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Multi-Agents Systems and environment modelling…

Multi-Agents Systems and environment modelling…. NetBioDyn, an easy to use multi-agents engine for ecosystems simulation. Vincent Rodin vincent.rodin@univ-brest.fr. Road map. Multi-Agents Systems (MAS) From Biological environment simulation Towards Ecosystems simulation

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Multi-Agents Systems and environment modelling…

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  1. Multi-Agents Systems and environment modelling… NetBioDyn, an easy to use multi-agents engine for ecosystems simulation Vincent Rodin vincent.rodin@univ-brest.fr

  2. Road map • Multi-Agents Systems (MAS) • From Biological environment simulation • Towards Ecosystems simulation • NetBioDyn software • Conclusions and futur works

  3. Multi-Agents systems properties Agent : perception-decision-action Multi-agents System : • auto-organisation • emergence • robustness • adaptability Models’ autonomy

  4. Road map • Multi-Agents Systems (MAS) • From Biological environment simulation • Towards Ecosystems simulation • NetBioDyn software • Conclusions and futur works

  5. Multi-Agent Systems and Biological modelling & simulation From cell-agent To systemic approach Interaction-agent Interface-Agent Reaction-agent Cell-agent

  6. Basic behaviours • Mitosis • Activation • Internalisation Behaviours • Expression of receptor • Apoptose Cell-agent model Model of located agents with complex behaviors

  7. Pa Pa Pa Pa Flux Xa Xa Xa Xa Xa Xa Ia Ia Ia Ia VIIa VIIa VIIa VIIa VIIa Ia Xa Xa Xa VIIIa VIIIa VIIIa VIIIa VIIIa VIIIa Ia Ia IIa IIa IIa IIa Va Va Va Va IXa IXa IXa F.W F.W F.W F.W Endothelial cell Fibroblasts + Tissue Factors Cell-agent model: An exemple of application Simulation of physiologic coagulation: Cell Fibroblasts cells, endothelial cells, platelet Procoagulant factors TF, I, II, V, VII, VIII, IX, X, XI, … Inhibitor factors TFPI, AT3, 2M, PC, PS, PZ, … Coagulation Cascade  Blood clot

  8. Cell-agent model: An exemple of application Simulation of physiologic coagulation:  blood clot…

  9. Cell-agent model: An exemple of application Elements of validation of the coagulation multiagents model : • Comparison with Biological experiment Curve of thrombin Generation [Hemker, 1995] • Coherence with respect to pathologies

  10. Thrombin generation Thrombin generation Normal coagulation Normal coagulation Hemophilia B + NovoSeven Hemophilia A Hemophilia B Hemophilia B Time Time in seconds in seconds Cell-agent model: An exemple of application Simulation of physiologic coagulation: Healthy patient, hemophiliac, hemophiliac with treatment

  11. Multi-Agent Systems and Biological modelling & simulation From cell-agent To systemic approach Interaction-agent Interface-Agent Reaction-agent Cell-agent located agent

  12. 1: reading of the concentrations in reactants 2:calculation the reaction speed and then the quantity of reactant to be reacted perception decision action 3: consequently, modification of the concentrations in reactants and products Reaction-agent model • « microscopic » level : • « macroscopic »level : agent = cell/molecule agent = reaction reaction

  13. Reaction-agent model Spatial indiscernibility … r1 r2 Non located agents rm C1, C2, …, Cn Chemical reactor • Asynchronous phenomena and chaotic order • No Ordinary Differential Equation

  14. Thrombine generation Thrombine generation Normal coagulation Normal coagulation Hemophilia B + NovoSeven Hemophilia A Hemophilia B Hemophilia B Time in Time in ½ seconds ½ seconds Reaction-agent model: An exemple of application VIIIa + IXa -> VIIIa-IXa VIIIa-IXa + X -> VIIIa-IXa + Xa Va-Xa + II -> Va-Xa + IIa AT3 + (IXa, Xa, XIa, IIa) -> 0 alpha2M + IIa -> 0 I + IIa -> Ia + IIa TM (endo cell) + IIa -> IIi AT3(endo cell) + (IXa, Xa, XIa, IIa) -> 0 ProC + IIi -> PCa + IIi PCa + Va -> 0 PCa + Va-Xa -> Xa PCa + PS -> PCa-S PCa + PCI -> 0 PCaS + Va -> 0 PCaS + V -> PCa-S-V PCa-S-V + VIIIa-IXa -> IXa PCa-S-V + VIIIa -> 0 Fibroblaste + VIIa -> VII-TF VII + VIIa -> VIIa + VIIa VII + VII-TF -> VIIa + VII-TF IX + VII-TF -> IXa + VII-TF X + VII-TF -> Xa + VII-TF TFPI + Xa -> TFPI-Xa TFPI-Xa + VII-TF -> 0 VII + IXa -> VIIa + IXa IXa + X -> IXa + Xa II + Xa -> IIa + Xa XIa + IX -> XIa + IXa XIa + XI -> XIa + XIa IIa + XIa -> IIa + XIa IIa + VIII -> IIa + VIIIa IIa + V -> IIa + Va Xa + V -> Xa + Va Xa + VIII -> Xa + VIIIa Xa + Va -> Va-Xa Simulation of physiologic coagulation: Healthy patient, hemophiliac, hemophiliac with treatment 42 reactions

