Engineering self modelling systems application to biology
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Engineering Self-Modelling Systems: Application to Biology. Carole Bernon , Davy Capera*, Jean-Pierre Mano SMAC Team ( C ooperative M ulti- A gent S ystems) I nstitut de R echerche en I nformatique de T oulouse *UPEtec www.irit.fr/SMAC - www.upetec.fr. Outline.

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Engineering self modelling systems application to biology

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

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

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

Towards Self-building Systems

  • Complexity  “autonomic computing” [IBM03]

  • Alleviate the designer’s task

    • Initial expertise

    • Some minimal feedback from time to time

  • 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

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

Four-layer Model

Data

User

User

User

User

Reorganisation

Tuning

Evolution

Nominal

Cooperative

Agent

Environment

Access & Modify

Environment coupling

Trigger


Outline1

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

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

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

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

Nominal

Nominal

Nominal

Nominal

Nominal

Nominal

Nominal

Cooperative

Cooperative

Cooperative

Cooperative

Cooperative

Nominal

Nominal

R

R

R

R

R

T

T

T

T

T

E

E

E

E

E

Nominal

Modelling Approach

Simulated results

Experimental data

Feedback

Model


Outline2

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

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

Functional Agents

  • Elements

    • Represent common attributes for each element within the cell

    • Quantity associated

  • Reactions

    • Genes

      • Confirm data about transcripts

    • 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

Example

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

Element

Synthesis reaction

Regulation

Consumption

Production

Catalysis reaction


Viewer agents

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

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

Incompetence

 or  (quantity value)

Incompetence

quantity value

Tuning Behaviour of Agents

Conflict

quantity error detected

Conflict

message to element

Incompetence

change quantity

Viewer

Incompetence

quantity value < 0

Incompetence

speed value

Incompetence

Tune stoichiometry

or speed

Unproductiveness

current context unknown

Unproductiveness

create new context


Reorganisation behaviour of agents

Reorganisation Behaviour of Agents

Viewer

Incompetence

change partner

Incompetence

tuning failure

Uselessness

no partner

Uselessness

search for partner

Incompetence

tuning failure

Incompetence

change/find new regulators

Partial uselessness

search for partner

Partial uselessness

Not enough partners


Example glycolysis

Example: Glycolysis


Preliminary results

Preliminary Results

  • Nominal functioning only

  • Adaptive behaviour

  • Memory of previous states


Outline3

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

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 biology1

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

References

  • References related to SMAC team

    • [Besse 05] C. Besse, Recherche de conformation de molécules et apprentissage du potentiel de Lennard-Jones par systèmes multi-agents adaptatifs, Research Master IARCL Report, Université Paul Sabatier, June 2005.

    • [Camps 97] V. Camps, M.P. Gleizes, S. Trouilhet, Properties Analysis of a Learning Algorithm for Adaptive Systems, First International Conference on Computing Anticipatory Systems, Liège, Belgium, August 1997.

    • [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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    • [Georgé 04] J.P. Georgé, Résolution de problèmes par émergence, Etude d'un Environnement de Programmation Emergente, PhD thesis, Université Paul Sabatier, IRIT, Toulouse III, 6 July 2004.

    • [Mano 06] J.P. Mano, Etude de l’émergence fonctionnelle au sein d’un réseau de neuro-agents coopératifs, PhD thesis, Université Paul Sabatier, IRIT, Toulouse III, 30 May 2006.


References 2

References (2)

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    • [Picard 04] G. Picard, Cooperative Agent Model Instantiation to Collective Robotics, In: 5th International Workshop on Engineering Societies in the Agents World (ESAW 2004), Toulouse, France, M.P. Gleizes, A. Omicini, F. Zambonelli (Eds), Springer Verlag, LNCS 3451, 209-221.

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    • [Topin 99] X. Topin, V. Fourcassie, M.P. Gleizes, G. Theraulaz, C. Régis, P. Glize, Theories and Experiments on Emergent Behaviour: From Natural to Artificial Systems and Back, In: European Conference on Cognitive Science, Siena, 1999.

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References 3

References (3)

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    • [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 ([email protected]'05), 91-106, 2005.

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    • [Conceicao 08] D. Conceição, M. Gatti, C. de Lucena, An Agent-based Framework for Stem Cell Behavior Modeling and Simulation, Research report 17/08, Department of Computer Sciences, Pontificia Universidade Catolico do Rio de Janeiro, April 2008.

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References 4

References (4)

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References 5

References (5)

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