Understanding and predicting biological complex system.

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

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Understanding and predicting biological complex system.

  1. 1. Supported byProminent international speakers from h"p://workshop.eisbm.eu1
  2. 2. Understanding and predicting biological complex system Eric Boix© The CoSMo Company 1
  3. 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.© The CoSMo Company
  4. 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© The CoSMo Company 3
  5. 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© The CoSMo Company
  6. 6. © The CoSMo Company 5
  7. 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© The CoSMo Company 6
  8. 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 …© The CoSMo Company 7
  9. 9. © The CoSMo Company 8
  10. 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 ?© The CoSMo Company 9
  11. 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© The CoSMo Company 10
  12. 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© The CoSMo Company 11
  13. 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© The CoSMo Company 12
  14. 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© The CoSMo Company 13
  15. 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© The CoSMo Company 14
  16. 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© The CoSMo Company 15
  17. 17. CSMML : the modeling language The making of a model 1/3© The CoSMo Company 16
  18. 18. CSMML : the modeling language The making of a model 2/3© The CoSMo Company 17
  19. 19. CSMML : the modeling language The making of a model 3/3© The CoSMo Company 18
  20. 20. CSMML : the modeling language Under the hood of a model© The CoSMo Company 19
  21. 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© The CoSMo Company 20
  22. 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)© The CoSMo Company 21
  23. 23. Simple trajectories demo© The CoSMo Company 22
  24. 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© The CoSMo Company 23
  25. 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 …© The CoSMo Company 24
  26. 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© The CoSMo Company 25
  27. 27. © The CoSMo Company 26
  28. 28. Biological question What are the mechanisms explaining carpel invagination (plant), blastula gastrulation (animals) ?© The CoSMo Company 27
  29. 29. Integrative model with geometry© The CoSMo Company 28
  30. 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© The CoSMo Company 29
  31. 31. CSMML : the modeling language Compounding induces hierarchies Mendoza Morphogenesis 1 level 2 levels© The CoSMo Company 30
  32. 32. Demos 1. Active flows (edges) 2. Fully integrated model 3. Ascidians (on going)© The CoSMo Company 31
  33. 33. Multi-scale model Difference between : • Intra-nuclear : Tbet / Gata3 • Cell-cell : IL4<->IL4R Modeling beyond simple delay : ambient diffusion Diffusion space© The CoSMo Company 32
  34. 34. Probes : observing the system© The CoSMo Company 33
  35. 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© The CoSMo Company 34
  36. 36. © The CoSMo Company 35
  37. 37. “  Biological”  question Epidemiology : how does host treatment, host susceptibility and host exposure impact on the spreading of a disease?© The CoSMo Company 36
  38. 38. Networks within networks© The CoSMo Company 37
  39. 39. Dynamical networks (structures) Platform : model rules • dynamic entities • dynamic networks • dynamic scheduler© The CoSMo Company 38
  40. 40. Demo Epidemiology (two views)© The CoSMo Company 39
  41. 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’© The CoSMo Company 40
  42. 42. CoSMo relevance Complex system phenomena with • Explicit networks: structure • Multi-scale: hierarchies • Geometry based symmetry breaking • Many dynamical feedback mechanisms • Multiple time scale© The CoSMo Company 41
  43. 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. 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© The CoSMo Company 43
  45. 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]© The CoSMo Company 44
  46. 46. CoSMo relevance Complex system phenomena with • Explicit networks: structure • Multi-scale: hierarchies • Geometry based symmetry breaking • Many dynamical feedback mechanisms • Multiple time scale© The CoSMo Company 45
  47. 47. Contact us : Eric Boix, CSO eric.boix@cosmo-platform.org Thierry de Lumley, Development Director - Biology tdelumley@thecosmocompany.com© The CoSMo Company 46

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