Uncertainty in Biology : A Computational Modeling Approach

David Gomez-Cabrero

ISBN 10: 3319212958 / ISBN 13: 9783319212951

Editore: Springer International Publishing, 2015

Riguardo questo articolo

Druck auf Anfrage Neuware - Printed after ordering - 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: 1. Modeling establishment under uncertainty 2. Model selection and parameter fitting 3. Sensitivity analysis and model adaptation 4. 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.

Codice articolo 9783319212951

Dati bibliografici

• Titolo: Uncertainty in Biology : A Computational Modeling Approach
• Editore: Springer International Publishing
• Data di pubblicazione: 2015
• Lingua: Inglese
• ISBN 10: 3319212958
• ISBN 13: 9783319212951
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 241 L x 160 A x 32 P (millimetri) 241 millimetri di larghezza per 160 millimetri di altezza per 32 millimetri di profondità
• Peso: 893 grammi

Descrizione articolo

Riassunto

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: 1. Modeling establishment under uncertainty 2. Model selection and parameter fitting 3. Sensitivity analysis and model adaptation 4. 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.

Dalla quarta di copertina

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:

1. Modeling establishment under uncertainty
2. Model selection and parameter fitting
3. Sensitivity analysis and model adaptation
4. 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.