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Mark Read's Lecture from AWASS 2013
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Capturing the Immune System: From the wet-‐lab to the robot,
building be;er quality immune-‐inspired engineering solu=ons
Dr. Mark Read Department of Electronics, The University of York
Talk Overview • Ar=ficial immune systems are engineered systems that
take inspira=on from the immune systems’ organiza=on and/or func=on
Bio-‐inspired algorithms/systems
• Immune System Func=on – Why seek immune inspira=on?
• Understanding and Capturing Immune System Principles – How to replicate that which you do not understand?
• Adop=ng Immune Inspira=on – Robots with Immune Systems?
The Immune System
A rich source of inspira=on
The immune system (IS)
• Large collec=on of cells, molecules and organs responsible for maintaining health of the host
The immune system (IS)
• What it is responsible for: – Iden=fying & clearing pathogens – Clearing tumors – Clearing dead cells and debris – Growing and shaping =ssues – Maintaining general health of the host
• IS must: – Differen=ate harmful/dangerous and healthy contexts – Correlate harm/health with causes – Not a5ack the host
[Cohen 2004]
IS Complexity
• Insanely complex. • Data needs to be integrated and understood • IS is a complex system, evolved through ages, adop=ng short term gains, with no organizing principles.
[Kindt 07]
IS func=on • Composed of a great many cell types – T, B, Macrophage, DC, NK, NK-‐T – More, and subsets of all of these
• Roughly composed of two halves – Innate, evolu=onarily conserved – Adap=ve, bespoke reac=on to infec=on
• Complement system, not cellular • ‘Communica=on channels’ also complex – Cytokines/receptors with overlapping func=on
Innate immunity • Fast response to known pathogens • Similar from one individual to the next • Skin • Phagocy=c cells • DCs and Macrophages secrete soluble factors – Complement system – ROS, NO, other harmful chemicals
• S=mulated by contact with par=cular pa;erns – E.g. bacteria, evolu=onarily conserved structures
• Differen=ates harmful/not • Interacts with adap=ve system
Adap=ve immunity
• Slower response to pathogens – An=bodies, T cells, B cells
• Specific, bespoke for par=cular pathogen
• Each individual’s adap=ve IS is unique
• Driven by the innate response
• Specificity improves during prolifera=on (for B cells)
Walk-‐through immune response
Primary and secondary responses
• IS has a “memory” • Generally get sick less as you get older
Autoimmunity & Regula=on • Fast-‐evolving pathogens? • Genera=on of new receptors – DNA recombina=on
• Thymus & bone marrow in nega=ve selec=on
• Not complete! – Auto-‐immune cells reside in all of us – Ordinarily, they are suppressed
Autoimmunity as a malfunc=on of regula=on • Peripheral tolerance • ‘By-‐stander’ Treg-‐regula=on
• Specific regula=on
Immune system proper=es
• Interest in the immune system? – Adapta=on – Pa;ern matching – Decentraliza=on – Self-‐organizing – Self-‐regula=ng – Op=miza=on – Memory – Homeostasis
Understanding and Capturing Immune System Principles
How to replicate that which you do not understand?
So you want to create an AIS?
• Typical instan=a=ons – Anomaly detec=on
• Nega=ve selec=on • Danger theory
– Op=miza=on • Clonal selec=on
– Clustering & classifica=on • Modified clonal selec=on
• Not a very diverse range of inspira=on • Not really immune “systems” – Integra=on of IS principles could lead to more sophis=cated applica=ons?
[Hart 2008]
Capturing the IS
• IS a much richer source of inspira=on than has been typically adopted
• But its hard • The typical approach – Iden=fy some interes=ng aspect of immunology – Read a textbook – `pretend’ that you understand it (trained biologists don’t understand a lot of this)
– Get hacking!
[Stepney 2005]
Capturing the IS
• Are there be;er ways to capture IS proper=es? • What is the principle challenge here?
[Tieri 2012]
Biological complexity
• We don’t really understand the biological systems we are trying to capture.
• Major debates in immunology about fundamental immune func=on – CD4Th ‘help’, tolerance
• Not necessarily a problem – As long as there is a coherent model, we can run with it. – … if we understand it
[Andrews 2005]
Conceptual Framework
[Stepney 2005, Andrews 08]
• What is the problem domain? • How do you select appropriate biological inspira=on?
