Upload
bozica
View
42
Download
0
Tags:
Embed Size (px)
DESCRIPTION
Finnish Centre of Excellence (2006-2011) in Computational Complex Systems Research (COSY) @ Department of Biomedical Engineering and Computational Science, Helsinki University of Technology. Challenges: Pierre Laplace (1749-1827): “ The simplicity of nature is not to be - PowerPoint PPT Presentation
Citation preview
Finnish Centre of Excellence (2006-2011) inFinnish Centre of Excellence (2006-2011) inComputational Complex Systems Research Computational Complex Systems Research
(COSY)(COSY)@@
Department of Biomedical Engineering and Department of Biomedical Engineering and Computational Science, Computational Science,
Helsinki University of TechnologyHelsinki University of Technology
Challenges:Challenges: Pierre Laplace (1749-1827): “Pierre Laplace (1749-1827): “The simplicity of nature is not to be The simplicity of nature is not to be measured by that of our conceptions. Infinitely varied in its effects,measured by that of our conceptions. Infinitely varied in its effects, nature is simple only in its causes, and its economy consists innature is simple only in its causes, and its economy consists in producing a great number of phenomena, often very complex, byproducing a great number of phenomena, often very complex, by means of a small number of general laws.”means of a small number of general laws.”
Stephen Hawking: Stephen Hawking: “I think the 21“I think the 21st st century will be the century ofcentury will be the century of complexity.”complexity.”
Mission & Approach: Mission & Approach:
For a Complex System – be it physical, For a Complex System – be it physical, biological or societal – one needs to make a biological or societal – one needs to make a
shift in research paradigm by taking a shift in research paradigm by taking a multidisciplinary and holistic system-level multidisciplinary and holistic system-level
approach in terms ofapproach in terms of
Computational Analysis & Modelling & Computational Analysis & Modelling &
SimulationSimulation BECSBECSCOSYCOSY
Complexity of a system:Complexity of a system:Structure & Structure & Function & ResponseFunction & Response
Communication system:Many non-identical
elements linked with diverse interactions
NETWORK
Six degrees - Small WorldC. ELEGANS: 19500
genes HOMO SAPIENS: 23300 genes
FRUIT FLY : 13600 genes
ARABIDOPSIS (mustard): 27000 genes
Is complexity in number?
Self-organisation – Emergent properties in Self-organisation – Emergent properties in structure, function and responsestructure, function and response
Complexity – How to approachComplexity – How to approachComplex systems:Complex systems:
Large number of interdependent agents Large number of interdependent agents (molecules, individuals, species, consumers, (molecules, individuals, species, consumers, companies...). companies...).
Self-organisation -> Emergent properties: Self-organisation -> Emergent properties: Structure & Function & ResponseStructure & Function & Response..
Adaptive and robust (biological and social Adaptive and robust (biological and social systems).systems).
Not understood by studying parts in isolation.Not understood by studying parts in isolation.
Change in research paradigm:Change in research paradigm:Holistic system level viewpoint.Holistic system level viewpoint.
Combination of Combination of physical, mathematical, biological, physical, mathematical, biological, social sciences…social sciences… towards transdisciplinaritytowards transdisciplinarity
Computational modelling and analysis:Computational modelling and analysis: Holistic viewpoint at the system level behaviour. Holistic viewpoint at the system level behaviour.
Generic tool for qualitative and quantitative Generic tool for qualitative and quantitative studies.studies.
