Modeling in Computational Biology and Biomedicine: A Multidisciplinary Endeavor (Hardcover) (ISBN-13: 9783642312076)

Foreword by Olivier Faugeras.- Foreword by Joël Janin.- Preface.- Part I Bioinformatics.- 1.Modeling Macro-molecular Complexes: a Journey Across Scales. F.Cazals, T.Dreyfus, and C.H. Robert.- 1.1.Introduction.- 1.2.Modeling Atomic Resolution.- 1.3.Modeling Large Assemblies.- 1.4.Outlook.- 1.5.Online Resources.- References.- 2.Modeling and Analysis of Gene Regulatory Networks. G.Bernot, J-P.Comet, A.Richard, M.Chaves, J-L.Gouzé, and F.Dayan.- 2.1.Introduction.- 2.2.Continuous and Hybrid Models of Genetic Regulatory Networks.- 2.3.Discrete Models of GRN.- 2.4.Outlook.- 2.5.Online Resources.- 2.6.Acknowledgments.- References.- Part II Biomedical Signal and Image Analysis.- 3.Noninvasive Cardiac Signal Analysis Using Data Decomposition Techniques. V.Zarzoso, O.Meste, P.Comon, D.G.Latcu, and N.Saoudi.- 3.1.Preliminaries and Motivation.- 3.2.T-Wave Alternans Detection via Principal Component Analysis.- 3.3.Atrial Activity Extraction via Independent Component Analysis.- 3.4.Conclusion and Outlook.- 3.5.Online Resources.- References.- 4.Deconvolution and Denoising for Confocal Microscopy. P.Pankajakshan, G.Engler, L.Blanc-Féraud, and J.Zerubia.- 4.1.Introduction.- 4.2.Development of the Auxiliary Computational Lens.- 4.3.Outlook.- 4.4.Selected Online Resources.- References.- 5.Statistical Shape Analysis of Surfaces in Medical Images Applied to the Tetralogy of Fallot Heart. K.McLeod, T.Mansi, M.Sermesant, G.Pongiglione, and X.Pennec.- 5.1.Introduction.- 5.2.Statistical Shape Analysis.- 5.3.Shape Analysis of ToF Data.- 5.4.Conclusion.- 5.5.Online Resources.- References.- 6.From Diffusion MRI to Brain Connectomics. A.Ghosh and R.Deriche.- 6.1.Introduction.- 6.2.A Brief History of NMR and MRI.- 6.3.Nuclear Magnetic Resonance and Diffusion.- 6.4.From Diffusion MRI to Tissue Microstructure.- 6.5.Computational Framework for Processing Diffusion MR Images.- 6.6.Tractography: Inferring the Connectivity.- 6.7.Clinical Applications 6.8.Conclusion.- 6.9.Online Resources.- References.- Part III Modeling in neuroscience.- 7.Single-Trial Analysis of Bioelectromagnetic Signals: The Quest for Hidden Information. M.Clerc, T.Papadopoulo, and C.Bénar.- 7.1.Introduction.- 7.2.Data-driven Approaches: Non-linear Dimensionality Reduction.- 7.3.Model-Driven Approaches: Matching Pursuit and its Extensions.- 7.4.Success Stories.- 7.5.Conclusion.- 7.6.Selected Online Resources.- References.- 8 Spike Train Statistics from Empirical Facts to Theory: The Case of the Retina. B.Cessac and A.Palacios.- 8.1.Introduction.- 8.2.Unraveling the Neural Code in the Retina via Spike Train Statistics Analysis.- 8.3.Spike Train Statistics from a Theoretical Perspective.- 8.4.Using Gibbs Distributions to Analysing Spike Trains Statistics.- 8.5.Conclusion.- 8.6.Outlook.- 8.7.Online Resources.- References.- Biology, Medicine and Biophysics.- Mathematics and Computer Science.- Index.