Modelling to understand
• Models and simula=ons demonstrate whether our theories explain what we observe – (they usually don’t)
• What is important, what is not? • What can be len out?
• Giving back: In silico experimenta=on
• Turns out we don’t even know how to build models par=cularly well…
Modelling to understand
• So how do you go about modelling biological phenomenon?
• Typical approach – Iden=fy some interes=ng aspect of immunology – Read a textbook – `pretend’ that you understand it (trained biologists don’t understand a lot of this)
– Get hacking!
• Sound familiar?
CoSMoS Process
[Andrews 2010, Bown 2012]
• A principled approach to inves=ga=ng complex system phenomena
• Emphasizes domain expert engagement and documen=ng assump=ons
Domain: Experimental Autoimmune Encephalomyeli=s (EAE)
[Kumar 1996 (redrawn)]
• Murine autoimmune disease, model for MS • Spontaneous recovery, iden=fying cells responsible
Domain: Experimental Autoimmune Encephalomyeli=s
[Read 2011]
What do we want to know?
• Inves=gate role of CD8Treg in media=ng recovery. • How efficient is this killing?
Domain Modelling I
• Itera=ve, DM engagement • UML
[Read 2011, 2009, manuscript in prep]
Domain Modelling II
• Ac=vity diagrams • Capture how cellular events hypothesized to a par=cular outcome
• Decompose disease into manageable subsets.
[Read 2011, 2009, manuscript in prep]
Domain Modelling III
• State machine diagrams capture lowest level en=ty behaviours
• Ques=ons concerning orthogonality
[Read 2011, 2009, manuscript in prep]
Plasorm Modelling
• State machine diagrams translated into code
• Emergent phenomena removed
• Implementa=on details added
Simula=on Plasorm
Results model
• Compare simula=on results with real-‐world observa=ons
• Perform in silico experiments
Baseline results Control Disable regula=on
Real m
ice
Simula=
on
Characterizing regulatory efficacy I
• How efficient is this killing?
Characterizing regulatory efficacy II
Regulatory Efficacy
Death (%)
Clinical Episodes (%)
1 2 3
100% 15.0 99.8 0 0
20% 16.0 99.8 0 0
5% 22.0 99.4 0.6 0
2% 26.6 85.0 12.4 2.6
0% 29.0 56.8 31.1 12.2
Th1 @ 40 days control
We have a model
• Now what? • Extract organizing principles from the models • Sensi=vity analysis – Iden=fies key components and pathways
• Need to find the analogy between the model, and the applica=on domain
• For EAE?
Don’t have a par=cular domain in mind
Mapping IS concepts to a domain
• For swarm-‐repair • Granulomas
[Ismail 11]
Granuloma Forma=on Algorithm
Par=al Failure
[Ismail 2011]
Par=al Failure + Granuloma
[Ismail 2011]
AdopDng Immune InspiraDon
Robots with immune systems?
Characterizing the ‘AIS Prac==oner’
• Engineers don’t speak Immunologist. • Intermediary between immunology & engineering
[Hart 2013]
Swarm Robo=cs • Swarm intelligence + robo=cs • Complex group behaviours emerge from simple decentralized individuals
• Robustness, flexibility, scalability – Apparently not without limits though
• Applica=ons, e.g., search and rescue [Bayindir 2007, Bjerknes 2010]
CoCoRo – the domain • Can IS-‐inspira=on be used to provide fault tolerance in CoCoRo?
CoCoRo Immunity • Fault tolerance • Immunity operates at 3 levels
Receptor Density Algorithm -‐ Inspira=on
[Owens 2010]
Receptor Density Algorithm
Single sensor anomaly detec=on
Gyroscope data
Single sensor anomaly detec=on result
Gyroscope Y
Single sensor anomaly detec=on result
Gyroscope X
Mul=-‐sensor anomaly detec=on
Mul=-‐sensor anomaly detec=on
• Correla=ons in sensor stream data – And what the OS thinks the AUV is supposed to be doing
• Sensors give overlapping perspec=ves of same secenario
• Spot the odd one out – Contextualize anomalies – Sensor/actuator anomaly?
