Presentation made during the EISBM workshop, 13-15 June 2012 by Eric Boix (The Cosmo Company).

1. Supported by
Prominent international speakers from
h"p://workshop.eisbm.eu1

2. Understanding and predicting
biological complex system
Eric Boix
© The CoSMo Company 1

3. Modeling & Simulation
• An in-silico model is a mathematical or computational
representation of a real system.
• A simulation is a virtual experiment conducted on the model.
• The CoSMo Company develops and distributes the next
generation software solution dedicated to the modeling and
simulation of complex systems.
• The models developed are specific to the real systems at stake
and allow to run virtual experiments to facilitate and accelerate
the innovation cycle, the development of new products and the
implementation of new strategies.
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4. The CoSMo solution:
multiscale modeling and simulation
The CoSMo solution features:
• A specific language for modeling
complex systems
• Heterogeneous model coupling
and description of interactions
between various levels
(molecules, cells, tissues, organs,
organisms) across different time
scales
• Flexibility of the model
architecture allows new
knowledge integration with a
rapid turn around
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5. Key field of applications
Dedicated modeling platform
Urban Planning Model pilot and industrialisation - Services
Key partners:
Dedicated modeling platform in systems biology
Biology Co-development of models
Pharma Key partners:
Large Pharmaceuticals
companies in drug
discovery and Vaccin
Smart grids, Energy supply
Field of Industrial complex systems
Research Finance
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6. © The CoSMo Company 5

7. Complexity definition
A scientific theory which asserts that some
systems display behavioral phenomena that
are completely inexplicable by any
conventional analysis of the systems’
constituent parts. These phenomena,
commonly referred to as emergent behavior,
seem to occur in many complex systems
involving living organisms, such as cities or
the human brain.
John L. Casti, Encyclopedia Britannica
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8. Complex systems
Encountered definitions : a complex system is
a system composed of interacting entities
applying rules and whose evolution …
displays emerging properties
cannot be predicted (without simulation)
is very sensitive to initial conditions
is robust to many small perturbations
…
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9. © The CoSMo Company 8

10. Biological question
Can we explain the flowering morphogenesis out of
the known involved genes ?
What are the gene regulated mechanisms driving
the differentiation of the carpel, stamen, petal and
sepal organs ?
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11. Modeling question
What are the dynamics of the Genetic
Regulatory Network (GRN) ?
Model building :
Select relevant genes
Construct the topology of the GRN
network and the relative strengths of
interactions among these genes
(publications)
Express dynamics constraints:
expression patterns of differentiated
tissues
Work by Mendoza-Alvarez 1998
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12. Modeling mapping
Select a mathematical formalism capturing all biological
knowledge and enabling the expression of dynamics
Work by Mendoza et al.
xi = { 0, 1 } (boolean network)
Find a possible dynamic requires numerical simulation
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13. CSMML : the modeling language
Basic building blocks
Entity defined by :
A state : set of attributes
characterizing the entity
A set of rules : methods
changing the state when
provided with the entity
neighborhood
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14. CSMML : the modeling language
Choosing a state
Biologist description of genes:
 “expressed”
 “mildly expressed”
 “not expressed”
• Gene A vs. gene B expressions
Question : gene entity state ?
Modeling answer :
2 states genes, 3 states genes
…
Modeling tool consequence :
quick and agile modeling cycle is a must
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15. CSMML : the modeling language
Interacting entities
Need to mediate interactions (notion of neighborhood)
Neighbour 1
• Define a graph where
- Vertices represent entities
- Arcs and Edges represent interactions
• ArcEntity, EdgeEntity are first class entities :
interactions may attributes and rules ENTITY
Neighbour 4
• Network = Entities + Graph
Neighbour 2
Examples
• Gene interactome Neighbour 3
• Proteome
• Metabolome
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16. CSMML : the modeling language
Interacting entities
• Act activates gene R
• Inh inhibits gene R
• Act and Inh are both active: what is the status of R ?
• A possible modeling solution: weighted arcs
• Interpreted data decides of relative weights
Modeling language : Arcs/Edges can be
decorated with any required attributes
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17. CSMML : the modeling language
The making of a model 1/3
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18. CSMML : the modeling language
The making of a model 2/3
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19. CSMML : the modeling language
The making of a model 3/3
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20. CSMML : the modeling language
Under the hood of a model
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21. CSMML : the modeling language
Dynamics and ordering
Modeling dynamics : rules and schedulers
Temporality defined by schedulers
• Sequential orders
 Rule1, Rule2, Rule3, Rule4
• Parallel orders
 Rule1 || Rule2
• Mixed sequential, parallel orders
 Rule1, (Rule2 || Rule3), Rule4
Example: mixed gene activation in flower
gene regulatory network
• (LFY || AG), LUG, (AP || UFO)… Flower regulatory network
Mendoza et al, 1998
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22. Studying dynamics
Configuration
• Consider order on genes
• Vector of states xi
Trajectory
• Pick up “some” configuration
• Iterate : apply the rules
• Until reaching attractor
Attractors
• Fix point (static equilibrium)
• Limiting cycle (oscillation)
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23. Simple trajectories demo
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24. Studying dynamics
Configuration space and basins of attraction
Structure of dynamic
space
Basins of Attractors
attraction
0x0xxxxx00xx
Fixed point attractor
Limit cycle attractor
Basin of attraction
Trajectory
0x0xxxxx010x SEPALS
0x0xxxxx0111
000100000000
0x1xxxxxx0xx
0x1xxxxxx10x
0x1xxxxxx111
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25. CoSMo platform
Protocols : sets of related simulations (with a objective)
Protocol usages :
study the structure of dynamic space
• Search the attractors
• Compute associated basins of attraction size
Model parameter sweep
Sensitivity analysis, structural/dynamical robustness
Model reconstruction
…
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26. Studying dynamics Basins of Attractors
attraction
0x0xxxxx00xx
0x0xxxxx010x SEPALS
Simulation protocol result : 0x0xxxxx0111
000100000000
0x1xxxxxx0xx
• If you take THIS scheduler 0x1xxxxxx10x
(EMF1 || TFL1), (LFI || API || CAL), 0x1xxxxxx111
(LUG || UFO || BFU), (AG || AP3 || PI), PETALS
0x0xxxxx0110
SUP 000100010110
0x0xxxxxx110
• Only attractors : six fix points 0x0xxxxx10xx CARPELS
0x0xxxxx110x 000000001000
0x0xxxxx1111
STAMENS
Answer to the biological question : 0x0xxxxx1110 000000011110
proposed GRN can explain flower
1xxxxxxxx0xx NOT OBSERVED
morphogenesis
1xxxxxxxx10x 110000000000
(when not : back to modeling cycle) 1xxxxxxxx111
NOT OBSERVED
0x0xxxxx1110 110000010110
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27. © The CoSMo Company 26

