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Fernando Martin-Sanchez, PhD. Head, Medical Bioinformatics Dept.

Fernando Martin-Sanchez, PhD. Head, Medical Bioinformatics Dept. National Institute of Health ‘Carlos III’ Madrid, SPAIN. BEYOND-THE-HORIZON TG4: Bio-ICT Synergies Palma de Mallorca, June 8 , 2005 NISIS Meeting. Presentation.

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Fernando Martin-Sanchez, PhD. Head, Medical Bioinformatics Dept.

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  1. Fernando Martin-Sanchez, PhD. Head, Medical Bioinformatics Dept. National Institute of Health ‘Carlos III’ Madrid, SPAIN BEYOND-THE-HORIZON TG4: Bio-ICT Synergies Palma de Mallorca, June 8 , 2005 NISIS Meeting

  2. Presentation

  3. Institute of Health “Carlos III”Ministry of Health and Consumer Affairs • Public Research Institute • Scientific and technological support to the National Health System of Spain. • Competences in: • Epidemiology, Public health laboratories (Food, Microbiology, Environmental Health) • Health Technology Assessment • Regenerative medicine and cell therapy • Biomedical research funding and coordination • School of Public Health, Health Sciences National Library • New technologies - Telemedicine, Bioinformatics and genomics, Health information systems

  4. Medical Bioinformatics Dept. • Established in 1998 • R+D in new technologies for the management of genetic information and its application in biomedical research and clinical practice • Multidisciplinary group including biologists, chemists, informaticians, statisticians, pharmacists and physicians.

  5. Medical Bioinformatics Dept. Microarrays & Bioinformatics Laboratory: • Established in 2000 • Affymetrix - 417 Arrayer • Affymetrix - 418 Scanner (2 channels) • Scanarray HT Scanner (20 slides holder & 4 channels) • Hybridization ovens • Other laboratory equipment • Applications in clinical microbiology

  6. Previous experience in prospective studies • BIOINFOMED –In this European Project (2002-2003) we coordinated an international panel of 30 experts. The group produced a White Paper that, based on the study of the convergence and synergies between medical informatics and bioinformatics, yielded a research roadmap in Biomedical Informatics. • SYMBIOMATICS – As a continuation of the previous project, this ERA-Net (2005-2006) aims to advance in the definition and research priorities in the field of applications of IT in Genomic Medicine. • VISION BOOK – Project funded by the EC in which 20 invited authors describe their long-term vision on how IST will affect different societal aspects. Chapter corresponding to Healthcare. • ICT-BIO – Rapporteur in the Meeting “Information Technologies at the Crossroads with Life Sciences”, organized by the European Commission. Meeting in Brussels (Oct 12th 2004).

  7. BACKGROUND EC Workshop ICT at the Crossroads with Life Sciences Brussels – Oct 12th, 2004

  8. Research RoadmapVision • Higher living systems contain at least two systems that can be interpreted as ‘natural’ information processors, namely neurons and genes. • Advances in biological and neuro-sciences of the last 20 years have enhanced our understanding of aspects like development, action, perception, homeostasis and learning. • This vast body of knowledge can be exploited for designing and implementing new ICT systems

  9. ROADMAP Math. Formal Methods Computation Modeling Combine Uncertainty Fault tolerance continuous & discrete Redundancy Hierarchy Therapy Drug design automatism Faster, robust algorithms for applications Non linear model Coding transmission (chem.) Parallelism Physic Chemistry Information Concurrency Bi-directional Communication with living systems Neuro Science Chemical / Cell structure of the brain Interaction with other brain structures Bio-Sensors plasticity Brain consciousness Gene Neurons Life Complexity neurons cortical collums cortex NN, Neurocomp. Autonomous Systems Chips ICT Interfacing (neuro, nano, chemistry) Pattern recognition 3D structure dynamics Display / Visualization Information measuremnts Eco-computers Physical substrates (biocompatible fault tolerance) A.I. Synthetic life forms ICT Engineering Viruses Interfacing Robotics New Membrane computing GG.AA., Cell computing PropertiesComputationalgorithms Immune endocrine algorithms Genome – genome interaction BIO New implants architectures environment Genes  stem cells cells tissues organs Building blocks: “Genes, Brain & Chips” TG5 Time / difficulty

  10. Toward NBIC convergence • Convergence of the biological, information, nano-, and cognitive sciences is accelerating. • A major limiting roadblock is the need for researchers to be familiar with concepts in more than one discipline. • In particular, the information technology and life science domains have developed independently with very different terminologies and concepts. • However, over the last decade new research communities did overcome this problem and successful inter-disciplinary collaborations have emerged.

  11. Successful examples of convergence • In neuro-informatics, ICT learns from the ways information is processed in our neural system and in living organisms in general, by studying for example the associative capabilities of the human brain, or the behaviour of ant colonies and bee swarms and how these self-organise. • In computational biology, ICT is applied to model, for example, the living cell. • Engineers and biologists join forces in ‘biomimetics’, where mechanisms found in nature are used to inspire technological design, possibly leading to ‘lifelike’ computers able to grow, self-repair and adapt.

