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.
Author(s): Marie-Claude Potier, Isabelle Rivals (auth.), N. Le Novère (eds.)
Edition: 1
Publisher: Springer Netherlands
Year: 2012
Language: English
Pages: 572
Tags: Neurosciences; Bioinformatics
Front Matter....Pages i-viii
Functional Genomics and Molecular Networks Gene Expression Regulations in Complex Diseases: Down Syndrome as a Case Study....Pages 1-22
Reconstructing Models from Proteomics Data....Pages 23-80
Using Chemical Kinetics to Model Neuronal Signalling Pathways....Pages 81-117
Breakdown of Mass-Action Laws in Biochemical Computation....Pages 119-132
Spatial Organization and Diffusion in Neuronal Signaling....Pages 133-161
The Performance (and Limits) of Simple Neuron Models: Generalizations of the Leaky Integrate-and-Fire Model....Pages 163-192
Multi-compartmental Models of Neurons....Pages 193-225
Noise in Neurons and Other Constraints....Pages 227-257
Methodological Issues in Modelling at Multiple Levels of Description....Pages 259-281
Virtues, Pitfalls, and Methodology of Neuronal Network Modeling and Simulations on Supercomputers....Pages 283-315
Co-operative Populations of Neurons: Mean Field Models of Mesoscopic Brain Activity....Pages 317-364
Cellular Spacing: Analysis and Modelling of Retinal Mosaics....Pages 365-385
Measuring and Modeling Morphology: How Dendrites Take Shape....Pages 387-427
Axonal Growth and Targeting....Pages 429-458
Encoding Neuronal Models in SBML....Pages 459-488
NeuroML....Pages 489-517
XPPAUT....Pages 519-531
NEST by Example: An Introduction to the Neural Simulation Tool NEST....Pages 533-558
Back Matter....Pages 559-570
