國立虎尾科技大學 |

Uncertainty in biology = a computational modeling approach /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Uncertainty in biology/ edited by Liesbet Geris, David Gomez-Cabrero.
其他題名:	a computational modeling approach /
其他作者:	Geris, Liesbet.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	ix, 478 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Biology - Mathematical models. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-21296-8
ISBN:	9783319212968

Uncertainty in biology = a computational modeling approach /
Uncertainty in biologya computational modeling approach /[electronic resource] :edited by Liesbet Geris, David Gomez-Cabrero. - Cham :Springer International Publishing :2016. - ix, 478 p. :ill., digital ;24 cm. - Studies in mechanobiology, tissue engineering and biomaterials,v.171868-2006 ;. - Studies in mechanobiology, tissue engineering and biomaterials ;v.9..

An 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.

Computational 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.

ISBN: 9783319212968

Standard No.: 10.1007/978-3-319-21296-8doiSubjects--Topical Terms:

528533
Biology
--Mathematical models.

LC Class. No.: QH323.5 / .U53 2016

Dewey Class. No.: 570.15195

Uncertainty in biology = a computational modeling approach /
LDR:03607nam a2200325 a 4500 001 860765
003 DE-He213
005 20160720155334.0
006 m d
007 cr nn 008maaau
008 170720s2016 gw s 0 eng d
020 $a 9783319212968 $q (electronic bk.)
020 $a 9783319212951 $q (paper)
024 7 $a 10.1007/978-3-319-21296-8 $2 doi
035 $a 978-3-319-21296-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QH323.5 $b .U53 2016
072 7 $a MQW $2 bicssc
072 7 $a TEC009000 $2 bisacsh
082 0 4 $a 570.15195 $2 23
090 $a QH323.5 $b .U54 2016
245 0 0 $a Uncertainty in biology $h [electronic resource] : $b a computational modeling approach / $c edited by Liesbet Geris, David Gomez-Cabrero.
260 $a Cham : $c 2016. $b Springer International Publishing : $b Imprint: Springer,
300 $a ix, 478 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in mechanobiology, tissue engineering and biomaterials, $x 1868-2006 ; $v v.17
505 0 $a An 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 $a Computational 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 $a Biology $x Mathematical models. $3 528533
650 0 $a Uncertainty $x Mathematical models. $3 559443
650 1 4 $a Engineering. $3 561152
650 2 4 $a Biomedical Engineering. $3 588771
650 2 4 $a Computational Science and Engineering. $3 670319
650 2 4 $a Computer Appl. in Life Sciences. $3 593908
700 1 $a Geris, Liesbet. $3 1072387
700 1 $a Gomez-Cabrero, David. $3 1102668
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a Studies in mechanobiology, tissue engineering and biomaterials ; $v v.9. $3 890002
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-21296-8
950 $a Engineering (Springer-11647)