nm Venule Perimeter in nm Pericyte Distance in nm
What are we doing with this? Developing UML models of the rolling process For the most part this has been done. Developing simulations First without space, then with space Output will be (in the first instance) a graph showing lymphocyte numbers over time Number of challenges Time, space etc. Importantly, we are reviewing the process of modelling. What assumptions do we make What problems do we encounter What tools work and what dont (and why)
A wider field than ever before? Three types of AIS people: 1. Literal school : Those who try and build things to do what the IS does (e.g. security systems) 2. metaphorical school: Those who use the IS as inspiration, but may be far from the what they IS actually does e.g. optimisation algorithms 3. modelling school: Those who try and understand the IS through a series of models (computational and mathematical) e.g. models of self/non-self or tunable activation thresholds [Cohen, 2007]
The great possibility for interaction Use of modelling tools and the development of new tools CoSMoS project http://www.cosmos-research.org Engage the experimentalist They want predictions - models should be able to help Through good modelling, engineering can also reap the benefit through a greater understanding of the immune system
References [Cohen, 2007] Computing the state of the body. Nature Rev. Imm. 7, 569-574 (2007) [De Lemos et al, 2007] R. De Lemos, J. Timmis. M. Ayara, and S. Forrest. Immune Inspired Adaptable Error Detection for Automated Teller Machines. IEEE SMC Part B. [Forrest and Beachemin, 2007] Computer Immunology. Immunological Reviews. Vol. 216. [Timmis 2007] J. Timmis. Challenges for Artificial Immune Systems. Natural Computation. [Stepney et al. 2006] S. Stepney, R. Smith, J. Timmis, A. Tyrrell, M. Neal and A.Hone. Conceptual Frameworks for Artificial Immune Systems, International Journal of Unconventional Computing. 2006. [De Castro and Timmis,2002] L. De Castro and J. Timmis. Artificial Immune Systems; A New Computational Intelligence Paradigm. Springer. 2002. [Farmer et al, 1986] Farmer, J. D., N. H. Packard and A. Perelson. "The Immune System, Adaptation, and Machine Learning." Physica D 22(1-3) (1986): 187-204 [Owens et al,2008] Owens, N, Timmis, J. Tyrrell, A. and Greensted, A. Modelling the Tunability of Early T-cell Signaling Events. ICARIS 2008.
Acknowledgements Paul Andrews /Susan Stepney / Amelia Ismail (CoSMoS) Lisa Scott, Mark Coles (IIU) Nick Owens / Andy Greensted / Andy Tyrrell (Xarch)
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