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The impact of the distribution of isoforms on CaMKII activation
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0002-2358-7815
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0002-0550-0739
2006 (English)In: Neurocomputing, ISSN 0925-2312, Vol. 69, no 10-12, 1010-1013 p.Article in journal (Refereed) Published
Abstract [en]

We have developed a computational model of the regulation of alpha- and beta-CaMKII activity, in order to examine (i) the importance of neighbour subunit interactions and (ii) the effect the higher CaMCa4 affinity of beta-CaMKII has on the holoenzyme activity in different configurations with the same alpha: beta ratio. The model consists of a deterministic biochemical network coupled to stochastic activation of CaMKII The results suggest that CaMKII holoenzyme activity is non-linear and dependent on the holoenzyme configuration of isoforms. This is especially pronounced in situations with a high-dephosphorylation rate of CaMKII.

Place, publisher, year, edition, pages
2006. Vol. 69, no 10-12, 1010-1013 p.
Keyword [en]
CaMKII; Plasticity; Computer modelling; Stochastic model
National Category
Information Science
Identifiers
URN: urn:nbn:se:kth:diva-7227DOI: 10.1016/j.neucom.2005.12.035ISI: 000237873900004Scopus ID: 2-s2.0-33646520327OAI: oai:DiVA.org:kth-7227DiVA: diva2:12174
Note
Hjorth, Johannes: Lic (Manuskript) QC 20100723Available from: 2007-05-30 Created: 2007-05-30 Last updated: 2012-01-08Bibliographically approved
In thesis
1. Early Information Processing in the Vertebrate Olfactory System: A Computational Study
Open this publication in new window or tab >>Early Information Processing in the Vertebrate Olfactory System: A Computational Study
2007 (English)Licentiate thesis, comprehensive summary (Other scientific)
Abstract [en]

The olfactory system is believed to be the oldest sensory system. It developed to detect and analyse chemical information in the form of odours, and its organisation follows the same principles in almost all living animals - insects as well as mammals. Likely, the similarities are due to parallel evolution - the same type of organisation has arisen more than once. Therefore, the olfactory system is often assumed to be close to optimally designed for its tasks. Paradoxically, the workings of the olfactory system are not yet well known, although several milestone discoveries have been made during the last decades. The most well-known is probably the disovery of the olfactory receptor gene family, announced in 1991 by Linda Buck and Richard Axel. For this and subsequent work, they were awarded a Nobel Prize Award in 2004. This achievement has been of immense value for both experimentalists and theorists, and forms the basis of the current understanding of olfaction. The olfactory system has long been a focus for scientific interest, both experimental and theoretical. Ever since the field of computational neuroscience was founded, the functions of the olfactory system have been investigated through computational modelling. In this thesis, I present the basis of a biologically realistic model of the olfactory system. Our goal is to be able to represent the whole olfactory system. We are not there yet, but we have some of the necessary building blocks; a model of the input from the olfactory receptor neuron population and a model of the olfactory bulb. Taking into account the reported variability of geometrical, electrical and receptor-dependent neuronal characteristics, we have been able to model the frequency response of a population of olfactory receptor neurons. By constructing several olfactory bulb models of different size, we have shown that the size of the bulb network has an impact on its ability to process noisy information. We have also, through biochemical modelling, investigated the behaviour of the enzyme CaMKII which is known to be critical for early olfactory adaptation (suppression of constant odour stimuli).

Abstract [sv]

Luktsystemet anses allmänt vara det äldsta sensoriska systemet. Det utvecklades för att upptäcka och analysera kemisk information i form av lukter, och det är organiserat efter samma principer hos nästan alla djurarter: insekter så väl som däggdjur. Troligen beror likheterna på parallell evolution -- samma organisation har uppstått mer än en gång. Därför antas det ofta att luktsystemet är nära optimalt anpassat för sina arbetsuppgifter. Paradoxalt nog är luktsystemets arbetssätt ännu inte väl känt, även om flera banbrytande framsteg gjorts de senaste decennierna. Det mest välkända är nog upptäckten av genfamiljen av luktreceptorer, som tillkännagavs 1991 av Linda Buck och Rikard Axel. För detta och efterföljande arbete belönades de med Nobelpriset år 2004. Upptäckten har varit mycket värdefull för både experimentalister och teoretiker, och formar grunden för vår nuvarande förståelse av luktsystemet. Luktsystemet har länge varit ett fokus för vetenskapligt intresse, både experimentellt och teoretiskt. Ända sedan fältet beräkningsbiologi grundades har luktsystemet undersökts genom datormodellering. I denna avhandling presenterar jag grunden för en biologiskt realistisk modell av luktsystemet. Vårt mål är att kunna representera hela luktsystemet. Så långt har vi ännu inte nått, men vi har några av de nödvändiga byggstenarna: en modell av signalerna från populationen av luktreceptorceller, och en modell av luktbulben. Genom att ta hänsyn till nervcellernas rapporterade variationer i geometriska, elektriska och receptor-beroende karaktärsdrag har vi lyckats modellera svarsfrekvenserna från en population av luktreceptorceller. Genom att konstruera flera olika stora modeller av luktbulben har vi visat att storleken på luktbulbens cellnätverk påverkar dess förmåga att behandla brusig information. Vi har också, genom biokemisk modellering, undersökt beteendet hos enzymet CaMKII, som är kritiskt viktigt för adaptering (undertryckning av ständigt närvarande luktstimuli) i luktsystemet.

