Engineering Self-Modelling Systems: Application to Biology Carole Bernon, Davy Capera*, Jean-Pierre Mano



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Engineering Self-Modelling Systems: Application to Biology

  • Carole Bernon, Davy Capera*, Jean-Pierre Mano

  • SMAC Team (Cooperative Multi-Agent Systems)

  • Institut de Recherche en Informatique de Toulouse

  • *UPEtec

  • www.irit.fr/SMAC - www.upetec.fr


Outline

  • Making complex systems self-build

    • Self-organisation by cooperation
    • Four-layer model
  • A domain of application: Biology

    • microMega specific case
    • Agents and Biology
  • Model applied to microMega

    • Architecture
      • Agents
      • Behaviours
    • Preliminary results
  • Conclusion



Statement

  • Systems: more and more complex

  • Environments: more and more open and dynamic

  • Biological domain is no exception

    • Huge volumes of data
      • To be gathered, processed, exploited, visualised…
    • Interaction networks
      • Large-scale
      • Interactions are incompletely known
      • Experimental data incomplete and heterogeneous
    • Model integration
      • Building a whole
      • By assembling coupled parts
      • In order to explain a higher level of functioning


Towards Self-building Systems

  • Complexity  “autonomic computing” [IBM03]

  • Alleviate the designer’s task

  • Let the system self-build

  • Autonomous change of the organisation of the system

  • Autonomous change of the behaviour of its components

    • Ability to learn what is unknown (or incompletely known)
    • Ability to interact in a different way
    • Ability to appear/disappear


Self-organisation by Cooperation

  • Adaptive Multi-Agent Systems theory [Camps98, Capera03]

  • Social attitude of agents

    • Perceive: Perceptions are understood without ambiguity
    • Decide: Perceptions enable conclusion(s)
    • Act: Actions are useful for the environment (and itself)
  • A cooperative agent acts to

    • Avoid
    • Prevent
    • Remove
  • situations that it judges as being cooperative failures



Four-layer Model



Outline

  • Making complex systems self-build

    • Self-organisation by cooperation
    • Four-layer model
  • A domain of application: Biology

    • microMega specific case
    • Agents and Biology
  • Model applied to microMega

    • Architecture
      • Agents
      • Behaviours
    • Preliminary results
  • Conclusion



Complexity and Biological Systems

  • Theories are often missing

  • Modelling and simulation (Gepasi [Mendes93], Copasi…)

  • Different approaches

    • Mathematical models
    • Petri nets
    • Cellular automata
    • Neural networks
  • Drawbacks

    • Black boxes
    • Models often static
    • Far from a biological reality


microMega

  • National project

    • LISBP, INSA  biologists
      • « Génie microbiologique » team
      • « Physiologie microbienne des eucaryotes » team
    • LAAS, Disco team  mathematicians
    • LSP, UPS  statisticians
  • Multi-agent modelling of the genetic-metabolic interaction of a yeast (Saccharomyces Cerevisiae)

  • From:

    • Transcriptomic data: genes
    • Macroscopic data: components
  • In order to get free from experimental conditions

  • Feasibility study



Agents and Biology

  • Agent and multi-agent technologies are rising [Lints05, Merelli06, Amigoni07]

  • Bioinformatics [Luck05] or systems biology

    • Protein folding/docking [Armano05, Bortolussi05]
    • Pathways [Khan03, Gonzalez03, Querrec03]
    • Cell simulation [Webb06, Lints05, Boss06, Jonker08]
    • Cell population simulation [Emonet05, Troisi05, D’Inverno05, Guo07]
  • Discover new phenomena?

