Computational Systems Neurobiology

ISBN-13: 9789400738577 / Angielski / Twarda / 2012 / 572 str.

Nicolas Le Nov Re

Opis

Computational neurosciences and systems biology are among the main domains of life science research where mathematical modeling made a difference. This book introduces the many different types of computational studies one can develop to study neuronal systems. It is aimed at undergraduate students starting their research in computational neurobiology or more senior researchers who would like, or need, to move towards computational approaches. Based on their specific project, the readers would then move to one of the more specialized excellent textbooks available in the field. The first part of the book deals with molecular systems biology. Functional genomics is introduced through examples of transcriptomics and proteomics studies of neurobiological interest. Quantitative modelling of biochemical systems is presented in homogeneous compartments and using spatial descriptions. A second part deals with the various approaches to model single neuron physiology, and naturally moves to neuronal networks. A division is focused on the development of neurons and neuronal systems and the book closes on a series of methodological chapters. From the molecules to the organ, thinking at the level of systems is transforming biology and its impact on society. This book will help the reader to hop on the train directly in the tank engine.

Recenzje

From the book reviews:

"This is an excellent reference on mathematical neurobiology at the single level, multiple level, and biophysical level. ... This is a good neural modeling book which brings to light ideas concerning Bayes Theorems and NeuroML ensembles. I highly recommend it." (Joseph J. Grenier, Amazon.com, July, 2014)

Spis treści

Foreword, Nicolas Le Novère.- 1 Functional genomics and molecular networks Gene expression regulations in complex diseases: Down syndrome as a case study, Marie-Claude Potier, Isabelle Rivals.-  2 Reconstructing Models from Proteomics Data, Lysimachos Zografos, Andrew J Pocklington, J Douglas Armstrong.- 3 Using chemical kinetics to model neuronal signalling pathways, Lukas Endler, Melanie I Stefan, Stuart J Edelstein, Nicolas Le Novère.- 4 Breakdown of mass-action laws in biochemical computation, Fidel Santamaria, Gabriela Antunes, Erik De Schutter.-  5 Spatial Organization and Diffusion in Neuronal Signaling, Sherry-Ann Brown, Raquell M. Holmes, and Leslie M. Loew.- 6 The Performance (and Limits) of Simple Neuron Models: Generalizations of the Leaky Integrate-and-Fire Model, Richard Naud, Wulfram Gerstner.- 7 Multi-compartmental models of neurons, Upinder S. Bhalla.- 8 Noise in neurons and other constraints, A. Aldo Faisal.- 9 Methodological issues in modelling at multiple levels of description, Kevin Gurney, Mark Humphries.- 10 Virtues, pitfalls, and methodology of neuronal network modeling and simulations on supercomputers, Anders Lansner, Markus Diesmann.- 11 Co-operative populations of neurons: Mean field models of mesoscopic brain activity, David T.J. Liley, Brett L. Foster, Ingo Bojak.- 12 Cellular spacing: analysis and modelling of retinal mosaics, Stephen J. Eglen.- 13Measuring and Modeling Morphology: How Dendrites Take Shape, Todd A. Gillette, Giorgio A. Ascoli.- 14 Axonal growth and targeting, Duncan Mortimer, Hugh D. Simpson, Geoffrey J. Goodhill.- 15 Encoding neuronal models in SBML, Sarah M. Keating, Nicolas Le Novère.- 16 NeuroML, Padraig Gleeson, Volker Steuber, R. Angus Silver, Sharon Crook.- 17 XPPAUT, Bard Ermentrout.- 18: NEST by example: an introduction to the neural simulation tool NEST, Marc-Oliver Gewaltig and Abigail Morrison, Hans Ekkehard Plesser.- Index