Computational Systems Neurobiology

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N. Le Novère
Springer Science & Business Media, 20.07.2012 - 572 Seiten
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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.
 

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Inhalt

Chapter 1 Functional Genomics and Molecular Networks Gene Expression Regulations in Complex Diseases Down Syndrome as a Case Study
1
Chapter 2 Reconstructing Models from Proteomics Data
23
Chapter 3 Using Chemical Kinetics to Model Neuronal Signalling Pathways
81
Chapter 4 Breakdown of MassAction Laws in Biochemical Computation
119
Chapter 5 Spatial Organization and Diffusion in Neuronal Signaling
133
Chapter 6 The Performance and Limits of Simple Neuron Models Generalizations of the Leaky IntegrateandFire Model
162
Chapter 7 Multicompartmental Models of Neurons
193
Chapter 8 Noise in Neurons and Other Constraints
227
Chapter 11 Cooperative Populations of Neurons Mean Field Models of Mesoscopic Brain Activity
316
Chapter 12 Cellular Spacing Analysis and Modelling of Retinal Mosaics
365
Chapter 13 Measuring and Modeling Morphology How Dendrites Take Shape
387
Chapter 14 Axonal Growth and Targeting
428
Chapter 15 Encoding Neuronal Models in SBML
459
Chapter 16 NeuroML
489
Chapter 17 XPPAUT
519
Chapter 18 NEST by Example An Introduction to the Neural Simulation Tool NEST
532

Chapter 9 Methodological Issues in Modelling at Multiple Levelsof Description
258
Chapter 10 Virtues Pitfalls and Methodology of Neuronal Network Modeling and Simulations on Supercomputers
283

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