1 / 29

Engineering Self-Modelling Systems: Application to Biology

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.

olisa
Download Presentation

Engineering Self-Modelling Systems: Application to Biology

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. Four-layer Model Data User User User User Reorganisation Tuning Evolution Nominal Cooperative Agent Environment Access & Modify Environment coupling Trigger

  7. 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

  8. 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

  9. 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

  10. 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]

  11. 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

  12. 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

  13. 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

  14. 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

  15. Example 1 Fructose1,6DP + 2 ADP + 2 NAD+ -> 2 Pyruvates + 2 ATP + 2 NADH,H+ Element Synthesis reaction Regulation Consumption Production Catalysis reaction

  16. 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

  17. 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

  18. 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

  19. 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

  20. Example: Glycolysis

  21. Preliminary Results • Nominal functioning only • Adaptive behaviour • Memory of previous states

  22. 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

  23. 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

  24. 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

  25. 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. • [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 multi­agent 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 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 (3) • 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 (4) • 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 (5) • 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.

More Related