Modeling Intracellular Signaling Underlying Striatal Function in Health and Disease
2014 (English)In: Computational Neuroscience / [ed] Blackwell, K.T., Elsevier, 2014, Vol. 123, 277-304 p.Chapter in book (Refereed)
Striatum, which is the input nucleus of the basal ganglia, integrates cortical and thalamic glutamatergic inputs with dopaminergic afferents from the substantia nigra pars cornpacta. The combination of dopamine and glutamate strongly modulates molecular and cellular properties of striatal neurons and the strength of corticostriatal synapses. These actions are performed via intracellular signaling networks, containing several intertwined feedback loops. Understanding the role of dopamine and other neuromodulators requires the development of quantitative dynamical models for describing the intracellular signaling, in order to provide precise unambiguous descriptions and quantitative predictions. Building such models requires integration of data from multiple data sources containing information regarding the molecular interactions, the strength of these interactions, and the subcellular localization of the molecules. Due to the uncertainty, variability, and sparseness of these data, parameter estimation techniques are critical for inferring or constraining the unknown parameters, and sensitivity analysis evaluates which parameters are most critical for a given observed macroscopic behavior. Here, we briefly review the modeling approaches and tools that have been used to investigate biochemical signaling in the striatum, along with some of the models built around striatum. We also suggest a future direction for the development of such models from the, now becoming abundant, high-throughput data.
Place, publisher, year, edition, pages
Elsevier, 2014. Vol. 123, 277-304 p.
, Progress in Molecular Biology and Translational Science, ISSN 1877-1173 ; 123
DARPP32, Dopamine, Kinetic models, LTP, Medium spiny neurons, Neural data integration, Postsynaptic signaling, Striatum, Synaptic plasticity
Neurosciences Bioinformatics (Computational Biology)
IdentifiersURN: urn:nbn:se:kth:diva-143341DOI: 10.1016/B978-0-12-397897-4.00013-9ISI: 000333380100012PubMedID: 24560149ScopusID: 2-s2.0-84964315450ISBN: 978-0-12-397897-4OAI: oai:DiVA.org:kth-143341DiVA: diva2:706293
FunderSwedish e‐Science Research CenterSwedish Research Council, 2010-3149, 2010-4429NIH (National Institute of Health)
QC 201403252014-03-192014-03-192014-05-23Bibliographically approved