Place, publisher, year, edition, pages
Stockholm: Numerisk analys och datalogi, 2007
Series
Trita-CSC-A, ISSN 1653-5723 ; 2007:8
Keyword
olfaction, olfactory system, olfactory bulb, synchronisation, CaMKII, mathematical modelling
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-4408 (URN)978-91-7178-696-8 (ISBN)
Presentation
2007-06-08, E2, Lindstedtsvägen 3, Stockholm, 15:00
Opponent
Supervisors
Available from: 2007-05-30 Created: 2007-05-30 Last updated: 2012-03-21
2. Computational Modelling of Early Olfactory Processing
Open this publication in new window or tab >>Computational Modelling of Early Olfactory Processing
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Chemical sensing is believed to be the oldest sensory ability. The chemical senses, olfaction and gustation, developed to detect and analyze information in the form of air- or waterborne chemicals, to find food and mates, and to avoid danger. The organization of the olfactory system follows the same principles in almost all living animals, insects as well as mammals. Likely, the similarities are due to parallel evolution – the same type of organisation seems to have arisen more than once. Therefore, the olfactory system is often assumed to be close to optimally designed for its tasks.Paradoxically, the workings of the olfactory system are not yet well known,although several milestone discoveries have been made during the last decades. The most well-known is probably the disovery of the olfactory receptor gene family,announced in 1991 by Linda Buck and Richard Axel. For this and subsequent work, they were awarded a Nobel Prize Award in 2004. This achievement has been of immense value for both experimentalists and theorists, and forms the basis of the current understanding of olfaction. The olfactory system has long been a focus for scientific interest within several fields, both experimental and theoretical, and it has often been used asa model system. And ever since the field of computational neuroscience was founded, the functions of the olfactory system have been investigated through computational modelling. In this thesis, I present several approaches to biologically realistic computational models of parts of the olfactory system, with an emphasis on the earlier stages of the vertebrate olfactory system – olfactory receptor neurons (ORNs) and the olfactory bulb (OB). I have investigated the behaviour of the enzyme CaMKII, which is known to be critical for olfactory adaptation (suppression of constant odour stimuli) in the ORN, using a biochemical model. By constructing several OB models of different size, I have shown that the size of the OB network has an impact on its ability to process noisy information. Taking into account the reported variability of geometrical, electrical and receptor-dependent neuronal characteristics, I have been able to model the frequency response of a population of ORNs. I have used this model to find the key properties that govern most of the ORN population’s response, and investigated some of the possible implications of these key properties in subsequent studies of the ORN population and the OB – what we call the fuzzy concentration coding hypothesis.

Abstract [sv]

Detektion av kemiska ämnen anses allmänt vara den äldsta sensoriska förmågan. De kemiska sinnena, lukt och smak, utvecklades för att upptäcka och analysera kemisk information i form av luft- eller vattenburna ämnen, för att hitta mat och partners, och för att undvika fara. Luktsystemet är organiserat efter samma principer hos nästan alla djurarter, insekter såväl som däggdjur. Troligen beror likheterna på parallell evolution – samma organisation verkar ha uppstått mer än en gång. Därför antas det ofta att luktsystemet är nära optimalt anpassat för sina arbetsuppgifter.Paradoxalt nog är luktsystemets arbetsprinciper ännu inte väl kända, även om flera banbrytande framsteg gjorts de senaste decennierna. Det mest välkända är nog upptäckten av genfamiljen av luktreceptorer, som tillkännagavs 1991 av Linda Buck och Rikard Axel. För detta och efterföljande arbete belönades de med Nobelpriset år 2004. Upptäckten har varit mycket värdefull för både experimentalister och teoretiker, och är grunden för vår nuvarande förståelse av luktsystemet. Luktsystemet har länge varit ett fokus för vetenskapligt intresse inom flera fält, experimentella såväl som teoretiska, och har ofta använts som ett modellsystem. Och ända sedan fältet beräkningsneurobiologi grundades har luktsystemet undersökts genom datormodellering. I denna avhandling presenterar jag flera ansatser till biologiskt realistiskaberäkningsmodeller av luktsystemet, med tonvikt på de tidigare delarna av ryggradsdjurens luktsystem – luktreceptorceller och luktbulben. Jag har undersökt beteendet hos enzymet CaMKII, som anses vara kritiskt viktigt för adaptation (undertryckning av ständigt närvarande luktstimuli) i luktsystemet, i en biokemisk modell. Genom att konstruera flera olika stora modeller av luktbulben har jag visat att storleken på luktbulbens cellnätverk påverkar dess förmåga att behandla brusig information. Genom att ta hänsyn till nervcellernas rapporterade variationer i geometriska, elektriska och receptor-beroende karaktärsdrag har jag lyckats modellera svarsfrekvenserna från en population av luktreceptorceller. Jag har använt denna modell för att hitta de nyckelprinciper som styr huvuddelen av luktreceptorneuron-populationens svar, ochundersökt några av de tänkbara konsekvenserna av dessa nyckelprinciper i efterföljande studier av luktreceptorneuron-populationen och luktbulben – det vi kallar ”fuzzy concentration coding”-hypotesen.

