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NiMOC: Nature-inspired Modeling, Optimization and ControlR. Guthke European Co-ordination Action ‘Nature-inspired Smart Information Systems’ Focus Group.

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Presentation on theme: "NiMOC: Nature-inspired Modeling, Optimization and ControlR. Guthke European Co-ordination Action ‘Nature-inspired Smart Information Systems’ Focus Group."— Presentation transcript:

1 NiMOC: Nature-inspired Modeling, Optimization and ControlR. 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

2 NiMOC: Nature-inspired Modeling, Optimization and ControlR. 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

3 NiMOC: Nature-inspired Modeling, Optimization and ControlR. Guthke NiMOC’s Contribution to the Roadmap NiMOC’s further Activities NiMOC: Activities

4 NiMOC: Nature-inspired Modeling, Optimization and ControlR. Guthke Contribution of NiMOC to the NiSIS Roadmap www.nisis.risk-technologies.com/tte/Roadmap.aspx 1. Introduction 2. 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

5 NiMOC: Nature-inspired Modeling, Optimization and ControlR. Guthke NiSIS Meeting on “Grand Challenges and Impact” Aachen, Sept 6-7, 2007

6 NiMOC: Nature-inspired Modeling, Optimization and ControlR. 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

7 NiMOC: Nature-inspired Modeling, Optimization and ControlR. Guthke Teresa Mendonca, Jose Lemos: Case Study “Case Study: Contributions to Modeling and Identification of Biomedical Systems” Accomplisehed Activities 2007

8 NiMOC: Nature-inspired Modeling, Optimization and ControlR. 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

9 NiMOC: Nature-inspired Modeling, Optimization and ControlR. Guthke NiSIS / JCB / DFG International Spring School and Workshop Data Mining and Modelling in Systems Biology International Spring School Integrative Analysis of Transcriptome and Proteome Data 15 th March 2007, Jena/Germany International Workshop Data and Knowledge Based Biomolecular Network Reconstruction 16 th March 2007, Jena/Germany

10 NiMOC: Nature-inspired Modeling, Optimization and ControlR. Guthke Accomplisehed Activities 2007 Michael Pfaff, Reinhard Guthke: NiSIS School, March 15, 2007, Jena, Germany

11 NiMOC: Nature-inspired Modeling, Optimization and ControlR. Guthke Accomplisehed Activities 2007 Michael Pfaff, Reinhard Guthke: NiSIS Workshop, March 16, 2007, Jena, Germany

12 NiMOC: Nature-inspired Modeling, Optimization and ControlR. 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

13 NiMOC: Nature-inspired Modeling, Optimization and ControlR. 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)

14 NiMOC: Nature-inspired Modeling, Optimization and ControlR. 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)

15 NiMOC: Nature-inspired Modeling, Optimization and ControlR. 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)

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