Computational Systems Neurobiology

Publisher: Springer; 2012 edition (21 July 2012)
Language: English
ISBN-10: 9400738579
ISBN-13: 978-9400738577
DOI:10.1007/978-94-007-3858-4

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General presentation

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 modeling 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 move 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.

Table of content

• Nicolas Le Novère. Forewords.
• Marie-Claude Potier and Isabelle Rivals. Gene expression regulations in complex diseases: Down syndrome as a case study.
• Lysimachos Zografos, Andrew Pocklington, Douglas J Armstrong. Reconstructing models from proteomics data.
• Lukas Endler, Melanie I Stefan, Stuart Edelstein and Nicolas Le Novère. Using chemical kinetics to model neuronal signalling pathways. [PDF]
• Fidel Santamaria, Gabriela Antunes and Erik De Schutter. Breakdown of mass-action laws in biochemical computation.
• Sherry-Ann Brown, Raquell Holmes and Les Loew. Spatial Organization and diffusion in neuronal signalling.
• Richard Naud and Wulfram Gerstner. The performance (and limits) of simple neuron models: Generalizations of the leaky integrate-and-fire model.
• Upinder S Bhalla. Multi-compartmental models of neurons
• A Aldo Faisal. Design principles of neurons from biophysical constraints.
• Kevin Gurney and Mark Humphries. Methodological issues in modelling at multiple levels of description.
• Anders Lansner and Markus Diesmann. Virtues, pitfalls, and methodology related to large-scale neuronal network models and supercomputer simulations.
• David T.J. Liley, Brett L. Foster and Ingo Bojak. Co-operative populations of neurons: Mean field models of mesoscopic brain activity.
• Steven J Eglen. Cellular spacing: analysis and modelling of retinal mosaics.
• Todd A. Gillette and Giorgio A. Ascoli. Measuring and Modeling Morphology: How Dendrites Take Shapes.
• Duncan Mortimer, Hugh Simpson and Geoffrey Goodhill. Axonal growth and targeting.
• Sarah Keating and Nicolas Le Novère. Encoding neuronal models in SBML. [PDF]
• Padraig Gleeson, Volker Steuber, R. Angus Silver and Sharon Crook. NeuroML.
• Bard Ermentrout. XPPAut.
• Mark-Oliver Gewaltig, Abigail Morrison and Hans Ekkehard Plesser. The neural simulation tool NEST.

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