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NiSIS - NiMOC. European Co-ordination Action ‘Nature-inspired Smart Information Systems’ Focus Group NiMOC Nature-inspired Modeling, Optimization and Control Chairman: Reinhard Guthke, HKI Jena, Germany Vice Chairman: Ronald Westra, Univ. Maastricht, The Netherlands. NiMOC: 29 Members. - PowerPoint PPT Presentation
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NiMOC: Nature-inspired Modeling, Optimization and Control R. Guthke
European Co-ordination Action ‘Nature-inspired Smart Information Systems’
Focus Group NiMOC
Nature-inspired Modeling, Optimization and Control
Chairman: Reinhard Guthke, HKI Jena, Germany
Vice Chairman: Ronald Westra, Univ. Maastricht, The Netherlands
NiSIS - NiMOC
NiMOC: Nature-inspired Modeling, Optimization and Control R. Guthke
NiMOC: 29 Members
K. Bayer, J. Borges, S. Burgess, G. Coghill, R. Dudda, J. Garibaldi, R. Guthke, M. Hecker, C. Hummert, C. Igel, S. Jovanovic, K. Leiviskä, J. Lemos, E. Lenart, K. Lieven, D. Linkens, T. Mendonca, D. Naso, A. Nowe, A. Offenhaeusser, S. Pizzuti, E. Plahte, M. Pfaff, M. Poel, P. Rocha, J. Sobecki, K. Tuyls, R. Westra, S. Zellmer
From 10 countries:
AT, BE, DE, FI, IT, NO, NL, PL, PT, UK
NiMOC: Nature-inspired Modeling, Optimization and Control R. Guthke
• NiMOC’s Contribution to the Roadmap
• NiMOC’s further Activities
NiMOC: Activities
NiMOC: Nature-inspired Modeling, Optimization and Control R. Guthke
Contribution of NiMOC to the NiSIS Roadmapwww.nisis.risk-technologies.com/tte/Roadmap.aspx
1. Introduction2. State-of-the-Art…2.3. Modeling and Systems…
3. Applications and Existing Challenges…3.3. Modeling, Optimization and Control
4. Grand Challenges…4.3. NiMOC Grand Challenges
5. Impacts
Annex...
Aachen 2007
NiMOC: Nature-inspired Modeling, Optimization and Control R. Guthke
NiSIS Meetingon “Grand Challenges and
Impact”Aachen, Sept 6-7, 2007
NiMOC: Nature-inspired Modeling, Optimization and Control R. Guthke
Accomplisehed Activities 2007
• Special Issue: Lecture Notes in Bioinformatics 4366 (2007), Eds. K. Tuyls, R. Westra, Y. Saeys, A. Nowé
• Teresa Mendonca, Jose Lemos: Case Study “Case Study: Contributions to Modeling and Identification of Biomedical Systems”
• Teresa Mendonca, Jose Lemos: NI Modelling and Control of Anaesthesia Including a Summer School on Modelling and Control of Physiological Variables: Nature Inspired Approaches, Portugal, on May 2-3, 2007
• Michael Pfaff, Reinhard Guthke: NiSIS School and Workshop, March 15-16, 2007, Jena, Germany, School: “Integrated Analysis of Transcriptome and Proteome Data”,Workshop on “Data and Knowledge Based Biomolecular Network Reconstruction”
• George Coghill: Visit at BCJ and HKI Jena for participation in the NiMOC Workshop
• NiMOC committee Meeting, Malta, November 26, 2007
NiMOC: Nature-inspired Modeling, Optimization and Control R. Guthke
• Teresa Mendonca, Jose Lemos: Case Study “Case Study: Contributions to Modeling and Identification of Biomedical Systems”
Accomplisehed Activities 2007
NiMOC: Nature-inspired Modeling, Optimization and Control R. Guthke
• Teresa Mendonca, Jose Lemos: NI Modelling and Control of Anaesthesia Including a Summer School on Modelling and Control of Physiological Variables: Nature Inspired Approaches, Portugal, on May 2-3, 2007
Accomplisehed Activities 2007
NiMOC: Nature-inspired Modeling, Optimization and Control R. Guthke
NiSIS / JCB / DFG International Spring School and Workshop
Data Mining and Modelling in Systems BiologyData Mining and Modelling in Systems Biology
International Spring School
Integrative Analysis of Transcriptome and Proteome DataIntegrative Analysis of Transcriptome and Proteome Data
15th March 2007, Jena/Germany
International Workshop
Data and Knowledge Based Biomolecular Network ReconstructionData and Knowledge Based Biomolecular Network Reconstruction
16th March 2007, Jena/Germany
NiMOC: Nature-inspired Modeling, Optimization and Control R. Guthke
Accomplisehed Activities 2007
Michael Pfaff, Reinhard Guthke: NiSIS School, March 15, 2007, Jena, Germany
NiMOC: Nature-inspired Modeling, Optimization and Control R. Guthke
Accomplisehed Activities 2007
Michael Pfaff, Reinhard Guthke: NiSIS Workshop, March 16, 2007, Jena, Germany
NiMOC: Nature-inspired Modeling, Optimization and Control R. Guthke
• NiSIS Spring School on “Integrated Analysis of Transcriptome and Proteome Data”, Jena, March 15, 2007
• NiSIS Workshop on “Data and Knowledge Based Biomolecular Network Reconstruction”, Jena, March 16, 2007
70 participants from 6 countries (Austria, U.K., The Netherlands, Sweden, Swizerland, Germany)
NiMOC Activities 2007
NiMOC: Nature-inspired Modeling, Optimization and Control R. Guthke
Nature-inspired Modelling, Optimization and Control are dedicated to the investigation of intelligent paradigms existing in Nature and studied by systems approaches, such as Systems Biology, in order to learn from them how to better design smart, i.e. intelligent, adaptive and advanced information systems.
Nature-inspired Algorithms are most appropriate for problems of optimization, scheduling, chemometrics, routing, and assignment, management, organization, and logistics.
NiMOC Conclusions (1/3)
NiMOC: Nature-inspired Modeling, Optimization and Control R. Guthke
Regulatory gene expression and cellular signal transduction may be considered as some kind of data processing or information processing. Together with motif recognition on promoters and enhancers they seem to have the potential for the design of new Nature-inspired algorithms of data and information processing.
Within NiSIS, Reverse Engineering in Systems Biology may also be considered an essential first step to elucidate/reconstruct some of Nature’s information processing principles in order to proceed towards design of more advanced artificial information systems.
NiMOC Conclusions (2/3)
NiMOC: Nature-inspired Modeling, Optimization and Control R. Guthke
Approaches to Nature-inspired Modeling, Optimization and Control represented by NiMOC members are:•Reverse Engineering
•Fuzzy rule-based modeling
•Linguistic reasoning and fuzzy modeling and inference
•Fuzzification as coarse graining in multiscale modeling and simulation
•Cellular automata, Turing models
•Neuro-fuzzy hybrid models
•Coupling intragranular dynamics with extragranular dynamics
•Modeling from sparse data
•Hierarchical decomposition of decision-making and control
•Modelling self-organizing adaptive behaviour
•Multi-objective optimisation/goal seeking using Particle Swarm Intelligence
•Modelling Coupled moduls
•Artificial Immune System
•Piecewise Linear Dynamic Modeling
•Network Modelling and Simulation from sparse, incomplete and uncertain data
NiMOC Conclusions (3/3)