    • Organisation is often fixed in MAS
    • Laws considered as known
    • Disruptions are not taken into account
      • Some exceptions [Querrec03, Shafaei08]


Modelling Approach



Outline

  • Making complex systems self-build

    • Self-organisation by cooperation
    • Four-layer model
  • A domain of application: Biology

    • microMega specific case
    • Agents and Biology
  • Model applied to microMega

    • Architecture
      • Agents
      • Behaviours
    • Preliminary results
  • Conclusion



Architecture of microMega

  • AMAS simulating chemical reactions

  • Two kinds of cooperative agents

    • Functional agents
      • Physical elements
      • Reactions
      • Interactions
        • Element consumption/production
        • Reactions regulation
    • Viewer agents
      • Interactions with users
      • Data injection
      • Specific constraints


Functional Agents

  • Elements

    • Represent common attributes for each element within the cell
    • Quantity associated
  • Reactions

    • Genes
    • Transporters
      • Move an element quantity from one compartment to another
      • Passive / Active (ATP consumption)
    • Catalysis
      • Transform a metabolite quantity into two
      • Catalysis may be regulated
    • Synthesis
      • Assemble two metabolites
      • Synthesis may be regulated


Example

  • 1 Fructose1,6DP + 2 ADP + 2 NAD+ -> 2 Pyruvates + 2 ATP + 2 NADH,H+



Viewer Agents

  • ElementViewerAgent

    • Gather quantities of a list of element agents
  • ElementSetterAgent

    • Control activity of a list of element agents
    • Database of experimental quantities
  • But also…

    • Evaluate biomass
      • Sum of the quantities of all element agents
    • Identify compartments within the cell
      • If the system is able to reorganise
      • Manage user’s constraints


Nominal Behaviour of Agents

  • Element agents

    • Manage related element quantity depending on feedback from reaction agents
    • Linked to a compartment
  • Reaction agents

    • Consume/product element agents depending on:
      • Stoichiometry
      • Contextual reaction speed (possible regulations)
  • Viewer agents

    • Access data of functional agents
    • Store these data
    • Compute error related to experimental data


Tuning Behaviour of Agents



Reorganisation Behaviour of Agents



Example: Glycolysis



Preliminary Results

  • Nominal functioning only

  • Adaptive behaviour

  • Memory of previous states



Outline

  • Making complex systems self-build

    • Self-organisation by cooperation
    • Four-layer model
  • A domain of application: Biology

    • microMega specific case
    • Agents and Biology
  • Model applied to microMega

    • Architecture
      • Agents
      • Behaviours
    • Preliminary results
  • Conclusion



Conclusion - Prospects

  • Feasibility demonstration

    • Self-building model
    • Self-tuning model
  • Model still incomplete

  • Exhibits adaptation abilities

  • Self-building = key for managing complexity

  • Emergence = key for this self-building

  • Finalise cooperative layers

  • Overcome problems related to noise (forget)

  • Validate models obtained on different experimental data



Engineering Self-Modelling Systems: Application to Biology

  • Thank you for your attention

  • SMAC Team (Cooperative Multi-Agent Systems)

  • Institut de Recherche en Informatique de Toulouse

  • UPEtec

  • www.irit.fr/SMAC - www.upetec.fr



References

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    • [Camps 98] V. Camps, Vers une théorie de l'auto-organisation dans les systèmes multi-agents basée sur la coopération : application à la recherche d'information dans un système d'information répartie, PhD thesis, Université Paul Sabatier N°2890, IRIT, Toulouse, January 1998.
    • [Capera 05] D. Capera, Systèmes multi-agents adaptatifs pour la résolution de problèmes : Application à la conception de mécanismes, PhD thesis, Université Paul Sabatier, IRIT, Toulouse III, 23 June 2005.
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  • References external to SMAC team (1)

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    • [Armano 05] G. Armano, G. Mancosu, A. Orro, E. Vargiu, A Multi-agent System for Protein Secondary Structure Prediction, In: Transactions on Computational Systems Biology III, LNCS 3737, Springer, 14-32, 2005.
    • [Bortolussi 05] L. Bortolussi, A. Dovier, F. Fogolari, Multi-Agent Simulation of Protein Folding, In: First Workshop on Multi-Agent Systems for Medecine, Computational Biology, and Bioinformatics (MAS*BIOMED'05@AAMAS'05), 91-106, 2005.
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References (4)

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References (5)

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