Studies in Mechanobiology, Tissue Engineering and Biomaterials 17 Liesbet Geris David Gomez-Cabrero Editors Uncertainty in Biology A Computational Modeling Approach Studies in Mechanobiology, Tissue Engineering and Biomaterials Volume 17 Series editor Amit Gefen, Ramat Aviv, Israel More information about this series at http://www.springer.com/series/8415 Liesbet Geris David Gomez-Cabrero (cid:129) Editors Uncertainty in Biology A Computational Modeling Approach 123 Editors LiesbetGeris DavidGomez-Cabrero Biomechanics ResearchUnit Centerfor Molecular Medicine University of Liège Karolinska University Hospital Liège Stockholm Belgium Sweden ISSN 1868-2006 ISSN 1868-2014 (electronic) Studies in Mechanobiology,Tissue EngineeringandBiomaterials ISBN978-3-319-21295-1 ISBN978-3-319-21296-8 (eBook) DOI 10.1007/978-3-319-21296-8 LibraryofCongressControlNumber:2015945595 SpringerChamHeidelbergNewYorkDordrechtLondon ©SpringerInternationalPublishingSwitzerland2016 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped. 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Printedonacid-freepaper SpringerInternationalPublishingAGSwitzerlandispartofSpringerScience+BusinessMedia (www.springer.com) Preface We are very proud to present the latest addition to the Springer series Studies in Mechanobiology,TissueEngineeringandBiomaterials.Whenwestartedthisbook, wewantedtocreateabookthatcouldserveasastartingpointforgraduatestudents and researchers interested in the development of computational models of biolog- ical processes, with a specific focus on how to deal with the inherent uncertainty. We have managed to get a great international set of authors together, each discussing on a particular aspect of the problem based on their own expertise and research background. All chapters start with a detailed theoretical description that serves the dual purposeofintroducingthetechnique andprovidingsufficientdetails(inthetextor by means of references to the literature) for all researchers to start using it them- selves.Subsequentlyoneormoreexamplesillustratehowthetechniquecanbeused in a practical setting. Chapters are ordered according to the order in which the technique they describe appears in the development and implementation of new models.Readingthebookfromstarttofinishwillthereforeprovidenewresearchers with a quite extensive tool set to get started for themselves. More experienced researchers will find for specific techniques the latest developments and a discus- sion offuture developments. Thisbookistheendproductofalengthyprocesswhichhassufferedfromsome unforeseen delays. Yet the vision and drive always remained present amongst the editors and authors. We are very happy with the end result and hope that readers will enjoy the book as much as we’ve enjoyed putting it together. Prof. Liesbet Geris Prof. David Gomez-Cabrero v Contents Part I Introduction 1 An Introduction to Uncertainty in the Development of Computational Models of Biological Processes . . . . . . . . . . . . . 3 Liesbet Geris and David Gomez-Cabrero Part II Modeling Establishment Under Uncertainty 2 Reverse Engineering Under Uncertainty . . . . . . . . . . . . . . . . . . . 15 Paul Kirk, Daniel Silk and Michael P.H. Stumpf 3 Probabilistic Computational Causal Discovery for Systems Biology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Vincenzo Lagani, Sofia Triantafillou, Gordon Ball, Jesper Tegnér and Ioannis Tsamardinos 4 Stochastic Modeling and Simulation Methods for Biological Processes: Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 Annelies Lejon and Giovanni Samaey Part III Model Selection and Parameter Fitting 5 The Experimental Side of Parameter Estimation . . . . . . . . . . . . . 127 Monica Schliemann-Bullinger, Dirk Fey, Thierry Bastogne, Rolf Findeisen, Peter Scheurich and Eric Bullinger vii viii Contents 6 Statistical Data Analysis and Modeling . . . . . . . . . . . . . . . . . . . . 155 Millie Shah, Zeinab Chitforoushzadeh and Kevin A. Janes 7 Optimization in Biology Parameter Estimation and the Associated Optimization Problem . . . . . . . . . . . . . . . . . . 177 Gunnar Cedersund, Oscar Samuelsson, Gordon Ball, Jesper Tegnér and David Gomez-Cabrero 8 Interval Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Warwick Tucker 9 Model Extension and Model Selection . . . . . . . . . . . . . . . . . . . . . 213 Mikael Sunnåker and Joerg Stelling 10 Bayesian Model Selection Methods and Their Application to Biological ODE Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 Sabine Hug, Daniel Schmidl, Wei Bo Li, Matthias B. Greiter and Fabian J. Theis Part IV Sensitivity Analysis and Model Adaptation 11 Sloppiness and the Geometry of Parameter Space . . . . . . . . . . . . 271 Brian K. Mannakee, Aaron P. Ragsdale, Mark K. Transtrum and Ryan N. Gutenkunst 12 Modeling and Model Simplification to Facilitate Biological Insights and Predictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 Olivia Eriksson and Jesper Tegnér 13 Sensitivity Analysis by Design of Experiments . . . . . . . . . . . . . . . 327 An Van Schepdael, Aurélie Carlier and Liesbet Geris 14 Waves in Spatially-Disordered Neural Fields: A Case Study in Uncertainty Quantification . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Carlo R. Laing 15 In-Silico Models of Trabecular Bone: A Sensitivity Analysis Perspective. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393 Marlène Mengoni, Sebastien Sikora, Vinciane d’Otreppe, Ruth Karen Wilcox and Alison Claire Jones Contents ix Part V Model Predictions Under Uncertainty 16 Neuroswarm: A Methodology to Explore the Constraints that Function Imposes on Simulation Parameters in Large-Scale Networks of Biological Neurons . . . . . . . . . . . . . . 427 David Gomez-Cabrero, Salva Ardid, Maria Cano-Colino, Jesper Tegnér and Albert Compte 17 Prediction Uncertainty Estimation Despite Unidentifiability: An Overview of Recent Developments . . . . . . . . . . . . . . . . . . . . . 449 Gunnar Cedersund 18 Computational Modeling Under Uncertainty: Challenges and Opportunities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467 David Gomez-Cabrero, Jesper Tegnér and Liesbet Geris Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 477 Part I Introduction