Engineering Self-Modelling Systems: Application to Biology

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

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