28. Biological question
What are the mechanisms explaining carpel
invagination (plant), blastula gastrulation (animals) ?
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29. Integrative model with geometry
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30. CSMML : the modeling language
Grouping things
Modeling : Compound Entities
Compound entities CELL
• Contain sub-entities
• Graph on sub-entities GEOMETRY GRAPH of GENES
• Scheduler on sub-entities
- Cross-scale synchronization
• Also an entity
- Set of states, rules.
Example: cell (proposition)
• Components:
- Gene regulatory network
- Scheduler on the network
• Attribute:
- Geometry
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31. CSMML : the modeling language
Compounding induces hierarchies
Mendoza Morphogenesis
1 level 2 levels
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32. Demos
1. Active flows (edges)
2. Fully integrated model
3. Ascidians (on going)
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33. Multi-scale model
Difference between :
• Intra-nuclear : Tbet / Gata3
• Cell-cell : IL4<->IL4R
Modeling beyond simple delay :
ambient diffusion
Diffusion
space
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34. Probes : observing the system
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35. Intestinal cancer integrative model
Gene expression
Intestinal
Microbiota
Mechanical
adhesion
Geometry
GRN
• Cell growth
• Migration
• Division
• Apoptosis
Cell Signaling
Cell Cycle
Model: van Leeuwen, Byrne, Jensen, 2009, University of Notthingham UK
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36. © The CoSMo Company 35

37. “ Biological” question
Epidemiology : how does host treatment, host
susceptibility and host exposure impact on the spreading of
a disease?
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38. Networks within networks
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39. Dynamical networks (structures)
Platform : model rules
• dynamic entities
• dynamic networks
• dynamic scheduler
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40. Demo
Epidemiology (two views)
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41. Epidemiology stress testtest
Epidemiology stress
• City: random graph, average degree of 10
• Computational time: generation and simulation (100 steps)
• City graph: fully connected graph
• Dynamic case: at each iteration city graphs are regenerated
1 city 10 cities 50 cities
N=1000 E=10000 static 8.45’’ 86’ 425s’’
N=1000 E=10000 dynamic 11’ 108’ 538’
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42. CoSMo relevance
Complex system phenomena with
• Explicit networks: structure
• Multi-scale: hierarchies
• Geometry based symmetry breaking
• Many dynamical feedback mechanisms
• Multiple time scale
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43. The CoSMo solution: multiscale M&S
CoSMo delivers a comprehensive
simulation platform to master and
predict biological systems
The CoSMo solution allows
heterogeneous model coupling and
description of interactions between
various levels (molecules, cells,
tissues, organs, organisms) in a
changing environment across
different time scales
CoSMo has developed a specific
language for modeling complex
systems: csmML
© The CoSMo Company

44. A 3-step methodology
Feasibility Model In silico
study building simulation
Needs analysis and Looking backward Looking forward:
assessment of to describe the
existing data, system and its
models and behaviour What if… ?
knowledge
Close collaboration between modelers and biologists
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45. Complex systems model
[1] Entity: heterogeneous building blocks
[2] Graphs: representation of “neighbors”
[3] Scheduler: dynamics sequence/parallel trees
[4] Compound: nodes of descriptive hierarchy
Complex Systems Model = [1 + 2 + 3 + 4]
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46. CoSMo relevance
Complex system phenomena with
• Explicit networks: structure
• Multi-scale: hierarchies
• Geometry based symmetry breaking
• Many dynamical feedback mechanisms
• Multiple time scale
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47. Contact us :
Eric Boix, CSO
eric.boix@cosmo-platform.org
Thierry de Lumley, Development Director - Biology
tdelumley@thecosmocompany.com
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