Collaborative Bio-Inspired Algorithms Lecture 8: Immune System … · 2011-01-16 · Collaborative...

Lecture 8

Collaborative Bio-Inspired AlgorithmsLecture 8: Immune System Modelling

Prof Jon Timmis

October 29, 2010

Lecture 8

Outline

Background to Modelling

Immune Modelling Case StudiesCase study 1 : Lymphocyte Entry to the Lymph Nodethrough HEV

Bridging Immunology and Engineering

Lecture 8


Complex systems

I Complex systems are a collection of component systemsI Components interact through environmental mediaI Components may be very simple or very complexI E.g. the immune system ...

I Potential for emergent behavioursI Behaviours that are not simply the sum of the outputs of the

component systemsI We are dealing with homogeneous complex systems

I Very large number of components e.g. cellsI Small number of sorts of componentI Example: the immune system

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Modelling

I Models are an abstraction to aid understanding ordescription

I Biologists and software engineers use diagrams todescribe static structures and patterns of interaction

I For a whole complex system, models need to describefeatures of component systems, high-level system, andenvironment

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Simulation

I Simulations:I Execution of the model components, many times, in parallelI Explicitly provide time, space and environmentI Key features of the (dynamic) environment provide the

context for component behaviour and interactionI Simulation issues:

I A significant concern in complex systems is the validity ofsimulation

I Poor choice of variables can give spurious equivalencegiving little insight in to mechanisms and behaviours

I The chosen components and environment has a significanteffect on the simulation outcomes

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Immune Modelling Case Studies

Case study 1 : Lymphocyte Entry to the Lymph Node through HEV

Lymphocyte Entry to the Lymph Node through HEV

I Blood-borne lymphocytes enter functional tissue of lymphnode through walls of high endothelial venules (HEV)

I During an immune responseI HEVs dilateI The number of lymphocytes in lymph node increases

I Does dilation account for the increased numbers?I Under what conditions is migration optimised?

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HEV’s

(a) Venules: small blood vessels thatbring de-oxygenated blood to theveins form capillary bed

(b) High Endothelial Venules (HEV)which are characterised by plumpendothelial cells

Figure: Venules in and HEV

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HEVs in a Lymph Node

Figure: Many HEV’s in the lymph node

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Pericytes

Figure: Cells that wrap around small blood vessels and act as a“scaffolding” around the blood vessel. Similar in nature to musclecells.

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Lymphocyte Migration

I Lymphocytes enter lymph node through HEVsI Initiate in a rolling processI Under certain conditions, lymphocytes slow and squeeze

though between endothelial cells

I Constriction and dilation regulates diameter and blood flowof vessel

I Rolling, slowing and migration mechanism controlled bycell surface molecules and receptors (selectins, integrins,chemokines)

I We have experimental data for amount of cells, time takenfor rolling, sizes of vessels . . .

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Figure: Rolling process of lymphocytes and their migration

The increase in lymphocyte numbers in lymph node during animmune response is a direct result of migration rather thanproliferation of existing lymphocytes in the lymph node

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A purpose for the simulation

I There should be a reason for what we are doing as it willeffect our design and implementation

I In this case:I Implement something that is biologically faithfulI Aid hypotheses testing

I Desired output:I Numerical data under different conditionsI A format that allows insight into in vitro experimental data

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The CoSMoS Process

Figure: CoSMoS Modelling Process

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A domain model of the biology

I Before we construct a simulation, we identify the relevantcomponents and behaviours in the form of a domain model

I Population of homogeneous lymphocytes interacting in anenvironment

I Environment:I Parts of the body with which the lymphocytes interactI Tube (HEV) consisting of HE cells and pericytes form of a

tubeI Lymphocyte behaviour:

I We model different environments lymphocytes passthrough as states

I Transitions occur when a lymphocyte moves betweenenvironments

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State diagram model for a lymphocyteStart: lymphocyte is ‘born’

