engineering self-modelling systems: application to biology

ESAW08 – Saint-Etienne – September 26, 2008

Engineering Self-Modelling Systems:Engineering Self-Modelling Systems:Application to BiologyApplication 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

Engineering Self-Modelling Systems: Application to Biology 2

OutlineOutline

Making complex systems self-buildSelf-organisation by cooperationFour-layer model

A domain of application: BiologymicroMega specific caseAgents and Biology

Model applied to microMegaArchitecture

o Agentso Behaviours

Preliminary results

Conclusion


StatementStatement

Systems: more and more complexEnvironments: more and more open and dynamicBiological domain is no exception

Huge volumes of datao To be gathered, processed, exploited, visualised…

Interaction networkso Large-scaleo Interactions are incompletely knowno Experimental data incomplete and heterogeneous

Model integrationo Building a whole o By assembling coupled partso In order to explain a higher level of functioning


Towards Self-building SystemsTowards Self-building Systems

Complexity “autonomic computing” [IBM03]Alleviate the designer’s task

Initial expertiseSome minimal feedback from time to time

Let the system self-buildAutonomous change of the organisation of the

systemAutonomous change of the behaviour of its

componentsAbility to learn what is unknown (or incompletely

known)Ability to interact in a different wayAbility to appear/disappear


Self-organisation by CooperationSelf-organisation by Cooperation

Adaptive Multi-Agent Systems theory [Camps98, Capera03]

Social attitude of agentsPerceive: Perceptions are understood without

ambiguityDecide: Perceptions enable conclusion(s) Act: Actions are useful for the environment (and itself)

A cooperative agent acts toAvoidPreventRemove

situations that it judges as being cooperative failures


Four-layer ModelFour-layer Model

Data

User User User User

Agent

Nominal

Cooperative

Environment

EvolutionTuning Reorganisation

Trigger Environment couplingAccess & Modify


OutlineOutline





Preliminary results

Conclusion


Complexity and Biological SystemsComplexity and Biological Systems

Theories are often missingModelling and simulation (Gepasi [Mendes93],

Copasi…)Different approaches

Mathematical modelsPetri netsCellular automataNeural networks…

DrawbacksBlack boxesModels often staticFar from a biological reality


microMegamicroMega

National projectLISBP, INSA biologists

o « Génie microbiologique » teamo « Physiologie microbienne des eucaryotes » team

LAAS, Disco team mathematiciansLSP, UPS statisticians

Multi-agent modelling of the genetic-metabolic interaction of a yeast (Saccharomyces Cerevisiae)

From:Transcriptomic data: genesMacroscopic data: components

In order to get free from experimental conditionsFeasibility study


Agents and BiologyAgents and Biology

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

Bioinformatics [Luck05] or systems biologyProtein 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 MASLaws considered as knownDisruptions are not taken into account

o Some exceptions [Querrec03, Shafaei08]


Modelling ApproachModelling Approach

Nominal

T R E

Cooperative

Nominal

T R ECooperative

Nominal

T R ECooperative

Nominal

T R ECooperative

Nominal

T R ECooperativeNominal

Nominal

Nominal

Nominal

Nominal

Model

Experimental data

Feedback

Simulated results


OutlineOutline





Preliminary results

Conclusion


Architecture of microMegaArchitecture of microMega

AMAS simulating chemical reactionsTwo kinds of cooperative agents

Functional agentso Physical elementso Reactionso Interactions

Element consumption/production Reactions regulation

Viewer agentso Interactions with userso Data injectiono Specific constraints


FunctionalFunctional Agents Agents

Elements Represent common attributes for each element within

the cellQuantity associated

ReactionsGenes

o Confirm data about transcriptsTransporters

o Move an element quantity from one compartment to anothero Passive / Active (ATP consumption)

Catalysiso Transform a metabolite quantity into twoo Catalysis may be regulated

Synthesiso Assemble two metaboliteso Synthesis may be regulated


ExampleExample

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

Element

Synthesis reaction

Catalysis reaction

Regulation

ConsumptionProduction


Viewer AgentsViewer Agents

ElementViewerAgentGather quantities of a list of element agents

ElementSetterAgentControl activity of a list of element agentsDatabase of experimental quantities