  15. Multi-Agent Systems and Biological modelling & simulation From cell-agent To systemic approach Interaction-agent Interface-Agent Reaction-agent non located agent Cell-agent located agent

  16. Interface-agent • interaction between the mediums Interface-agent model An « agent » point of view: • Asynchronous phenomena and chaotic order • No Partial Differential Equation

  17. Interface-agent model: An exemple of application Coagulation : HOLE Flux 3D Vessel Chimical Reactors : 42 reactions (coagulation)

  18. Multi-Agent Systems and Biological modelling & simulation From cell-agent To systemic approach Interaction-agent Interface-agent transport agent Reaction-agent non located agent Cell-agent located agent

  19. Interaction between models of different natures Multi-modeling Simulation Systemic paradigm Data Matter exchange between organisations Generic model of interaction-agent Main principle : Models’ autonomy

  20. Phenomenon (organisation) Simulator Activity * Interface agent Reaction agent Organ agent Cell agent * * Interaction agent * Component Generic model of interaction-agent

  21. Generic model of interaction-agent : An exemple of application Keratinocyte Macrophage Mast cell Capillary vessel

  22. Multi-Agent Systems and Biological modelling & simulation From cell-agent To systemic approach Interaction-agent multimodeling Interface-agent transport agent Reaction-agent non located agent Cell-agent located agent

  23. Analyse parameters influence Test and validate hypotheses Interests in the field of biology • To help the reflection Understand complex phenomena • To prepare experiments MA S • To accelerate the drug discovery Compute Sciences Biology

  24. Road map • Multi-Agents Systems (MAS) • From Biological environment simulation • Towards Ecosystems simulation • NetBioDyn software • Conclusions and futur works

  25. Towards Ecosystems simulation Cells  Entities (insects, etc.) & Low level interaction Reaction  High level interaction between entities Interface/Interaction  Physical environment

  26. Road map • Multi-Agents Systems (MAS) • From Biological environment simulation • Towards Ecosystems simulation • NetBioDyn software • Conclusions and futur works

  27. NetBioDyn software : an easy to use multi-agents engine Goal: Rapid prototyping of biological & ecosystem simulations http://virtulab.univ-brest.fr/netbiodyn.html http://virtulab.univ-brest.fr/netbiodyn3D.html

  28. NetBioDyn software : an easy to use multi-agents engine • Key concepts: • Environment: a grid • Entities: colored squares • Interaction with environment : simple rules • Interaction between entities : simple rules • Easy to use… No programming skill requires !!!!

  29. NetBioDyn software : How to model…? 1st : take a grid 2nd : decide which entities to use ….. 3rd : define rules (with a probility of activation) to give entities movements and interactions + Ø + Ø  P=1 (go to the right) P=0.5 (contamination) + +  …..

  30. NetBioDyn software : How to model…? 4th : place entities and : run + + (go to the right)  (contamination) + +  …..

  31. NetBioDyn software : How to model…? 4th : place entities and : run + + (go to the right)  (contamination) + +  …..

  32. NetBioDyn : 1st example, randomwalk Entities: or Rd_Walk  P=1 or

  33. NetBioDyn : 1st example, randomwalk Entities: or Rd_Walk  P=1 or

  34. NetBioDyn : 2nd example, sandglass Entities: grain and border Go_Down  P=1 Go_LeftRight or  P=0.5 Go_Border or  P=0.75

  35. NetBioDyn : 3rd example, prey-predator Entities: prey and predator Prey ½ life time : 2000 cycles + Rd_Walk Predator ½ life time : 200 cycles + Rd_Walk

  36. NetBioDyn : 3rd example, prey-predator Entities: prey and predator or Prey_Birth  P=0.01 or

  37. NetBioDyn : 3rd example, prey-predator Entities: prey and predator Predator_Prey  P=0.6    

  38. NetBioDyn : 4th example, fish farm Entities: fish , shrimp , algae , fisherman , border

  39. NetBioDyn : 4th example, fish farm Entities: fish , shrimp , algae , fisherman , border

  40. Road map • Multi-Agents Systems (MAS) • From Biological environment simulation • Towards Ecosystems simulation • NetBioDyn software • Conclusions and futur works

  41. NetBioDyn : Conclusion • Advantage:  very simple (entities, rules) •  no programming ! • Drawback:  very simple (entities, rules) •  no entity’s state ! Example: + Ø  Ø + (movement) …………… Example: + *  + * (new entity’s state) ……………  new entity !

  42. NetBioDyn : futur works • Self-ajusting parameters… •  a great challenge ! • Entity’s state (simply an integer)… •  Building a model would be simpler by • minimizing the nb of « different » entities

  43. Road map • Multi-Agents Systems (MAS) • From Biological environment simulation • Towards Ecosystems simulation • NetBioDyn software • Conclusions and futur works

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