1. Grand Challenges Nature-inspired Systems, Modelling, Optimisation and Control Palma de Mallorca – 9 June 2006

2. NiSIS Roadmap 1. Introduction 2. State-of-the-art 3 Applications and Existing Challenges (1.-3. already public via www.nisis.de) 4. New Challenges - Grand Challenges (to be published after the Mallorca meeting up to 08/2006) 5. Impacts …

3. ICT-Systems inspired by Living Systems (from 2.3.) Metabolism of matter and energy and information Propagation, Self-Reproduction, Self-Information, Heredity Communication inside and outside the living system with its environment Mutability in structure and information processing Evolvability in function and behaviour

4. Grand Challenges NiMOC (06/2006) [Contribution to NiSIS Roadmap chapter 4.3] Nature-inspired Modeling Nature-inspired Optimization Nature-inspired Control

5. Grand Challenges in Nature-inpired Modeling (1) Modeling of high-dimensional processes in space and time (learned e.g. from the spacial and temporal organization of the liver) Network inference including non-linearities (learned from multi- and instabilities in living systems) Context-dependend network models (learned from protein-protein interaction networks) Modeling of growing, maturation and aging networks (learned from biological and social networks) Growing artificial neuronal networks (learned from brain) ‘Complex’ functions (learned from genotype-phenotype mapping, i.e. sequences  networks) (Network) Model inference from incomplete and heterogeneous data (learned e.g. from metabolic network infererence from genome, …, metabolome data)

6. Grand Challenges in Nature-inpired Modeling (2) Cell-cell Interaction (as e.g. in heterogeneous liver cell bioreactor or the liver as such), extending protein-protein interaction modelling; cytokine networks as an example of a natural cellular network, analogies to rheumatoid arthritis, Cell-Cell interaction using synergies of capabililties of different cell types In terms of methods, even huge differential equation system may not be suffiecient, perhaps a new mathematical description is needed, hybrid models; Multiple models with redundancy, use of redundant models in particular with respect to control, e.g. in the context of medicine, drug injection Cells are highly dynamic systems, small individual models (e.g. with respect to regulation) and their interaction in order to develop interfaces between them

7. Grand Challenges in Nature-inpired Modeling (3) Probabilistic models in particular in the context of control, perhaps something more general than known Bayesian Networks Monte Carlo Methods for biological networks too? How to shape realistic neurons? - to develop better methods to relate morphology and function, rather the field of neuro science but should also be on the list of NiSIS - adaptive structures - general issue with respect to biology

8. Grand Challenges in Nature-inspired Optimization (1) Biomimetical solutions (evolutionary algorithms = microevolutoin  macro-evolutionary strategies (learning from the ‘big steps’ in the biol. Evolution, e.g. uni-  multicellular organisms, procaryotes  eucaryotes) Optimization of cyclic processes and multi-tasking processes (learned e.g. from the liver) optimization by permanently renewed (recyled) material (learned e.g. from the regeneration of liver cells/hepatocytes), see also modeling of spatio-temporal organization Self-configuring by modifying the interaction of components

9. Grand Challenges in Nature-inspired Optimization (2) identification of application areas and cases for strategies learned from nature models of biodiversity, e.g. with respect to pharmaceutical research and development, lead structure search etc. reliable sensor development as a prerequisite for optimization and control Adaptive distributed optimization and control how can we understand adaptation as optimization as a natural analogy

10. Grand Challenges in Nature-inspired Control (1) Control of interconnected processes (learned e.g. from co- ordinated actioin E. coli’s catabolism and anabolism) Data-Filtering by improved Artificial Immune systems (learned e.g. the defense of own but harmful, e.g. tumor cells and considering also dysregulation during autoimmune diseases and its therapy) Sensoring of multiple signals and information processing toward multiple targets /MIMO (learned e.g. from signaling pathways into and in cells including cross-talk of such pathways, signal transduction via membranes, second messengers) Distributed control by multi-agent systems (e.g. pheromone-based robotics,…)

11. Grand Challenges in Nature-inspired Control (2) Self-healing, i.e. diagnose and react to system malfunctions, including regeneration of system components (learned e.g. from the regeneration capacity of liver) Recommendation systems with collaborative filtering by artificial immune systems Real-time pattern recognition and learning including image-data processing (knowledge generation from image data, learned e.g. from ant colonies),

12. Grand Challenges in Nature-inspired Control (3) Fault detection and fault tolerance control, e.g. in analogy to proofreading in biology, similar to self- healing; but proofreading in simple organisms like E. coli is already fairly difficult to understand and model; operation under constraints question of comparability of biological systems and e.g. technical application problems; but again primarily we ought to use nature as an inspiration with respect to control: component-orientated methods, algorithms, software; still in its early days yet

13. Grand Challenges in Nature-inspired Control (4) Adaptation issues with respect to artificial immune systems, may be similar to issues of biodiversity Adaptive learning currently much movement; we may have outputs, but what are the questions? Computational modelling to compare algorithms