But also…Evaluate biomass

o Sum of the quantities of all element agents Identify compartments within the cell

o If the system is able to reorganiseo Manage user’s constraints


Nominal Behaviour of AgentsNominal Behaviour of Agents

Element agentsManage related element quantity depending on

feedback from reaction agentsLinked to a compartment

Reaction agentsConsume/product element agents depending on:

o Stoichiometryo Contextual reaction speed (possible regulations)

Viewer agentsAccess data of functional agentsStore these dataCompute error related to experimental data


Tuning Behaviour of AgentsTuning Behaviour of Agents

Viewer

Incompetencespeed value

Unproductivenesscreate new context

Incompetencechange quantity

IncompetenceTune stoichiometry

or speed

Unproductivenesscurrent context unknown

Incompetencequantity value

Incompetence

or (quantity value)

Incompetencequantity value < 0

Conflictquantity error detected

Conflictmessage to element


Reorganisation Behaviour of AgentsReorganisation Behaviour of Agents

Viewer

Incompetencetuning failure

Incompetencetuning failure

Uselessnessno partnerUselessness

search for partner

Incompetencechange partner

Incompetencechange/find new regulators

Partial uselessnesssearch for partner

Partial uselessnessNot enough partners


Example: GlycolysisExample: Glycolysis


Preliminary ResultsPreliminary Results

Nominal functioning onlyAdaptive behaviourMemory of previous states


OutlineOutline





Preliminary results

Conclusion


Conclusion - ProspectsConclusion - Prospects

Feasibility demonstrationSelf-building modelSelf-tuning model

Model still incompleteExhibits adaptation abilitiesSelf-building = key for managing complexityEmergence = key for this self-building

Finalise cooperative layersOvercome problems related to noise (forget) Validate models obtained on different

experimental data

ESAW08 – Saint-Etienne – September 26, 2008

Engineering Self-Modelling Systems:Engineering Self-Modelling Systems:Application to BiologyApplication 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

25

ReferencesReferences

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.

[Cornet 06] F. Cornet, Etude d'un problème d'allocation de fréquences par systèmes multi-agents adaptatifs, Research Master IARCL Report, Université Paul Sabatier, June 2006.

[Dotto 99] F. Dotto, L. Trave-Massuyes, P. Glize, Acheminement du trafic d'un réseau téléphonique commuté par une approche multi agent adaptative, Congrès CCIA, Girona.

[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.

26

References (2)References (2)

References related to SMAC team (2) [Ottens 07] K. Ottens, Un système multi-agent adaptatif pour la construction

d'ontologies à partir de textes, PhD thesis, Université Paul Sabatier, IRIT, Toulouse III, 2 October 2007.

[Pesquet 99] B. Pesquet, M.P. Gleizes, P. Glize, Une équipe de robots footballeurs auto-organisée : les SMACkers, Intelligence artificielle située, cerveau, corps et environnement, A. Drogoul & J.A. Meyer coordonnateurs, Editions Hermès, 1999.

[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.

[Sontheimer 99] T. Sontheimer, Modèle adaptatif de prévision de crues par systèmes multi-agents auto-organisateurs, Institut Universitaire Professionnalisé Report, 1999, Diren.

[TFGSO 04] AgentLinkIII TFG “Self-organisation in Multi-Agent Systems” report. [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.

[Welcomme 08] J.B. Welcomme, MASCODE : un système multi-agent adaptatif pour concevoir des produits complexes. Application à la conception préliminaire avion, PhD thesis, Université de Toulouse, 31 March 2008.

27


References external to SMAC team (1)[Amigoni 07] F. Amigoni, V. Schiaffonati, Multiagent-Based Simulation in Biology: A Critical Analysis, In: Model-Based Reasoning in Science, Technology, and Medicine, Springer, Studies in Computational Biology, 64, Lorenzo Magnani and Ping Li (Eds), 179-191, 2007.

[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.

[Bosse 06] T. Bosse, C. Jonker, J. Treur, Simulation and Analysis of Complex Biological Processes: an Organisation Modelling Perspective, In: 39th Annual Simulation Symposium, 2006.

[Camazine 01] S. Camazine, J.L. Deneubourg, N. Franks, J. Sneyd, G. Theraulaz G., E. Bonabeau, Self-Organization in Biological Systems, Princeton University Press, 2001.

[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.

[D’Inverno 05] M. d’Inverno, R. Saunders, Agent-based Modelling of Stem Cell Organisation in a Niche, In: Engineering Self-Organising Systems: Methodologies and Applications, Springer, Brueckner S., Di Marzo Serugendo G., Karageorgos A., Nagpal R. (Eds), LNCS 3464, Springer, 52-68, 2005.