I Creation transits to Blood circulation

HEV

Lumen

RollingLymph

Node

Circulation

BloodDeath

Creatio

n

Enter HEV

Exit HEV

Dis

asso

ciat

e Cap

ture

Migrate

Dra

in

Stop

Start

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State diagram model for a lymphocyteBlood Circulation: parts of the body that the lymphocyte is inwhen it is not in the HEV or the lymph node tissue

I Enter HEV transits to HEV LumenI Death transits to Stop

HEV

Lumen

RollingLymph

Node

Circulation

BloodDeath

Creatio

n

Enter HEV

Exit HEV

Dis

asso

ciat

e Cap

ture

Migrate

Dra

in

Stop

Start

Lecture 8



State diagram model for a lymphocyteHEV Lumen: lymphocyte when it is flowing freely in the lumenof a HEV

I Exit HEV transits to Blood circulationI Capture transits to Rolling

HEV

Lumen

RollingLymph

Node

Circulation

BloodDeath

Creatio

n

Enter HEV

Exit HEV

Dis

asso

ciat

e Cap

ture

Migrate

Dra

in

Stop

Start

Lecture 8



State diagram model for a lymphocyteRolling: This state represents the lymphocyte when it is rollingon the interior surface of an HEV

I Disassociate transits to HEV LumenI Migrate transits to Lymph Node

HEV

Lumen

RollingLymph

Node

Circulation

BloodDeath

Creatio

n

Enter HEV

Exit HEV

Dis

asso

ciat

e Cap

ture

Migrate

Dra

in

Stop

Start

Lecture 8



State diagram model for a lymphocyteLymph Node: This state describes the lymphocyte when it ispresent in functional tissue of a lymph node

I Drain transits to Blood circulation

HEV

Lumen

RollingLymph

Node

Circulation

BloodDeath

Creatio

n

Enter HEV

Exit HEV

Dis

asso

ciat

e Cap

ture

Migrate

Dra

in

Stop

Start

Lecture 8



State diagram model for a lymphocyte

Stop: lymphocyte ‘dies’

HEV

Lumen

RollingLymph

Node

Circulation

BloodDeath

Creatio

n

Enter HEV

Exit HEV

Dis

asso

ciat

e Cap

ture

Migrate

Dra

inStop

Start

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Main simplificationI Reduced the multi-stage rolling and adhesion cascade

down to two main steps1. Capture of lymphocytes on to the endothelial wall2. Migration after receiving the chemokine signal

I Other stages assumed to be either deterministic, or havesuch small probabilities of failing that they are insignificant

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Simulations

I Developed two simulations of the domain modelI Migration-abstract

I No explicit co-ordinate system, only the four body locationsI Each of these four state spaces can contain a number of

lymphocyte agentsI Migration-space

I 3-dimensional HEV tube made up of endothelial cells,I Supports visualisation of the HEV and of the lymphocytes

migrationI Simulation is “closer to the biology”(?)

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Initial Simulation

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Results from Simulation

(a) Not taking into account a greaterflow of lymphocytes through the HEVwhen the volume of the HEV ex-pands

(b) Increasing the number of lym-phocytes entering the HEV propor-tional to its volume: the number oflymphocytes in the lymph node in-creases.

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—————————————————————–

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Immuno-Engineeringimmuno-engineering:the abstraction of immuno-ecological and immuno-informaticsprinciples, and their adaptation and application to engineeredartefacts (comprising hardware and software), so as to provide theseartefacts with properties analogous to those provided to organisms bytheir natural immune systems.

TheoreticalImmunology Maths

Immuno!Engineering

ExperimentalImmunology

ComputationalModelling

Engineering

Figure: Immuno-engineering

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I Can all this feedback into Engineering?

I Development of tools and methodologies for modellingcomplex systems (including the immune system)

I Via “immuno-engineering” we can soundly abstract andapply immune-inspired ideas

I Later in the lecture series we will look two examples ofmodelling feeding directly the development of algorithms (Tcell signalling and chemical identification, then Granulomaformation and robotics)

I Can all this feedback into Immunology?

I Models allow for in-silco experiments and drive in-vitroexperiments

I Develop a deeper understanding of the system under study

Collaborative Bio-Inspired Algorithms Lecture 8: Immune System … · 2011-01-16 · Collaborative...

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