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001 978-3-319-21296-8
003 DE-He213
005 20200421111705.0
007 cr nn 008mamaa
008 151026s2016 gw | s |||| 0|eng d
020 _a9783319212968
_9978-3-319-21296-8
024 7 _a10.1007/978-3-319-21296-8
_2doi
050 4 _aR856-857
072 7 _aMQW
_2bicssc
072 7 _aTEC009000
_2bisacsh
082 0 4 _a610.28
_223
245 1 0 _aUncertainty in Biology
_h[electronic resource] :
_bA Computational Modeling Approach /
_cedited by Liesbet Geris, David Gomez-Cabrero.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aIX, 478 p. 142 illus., 45 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Mechanobiology, Tissue Engineering and Biomaterials,
_x1868-2006 ;
_v17
505 0 _aAn Introduction to Uncertainty in the Development of Computational Models of Biological Processes -- Reverse Engineering under Uncertainty -- Probabilistic Computational Causal Discovery for Systems Biology -- Macroscopic Simulation of Individual-Based Stochastic Models for Biological Processes -- The Experimental Side of Parameter Estimation -- Statistical Data Analysis and Modeling -- Optimization in Biology: Parameter Estimation and the Associated Optimization Problem -- Interval Methods -- Model Extension and Model Selection -- Bayesian Model Selection Methods and their Application to Biological ODE Systems -- Sloppiness and the Geometry of Parameter Space -- Modeling and Model Simplification to Facilitate Biological Insights and Predictions -- Sensitivity Analysis by Design of Experiments -- Waves in Spatially-Disordered Neural Fields: a Case Study in Uncertainty Quantification -- X In-silico Models of Trabecular Bone: a Sensitivity Analysis Perspective -- Neuroswarm: a Methodology to Explore the Constraints that Function Imposes on Simulation Parameters in Large-Scale Networks of Biological Neurons -- Prediction Uncertainty Estimation Despite Unidentifiability: an Overview of Recent Developments -- Computational Modeling Under Uncertainty: Challenges and Opportunities.
520 _aComputational modeling of biomedical processes is gaining more and more weight in the current research into the etiology of biomedical problems and potential treatment strategies.  Computational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process.  This book wants to address four main issues related to the building and validation of computational models of biomedical processes: Modeling establishment under uncertainty Model selection and parameter fitting Sensitivity analysis and model adaptation Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples.  This book is intended for graduate students and researchers active in the field of computational modeling of biomedical processes who seek to acquaint themselves with the different ways in which to study the parameter space of their model as well as its overall behavior.
650 0 _aEngineering.
650 0 _aBioinformatics.
650 0 _aComputational biology.
650 0 _aComputer mathematics.
650 0 _aBiomedical engineering.
650 1 4 _aEngineering.
650 2 4 _aBiomedical Engineering.
650 2 4 _aComputational Science and Engineering.
650 2 4 _aComputer Appl. in Life Sciences.
700 1 _aGeris, Liesbet.
_eeditor.
700 1 _aGomez-Cabrero, David.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319212951
830 0 _aStudies in Mechanobiology, Tissue Engineering and Biomaterials,
_x1868-2006 ;
_v17
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-21296-8
912 _aZDB-2-ENG
942 _cEBK
999 _c55217
_d55217