Computational Complex Systems Computational Complex Systems ResearchResearch
Models & MethodsModels & Methods• Complex Networks and Agent-Based ModelsComplex Networks and Agent-Based Models• Complex Dynamics and Statistical PhysicsComplex Dynamics and Statistical Physics• Statistical and Information Theoretic Modelling Statistical and Information Theoretic Modelling
MethodsMethods• Brain Signal AnalysisBrain Signal Analysis
Engineered & Artificial SystemsEngineered & Artificial Systems• Engineered NanosystemsEngineered Nanosystems• Modelling of Learning and PerceptionModelling of Learning and Perception• Computational NeuroscienceComputational Neuroscience
Cognitive & Social SystemsCognitive & Social Systems• Cognitive SystemsCognitive Systems• Structure and Dynamics of Social NetworksStructure and Dynamics of Social Networks
Computational Systems BiologyComputational Systems Biology• BioimagingBioimaging• Biospectroscopy Biospectroscopy -> Computational Medicine-> Computational Medicine
Complex systems and networks – Oxford unitComplex systems and networks – Oxford unit
Models & MethodsModels & MethodsNetworks & agent-based models Complex dynamics & stat. physics
Statistical modelling methods Brain signal analysis
Bayesian MEG/fMRI data analysis
Polymer translocation
Healthcare data analysis
Network theory & dynamics
Engineered & Artificial Engineered & Artificial SystemsSystems
Engineered Nanosystems
Modelling of Learning and Perception Bayesian Object RecognitionBayesian Object Recognition Computational Computational
NeuroscienceNeuroscience
Optical (quantum) Optical (quantum) memoriesmemories
Quantum dots
Cognitive & Social SystemsCognitive & Social Systems
Cognitive Systems• Modulation of auditory system tuning by selective attention
(PNAS 2004 & PNAS 2006 & PNAS 2007)Structure & Dynamics of Social Networks- Mobile phone network- Analysis & Modelling
(PNAS 2007 & PRL 2007)PNAS = Proc. Natl. Acad. Sci.PRL = Physical Review Letters
Computational Systems Computational Systems BiologyBiology
Bioimaging-Cryo Bioimaging-Cryo EM:EM:
IIllustration of LDL llustration of LDL particles at 37particles at 37ooC and C and 66ooCCIn preparation.In preparation.
Biospectroscopy -Biospectroscopy -NMRNMR Metabonomics:Metabonomics: Atherosclerosis;health path and risk profilingAnnals of Medicine 38, 322-Annals of Medicine 38, 322-336, 2006;336, 2006;
NMR in Biomedicine, 20, 658, NMR in Biomedicine, 20, 658, 2007;2007;
Molecular Systems Biology 4, Molecular Systems Biology 4, 167, 2008167, 2008;;
» Computational Computational Medicine Medicine
Collaboration HighlightsCollaboration Highlights (international)(international)
CSNR, Oxford with CABDyN, Oxford research cluster:CSNR, Oxford with CABDyN, Oxford research cluster:Reseach:Reseach: Complex systems and networks, Mathematical Complex systems and networks, Mathematical BiologyBiology
Harvard Medical School, MIT & Mass. Gen. Hospital, Harvard Medical School, MIT & Mass. Gen. Hospital, BostonBoston
Research:Research: Cognitive Neuroimaging Cognitive Neuroimaging
Harvard University & U. Notre Dame, Boston & IndianaHarvard University & U. Notre Dame, Boston & IndianaResearch:Research: Complex Networks Complex Networks
Budapest University of Technology,Budapest University of Technology,Research:Research: Complex Systems and Networks, Econophysics … Complex Systems and Networks, Econophysics …
Inst. for Cross-Disciplinary Physics and Complex Systems, Inst. for Cross-Disciplinary Physics and Complex Systems, Palma, Palma,
Research:Research: Complex Systems and Networks and Agent-based Complex Systems and Networks and Agent-based modelsmodels
Northwestern University, Institute of Neuroscience, Northwestern University, Institute of Neuroscience, ChicagoChicago
Research:Research: Cognitive neuroscience Cognitive neuroscience
Georgetown University Medical Center, Washington DCGeorgetown University Medical Center, Washington DCReserch: FIDIPRO; Systems neuroscience (monkey & human)Reserch: FIDIPRO; Systems neuroscience (monkey & human)
CoE 2000 – 2005CoE 2000 – 2005 20002000 ’’0101 ’’0202 ’’0303 ’’0404 ’’0505 TotalTotal
PhD’s 2 3 3 2 5 8 23
MSc’s 7 6 8 5 7 17 50
Refereed journals 34 29 37 49 59 55 263
Impact factor/paper NA NA 2.12 2.30 2.53 2.86 -
Book chapters 7 9 5 8 11 12 52
Pleanary/invited talks 7 15 9 13 11 11 66
CoE 2006 - 2011CoE 2006 - 2011 20062006 ’’0707 3/’083/’08 ’’0808 ’’0909 ’’1010 ’’1111 ’’06-06-3/’083/’08
’’06-06-’’0808
’’06-06-’11’11
PhD’s 9 6 2 6 17 21 30
MSc’s 23 17 2 15 42 55
Refereed journals 60 60 16 60 120 180 300
Impact factor/paper 3.25 3.55 -
Book chapters 8 4 12
Pleanary/invited talks 11 13 24
CoE’s achievementsCoE’s achievements
CoE’s achievementsCoE’s achievementsDegrees: CCSE(2000-05) COSY (2006-07)
CCSE COSY
Publications: CCSE(2000-05) COSY (2006-07)
CCSE COSY
Impact factor /publication