Modeling in Computational Biology and Biomedicine (eBook)

Frédéric Cazals holds a PhD in Theoretical Computer Science from the University of Paris VII. He is Research Director at INRIA Sophia-Antipolis Méditerranée, where he leads the Algorithms-Biology-Structure project-team. His research interests span geometric and topological modeling, scientific software development, and computational structural biology. Pierre Kornprobst holds a PhD in Mathematics from the University of Nice Sophia Antipolis. He is a researcher at INRIA Sophia-Antipolis Méditerranée. His research interest is the study of vision, from computational and biological perspectives, including image processing using partial differential equations, retina modeling, motion perception estimation and categorization.

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.