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

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

• 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

• 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
• 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

• 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

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