  12. Bridging the conceptual gap between the bio- and techno-disciplines • A common conceptual basis for Bio-ICT research is needed • This can be achieved in modelling projects, leading to: • better biological system modelling, • new types of computational architectures • new ways of computing that reflect the ‘information processing’ type of activities in living organisms (cells, neurons, genes, metabolism, etc.).

  13. Bridging the temporal gap between the bio- and techno-disciplines • The static-dynamic dichotomy between biological and ICT systems is one of the key hurdles in the natural combination of living and artificial systems. • The static nature of ICT is incompatible with the processes of change that govern the Bio. • This leads to biological systems having to take all the burden of adaptation in the synergistic combination, and to ICT systems prone to becoming obsolete, malfunctioning and disturbing.

  14. Bridging the physical gap between the bio- and techno-disciplines • The physical incompatibility between biological and ICT systems (‘wet-dry dichotomy’) requires work on interfacing, embedding and on hybrid combinations of biological and ICT systems. • Not only prosthesis work, there is a much wider potential. • For instance, brain-machine and neural interfaces could cover a large field of applications ranging from motor prostheses to any kind of sensory prostheses. • One could even imagine ICT-based interactions directly within metabolic processes in organisms (smart drugs, for instance).

  15. Proposed Research Programmes • New Modelling Paradigms (conceptual gap) • Bio-Inspired Strategies of Growth, Adaptation and Evolution (temporal gap) • Bio-ICT Artifacts (physical gap)

  16. Natural world Digital world Artificial world Virtual cell 3-Bio-ICT artifacts 1-Modeling Augment repair, ... Understanding biology Artificial cell Natural cell Hybrid tissue or muscle 2-Strategies Replication, Growth, Evolution, ...

  17. Major steps towards bridging these gapsNew Computational Modelling Paradigms • New ways of computing capable of capturing, relating and integrating the different levels of complexity in biological systems — from the molecule to the cell, tissue, organ, system, individual, population and ecosystem. • New computing systems based on life-like hardware, consisting of large numbers of simple devices operating in a highly parallel fashion at comparatively low speed and with very low power dissipation. • New bio-inspired computation, capable of autonomy and self-organisation.

  18. Major steps towards bridging these gapsBio-Inspired Strategies of Growth, Development and Evolution • Technological systems should mimic the capacity of biological systems to grow, adapt, self-assemble, replicate, heal, self-organize, and evolve. • The idea of using these strategies for problem solving is, of course, not new – think of genetic algorithms – but more recent understanding of biological processes have not been exploited yet (for example, SNPs). • Enormous application potential, for software and hardware, to develop new types of growing, self-assembling or evolving hardware, • For example memory growing when needed, and intelligent materials applicable in ambient interfaces (displays, active surfaces).

  19. Biological concepts that can inspire ICT systems • Ecology (colonies) • Organisms (robots) • Organs (Brain) • Systems (Immune) • Cell (pathways, interactions, networks, membranes, embryonics) • Molecules (DNA-RNA-Proteins)

  20. Major steps towards bridging these gapsBio-ICT Artefacts • Artificial entities seamlessly integrated into biological systems, (retinas, limbs) • Applications include diagnostic technologies, controlled drugs release • The main research challenges are: • new information theories and modelling techniques that capture sensing, action, memory, adaptation, homeostasis • to validate the results with respect to real biological systems • to apply these theories for the design of ICT technologies, that replicate, complement or substitute for these basic capabilities of living systems or are interfaced to them • to study how such technologies can sustainably adapt and evolve to match, over long periods of time, with evolving needs and compatible with various natural processes of change (e.g., growth, learning, aging).

  21. Connections with other B-T-H areas • TG1 - Pervasive computing (evolvable systems, bionets, interfacing with human sensory system, biological sources of electric power) • TG2 - Nanotechnology and nanoelectronics (molecular & DNA computing, self-assembly and bio-inspired fabrication, biomimetic interfaces, magnetic biomarkers, BioNEMS, LOAC, intelligent processing of biomedical signals) • TG3 - Security (bio-inspired mechanisms – immune system, evolutionary strategies) • TG5 - Intelligent and cognitive systems (genotype-phenotype mapping, “evo-devo”strategies, embodiment, growth) • TG6 - Software intensive systems (Adaptation and evolution, design for emergence)

  22. Davide Anguita Jerome Chailloux Alex Dommann Francois Fages Eduardo Fernandez Sten Grillner Francois Kepes Rod Hose Damian Mac Randal Bernard Manderick Victor Maojo Matej Oresic Nico Rijkoff Bernd Schuermann Walter Van de Velde (EC) Thanks to all the participants

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