Failure Mode Effect Analysis (FMEA) • Offline algorithm analysis • Iden=fy the algorithmic/swarm-‐level impact of hardware/
subsystem failures in an AUV • Informs algorithmic design, recovery mechanisms design • Performed on shoaling
– And relay chain… but I don’t want to give anything away :o)
SHOALING VIDEO HERE
FMEA results on shoaling
Blue light transmission failure Leads to the most effects Most common effects are collisions and ge}ng lost Anchoring is disrup=ve, but not the most prevalent fault
CoCoRo Immunity • How does this all fit together? • EAE again – Grading controller ‘disease’? – Recovery likely to be disrup=ve – Strength of response linked to state of disease
Summary
• IS very rich source of interes=ng behaviours – Pa;ern recogni=on, anomaly detec=on, memory, decentraliza=on, self-‐organizing, self-‐regula=ng
• Capturing it is difficult – It is not yet well understood – Methodologies for reasoning by model/simula=on – Extrac=ng key principles/components – Try to lose the immunological nomenclature
• Swarm immunity can be more than one algorithm – Systemic, with layers feeding into one another
The possibili=es
References • PS Andrews. An Inves=ga=on of a Methodology for the Development of Ar=ficial Immune Systems: A Case-‐Study in Immune Receptor
Degeneracy, PhD Thesis, the University of York, 2008. • PS Andrews, J Timmis. Inspira=on for the Next Genera=on of Ar=ficial Immune Systems. LNCS 3627:126-‐138, 2005. • PS Andrews et al. The CoSMoS Process Version 0.1: A Process for the Modelling and Simula=on of Complex Systems. Technical Report
Number YCS-‐2010-‐453. Department of Computer Science, University of York, 2010. • L Bayindir and E Sahin. A review of studies in swarm robo=cs. Turkish journal of electrical engineering and computer science, 15(2):
115-‐147, 2007. • J Bjerknes and A Winfield. On fault-‐tolerance and scalability of swarm robo=c systems. DARS 2010, Springer Tracks in advanced robo=cs
series 1:1-‐12, 2010. • J Bown et al. Engineering Simula=ons for Cancer Systems Biology. Current Drug Targets 13(12):1560-‐1574, 2012. • IR Cohen. Tending Adam’s Garden : Evolving the Cogni=ve Immune Self. Elsevier Academic Press, August 2004. • E Hart, J Timmis. Applica=on areas of AIS: The past, the present and the future. Applied Son Compu=ng (8):191-‐201, 2008. • E Hart et al. On the role of the AIS Prac==oner. Abstract accepted to ICARIS track at ECAL 2013. • AR Ismail. Immune-‐inspired self-‐healing swarm robo=c systems. PhD Thesis, the University of York, 2011. • TJ Kindt et al. Kuby Immunology. W. H. Freeman and Company, 6th edi=on, 2007. • V Kumar, K Stellrecht and E Sercarz. Inac=va=on of T Cell Receptor Pep=de-‐specific CD4 Regulatory T Cells Induces Chronic
Experimental Autoimmune Encephalomyeli=s (EAE). Journal of Experimental Medicine (184):1609-‐1617, 1996 • N Owens. From Biology to Algorithms. PhD Thesis, The University of York, 2010. • M Read et al. A Domain Model of Experimental Autoimmune Encephalomyeli=s. 2nd Workshop on Complex Systems Modelling and
Simula=on.pp:9-‐44, 2009. • M Read et al. Techniques for Grounding Agent-‐Based Simula=ons in the Real Domain: A Case Study in Experimental Autoimmune
Encephalomyeli=s. MCMDS 18(1):67-‐86, 2012. • M Read. Sta=s=cal and Modelling Techniques to Build Confidence in the Inves=ga=on of Immunology through Agent-‐Based Simula=on.
PhD Thesis, the University of York, 2011. • S Stepney et al. Conceptual Frameworks for Ar=ficial Immune Systems. Interna=onal Journal of Unconven=onal Compu=ng 1(3):
315-‐338, 2005. • J Timmis et al. Immuno-‐Engineering. IFIP World Computer Congress, IEEE Press 268:3-‐17, 2008. • P Tieri et al. Char=ng the NK-‐kB Pathway Interactome Map. Plos One 7(3): e32678, 2012.
