Biological modelling and validation with FLAME

Mike HolcombeUniversity of Sheffield

How we are making new biological discoveries using systems biology

• Through intensive collaborations with experimental biologists

• Emphasis on very detailed and ‘correct’ agent definitions – the biologists may need to do new experiments

• Taking account of geometry and location and physical forces – this is vital

• Recognising the diversity of natural systems – not all cells are the same

• Validating model predictions through new experiments

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A complex system

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Pharoah’s ants• We are studying trail formation in Pharoah’s ants

(M.pharaonis).• Observations have identified “trail-laying behaviours” This

is used to indicate to others where sources of food is.• The seven trail pheromones in Pharaoh’s ants are

synthesised by the Dufour’s gland/poison apparatus.• The volatile component is very short-lived but the other

components are very persistent• A model based on rules derived from extensive

observation in the lab was built.

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Trail geometry

Branching networks of pheromone trails

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New discovery -Trail Geometry

• The bifurcation angle is very regular - about 60°. This tells the ants which way to go

• The pheromone has no directional information so how does it work?

• A simple rule such as: If fed then: take the easy route; if there are 2 easy routes turn round

will get them home.

Jackson et al Nature, vol. 432: 2004. Robinson et al Nature Vol 438, 2005Jackson et al ANIMAL BEHAVIOUR 71: 2006

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Ants foraging randomly and with long lived pheromone trail (Bicak)

Simulation of a Pharoah’s Ant colony using a supercomputer

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A simple chemical reaction

• A simple reaction: Two chemicals -A (blue) plus B (yellow) combine to make C (green)

Pogson

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A more complex molecular example:part of the human immune system

• Innate immune system - deals with basic infections and inflamation

• Adaptive immune system - learns from exposure to diseases - bacteria, virus, etc.– Basis of vaccination

• Very complex systems - still not fully understood

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Model basics• Each NF-B, IB and IB-kinase

(IKK) molecule is an individual

agent,

• As are the importing and exporting

nuclear receptors and the interleukin-

1 (IL-1) toll like receptors.

• The agents are all contained within a

spherical cell consisting of a

cytoplasm and concentric nucleus

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New discovery

• There had been some evidence that the ratio of IBα to NFB was 3 times what was ‘needed’

• Where was all this excess IBα?• The model predicted that if it was

sequestered with the actin filaments this would explain where it was

• We can track every molecule at all times and thus model the full pathway in detail

• Recent experiments have produced very significant data that confirms this

Pogson et al PLOSOne 3(6): (2008)

Immunoprecipitation of IB and secondary actin immunoprecipitation

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Epithelial tissue - skin and urotheliome

Mac Neil

16Mac Neil

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Different types of cells

• Stem cells• Transit amplifying cells• Differentiating cells• Fibroblasts• Keratinocytes• Corneocytes

Basic cell cycle

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T. Sun, P. McMinn, J. Southgate, DWalker

Wound healing

Why do some wounds heal and others

do not?

Each cell is an individual and yet some

will start to divide and close up the

wound.

What is organising this?

How can we find out what goes wrong

when it doesn’t work?

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Skin healing – stem cells are blue - 3d model

McMinn et al Sun et al

Fibroblasts and keratinocytes self-organising

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Functions of TGF-β1 During Epidermal Wound Healing

Sun et al

Healed virtual epidermis - the stratified cells with relatively high expression level of TGF-β1 were labelled with yellow(A), In the integrated model different colours were used to represent keratinocyte stem cells (blue), TA cells (light green), committed cells (dark green), corneocytes (brown), provisional matrix (dark red), secondary matrix (Green), BM tile agent (light purple). Some of the cells with relative low expression level TGF-β1 were also illustrated

In virtuo investigation of the influence of TGF-β1 on epidermal wound healing at subcellular level. The virtual wound with normal proliferation and migration rates were simulated for (A) 0, (B) 200, (C) 400 and (D) 800 iterations. The cells with high TGF-β1 expression levels

were labelled with yellow.

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So what is FLAME?

• It is based on representing each agent as a general computational model - the X-machine (Eilenberg 1974)

• The agents communicate using messages and message boards

• Agents are specified using XMML• Filters and message board libraries ensure

concurrent efficiency• Complete models are automatically generated in

C from the specifications

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Hierarchical modelling

FLAME framework

Molecular agent-basedmodel

Cellular agent-based model

callreturn

Internalsolver

External solver

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• COPASI (COmplex PAthway Simulator) can be called as a function within the agents

n.xmlstate, positional informationetc . Input to external solvers .Input to post -processing .

messages

functions- mathematical- logical

internal functions orexternal e .g. COPASI

0.xml

initial conditions

FLAME framework

Salem Adra

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FLAME Block Diagram

X parser files

Xparser.exe

Model.xml

Functions.c

1-NXml files

Main.exe

0.xml

make

Libmboard

Your files

Xparser files

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Output analysis• FLAME produces a vast amount of data• We will need to use data mining and

information extraction technology to fully exploit this

• DAIKON – Dynamic invariant detector• http://pag.csail.mit.edu/daikon/• This uses machine learning techniques to

identify properties that hold in thousands of simulation runs.

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• keratinocyte0:::OBJECT • z == motility • x <= 500.0 • x >= 0.0 • y != 0 • y <= 467.957706 • y >= 27.36479 • z == 0.0 • force_x <= 0.288605 • force_x >= -0.30796 • force_y <= 0.312008 • force_y >= -0.311693 • force_z != 0 force_z <= 4.9E-

324 force_z >= -0.399635 num_xy_bonds <= 10 num_xy_bonds >= 0

• num_z_bonds <= 8 • num_z_bonds >= 0 • num_stem_bonds <= 10 • num_stem_bonds >= 0

•cycle != 0 cycle <= 120 cycle >= 1 •diff_noise_factor != 0 •distance_travelled <= 840.52939 •distance_travelled >= 0.0 •x >= z •x >= force_x •x != force_y• x > force_z x != diff_noise_factor •x >= motility •x != dir •x != distance_travelled •(distance_travelled == 0) ==> (force_x == 0)•(num_xy_bonds == 0) ==> (num_z_bonds == 0) •(num_xy_bonds == 0) ==> (num_stem_bonds == 0) •num_xy_bonds >= num_stem_bonds

N. Walkinshaw, P. McMinn

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Conclusions• Agent–based modelling provides a different insight into

many types of complex systems• It can help uncover what may be going on ‘internally’• It is complementary to traditional modelling approaches• The structured way these models are built aids

understanding• Models can easily be extended by combining several

agent-based models together and by introducing new types of agents

• FLAME - Flexible Large-scale Agent-based Modelling Environment

• http://www.flame.ac.uk

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Acknowledgements• Rod Smallwood• Sheila Mac Neil• Salem Adri• Des Ryan• Francis Ratnieks• Eva Qwarnstrom• Dawn Walker• Simon Coakley• Duncan Jackson• Elva Robinson• Mark Pogson• Mariam Kiran• Rob Poole• Jeff Green• Petros Kefalas• Mesude Bicak• Mark Birkett

• Phil McMinn• Susheel Varma• Sun Tao• Chris Thompson,• John Karn,• Stephen Wood (IWP)• Neil Walkinshaw• Phil McMinn• Jenny Southgate (York)• Chris Greenough (RAL)• David Worth (RAL)• Shawn Chin (RAL)• Hubert Dravid• Michael Neugart• Silvano Cincotti • Afsaneh Maleki-Dizaj• And many more

IBM