[Emonet 05] T. Emonet, C. Macal, M. North, C. Wickersham, P. Cluzel, AgentCell: a Digital Single-cell Assay for Bacterial Chemotaxis, Bioinformatics Advance Access, In: Bioinformatics, 21, 2714-2721, 2005.

28


References external to SMAC team (2)[Querrec 03] G. Querrec, V. Rodin, J.F. Abgrall, S. Kerdelo, J. Tisseau, Uses of Multiagent Systems for Simulation of MAPK Pathway, In: Third IEEE Symposium on Bioinformatics and Bioengineering (BIBE'03), 421-425, 2003.

[Gonzalez 03] P. González, M. Cárdenas, D. Camacho, A. Franyuti, O. Rosas, J. Lagúnez-Otero, Cellulat: an Agent-based Intracellular Signalling Model, In: Biosystems, 68(2-3), 171-185, 2003.

[Guo 07] D. Guo, E. Santos, A. Singhal, E. Santos, Q. Zhao, Adaptivity Modeling for Complex Adaptive Systems with Application to Biology, In: IEEE International Conference on Systems, Man and Cybernetics, 272-277, 2007.

[Jonker 08] C. Jonker, J. Snoep, J. Treur, H. Westerhoff, W. Wijngaards, BDI-modelling of Complex Intracellular Dynamics, In: Journal of Theoretical Biology, 251, 1-23, 2008.

[Khan 03] S. Khan, R. Makkena, W. Gillis, C. Schmidt, A Multi-agent System for the Quantitative Simulation of Biological Networks, In: Second International Joint Conference on Autonomous Agents & Multiagent Systems (AAMAS’03), Melbourne, ACM, 385-392, 2003.

[Lales 05] C. Lales, N. Parisey, J-P. Mazat, M. Beurton-Aimar, Simulation of Mitochondrial Metabolism using Multi-agents System, In: First Workshop on Multi-Agent Systems for Medecine, Computational Biology, and Bioinformatics (MAS*BIOMED'05 at AAMAS'05), 137-145, 2005.

[Lints 05] T. Lints, Multiagent Modelling of a Bacterial Cell, a DnaA Titration Model Based Agent Model as an Example, In: Ninth Symposium on Programming Languages and Software Tools, Tartu, Estonia, Vene V., Meriste M. (Eds.), 82-96, 2005.

[Luck 05] M. Luck, E. Merelli, TFG on Agents in Bioinformatics, In: Knowledge Engineering Review, 20(2), 117-125, 2005.

[Mendes 93] P. Mendes, GEPASI: A Software Package for Modelling the Dynamics, Steady States and Control of Biochemical and other Systems, In: Computer Applications in the Biosciences, 9(5), 563-571, 1993.

29


References external to SMAC team (3)[Merelli 06] E. Merelli, G. Armano, N. Cannata, F. Corradini, M. d'Inverno, A. Doms, P. Lord, A. Martin, L. Milanesi, S. Moller, M. Schroeder, M. Luck, Agents in Bioinformatics, Computational and Systems Biology, In: Briefings in Bioinformatics, 8(1), 45-59, 2006.

[Troisi 05] A. Troisi, V. Wong, M. Ratner, An Agent-based Approach for Modeling Molecular Self-organization, In: Proceedings of the National Academy of Sciences of the USA (PNAS), 102(2), 255-260, 2005.

[Santos 04] E. Santos, D. Guo, E. Santos Jr., W. Onesty, A Multi-Agent System Environment for Modelling Cell and Tissue Biology, In: International Conference on Parallel and Distributed Processing Techniques and Applications, Las Vegas, USA, CSREA Press, Arabnia H. R. (Eds), 3-9, 2004.

[Shafaei 08] S. Shafaei, N. Aghaee, Biological Network Simulation Using Holonic Multiagent Systems, In: Tenth International Conference on Computer Modeling and Simulation (UKSIM'08), 1-3 April, 617-622, 2008.

[Webb 06] K. Webb, T. White, Cell Modeling with Reusable Agent-based Formalisms, In: Applied Intelligence, 24(2), 169-181, 2006.

engineering self-modelling systems: application to biology

Documents

multiagent technologies

transcriptomic data

genesmacroscopic data

dynamicbiological domain

simulation gepasi mendes93

biological systemstheories

componentsin order

systemautonomous change