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Modeling Intracellu...
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Nair, Anu G.KTH,Beräkningsbiologi, CB,Department of Computational Biology. Royal Institute of Technology
(författare)
Modeling Intracellular Signaling Underlying Striatal Function in Health and Disease
- Artikel/kapitelEngelska2014
Förlag, utgivningsår, omfång ...
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Amsterdam :Elsevier,2014
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printrdacarrier
Nummerbeteckningar
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LIBRIS-ID:oai:DiVA.org:kth-143341
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https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143341URI
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https://doi.org/10.1016/B978-0-12-397897-4.00013-9DOI
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http://kipublications.ki.se/Default.aspx?queryparsed=id:128737551URI
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https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-104014URI
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https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-245792URI
Kompletterande språkuppgifter
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Språk:engelska
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Sammanfattning på:engelska
Ingår i deldatabas
Klassifikation
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Ämneskategori:ref swepub-contenttype
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Ämneskategori:kap swepub-publicationtype
Serie
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Progress in Molecular Biology and Translational Science,1878-0814 ;123
Anmärkningar
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QC 20140325
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AuthorCount:6;
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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.
Ämnesord och genrebeteckningar
Biuppslag (personer, institutioner, konferenser, titlar ...)
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Gutierrez-Arenas, OmarUppsala universitet,KTH,Beräkningsbiologi, CB,Centrum för forsknings- och bioetik,Department of Computational Biology. Royal Institute of Technology(Swepub:uu)guaom255
(författare)
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Eriksson, OliviaStockholms universitet,Numerisk analys och datalogi (NADA)(Swepub:su)olier
(författare)
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Jauhiainen, AlexandraDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm
(författare)
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Blackwell, Kim T.Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
(författare)
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Hällgren Kotaleski, JeanetteKarolinska Institutet,KTH,Beräkningsbiologi, CB(Swepub:kth)u13pnwqn
(författare)
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KTHBeräkningsbiologi, CB
(creator_code:org_t)
Sammanhörande titlar
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Ingår i:Computational NeuroscienceAmsterdam : Elsevier123, s. 277-3049780123978974
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Ingår i:Progress in molecular biology and translational scienceAmsterdam : Elsevier123, s. 277-3041878-0814
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