Place, publisher, year, edition, pages
Stockholm: KTH, 2010. xiv, 151 p.
Series
Trita-CSC-A, ISSN 1653-5723 ; 2010:03
Keyword
olfaction, olfactory system, olfactory bulb, olfactory receptor neuron, synchronization, CaMKII, mathematical modelling, lukt, luktsystemet, luktbulben, synkronisering, CaMKII, matematisk modellering
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-12090 (URN)978-91-7415-575-4 (ISBN)
Public defence
2010-03-26, D3, Lindstedtsvägen 5, Stockholm, 13:00 (English)
Opponent
Supervisors
Note
QC20100723Available from: 2010-03-04 Created: 2010-03-03 Last updated: 2010-07-23Bibliographically approved
3. Information processing in the Striatum: a computational study
Open this publication in new window or tab >>Information processing in the Striatum: a computational study
2006 (English)Licentiate thesis, comprehensive summary (Other scientific)
Abstract [en]

The basal ganglia form an important structure centrally placed in the brain. They receive input from motor, associative and limbic areas, and produce output mainly to the thalamus and the brain stem. The basal ganglia have been implied in cognitive and motor functions. One way to understand the basal ganglia is to take a look at the diseases that affect them. Both Parkinson's disease and Huntington's disease with their motor problems are results of malfunctioning basal ganglia. There are also indications that these diseases affect cognitive functions. Drug addiction is another example that involves this structure, which is also important for motivation and selection of behaviour.

In this licentiate thesis I am laying the groundwork for a detailed model of the striatum, which is the input stage of the basal ganglia. The striatum receives glutamatergic input from the cortex and thalamus, as well as dopaminergic input from substantia nigra. The majority of the neurons in the striatum are medium spiny (MS) projection neurons that project mainly to globus pallidus but also to other neurons in the striatum and to both dopamine producing and GABAergic neurons in substantia nigra. In addition to the MS neurons there are fast spiking (FS) interneurons that are in a position to regulate the firing of the MS neurons. These FS neurons are few, but connected into large networks through electrical synapses that could synchronise their effect. By forming strong inhibitory synapses on the MS neurons the FS neurons have a powerful influence on the striatal output. The inhibitory output of the basal ganglia on the thalamus is believed to keep prepared motor commands on hold, but once one of them is disinhibited, then the selected motor command is executed. This disinhibition is initiated in the striatum by the MS neurons.

Both MS and FS neurons are active during so called up-states, which are periods of elevated cortical input to striatum. Here I have studied the FS neurons and their ability to detect such up-states. This is important because FS neurons can delay spikes in MS neurons and the time between up-state onset and the first spike in the MS neurons is correlated with the amount of calcium entering the MS neuron, which in turn might have implications for plasticity and learning of new behaviours. The effect of different combinations of electrical couplings between two FS neurons has been tested, where the location, number and strength of these gap junctions have been varied. I studied both the ability of the FS neurons to fire action potentials during the up-state, and the synchronisation between neighbouring FS neurons due to electrical coupling. I found that both proximal and distal gap junctions synchronised the firing, but the distal gap junctions did not have the same temporal precision. The ability of the FS neurons to detect an up-state was affected by whether the neighbouring FS neuron also received up-state input or not. This effect was more pronounced for distal gap junctions than proximal ones, due to a stronger shunting effect of distal gap junctions when the dendrites were synaptically activated.

We have also performed initial stochastic simulations of the Ca2+-calmodulin-dependent protein kinase II (CaMKII). The purpose here is to build the knowledge as well as the tools necessary for biochemical simulations of intracellular processes that are important for plasticity in the MS neurons. The simulated biochemical pathways will then be integrated into an existing model of a full MS neuron. Another venue to explore is to build striatal network models consisting of MS and FS neurons and using experimental data of the striatal microcircuitry. With these different approaches we will improve our understanding of striatal information processing.

Place, publisher, year, edition, pages
Stockholm: KTH, 2006. ix, 45 p.
Series
Trita-CSC-A, ISSN 1653-5723 ; 2006:8
Keyword
striatum, fast spiking interneuron, gap junctions, synchronisation, up-state detection, CaMKII, mathematical modelling
National Category
Neurosciences
Identifiers
urn:nbn:se:kth:diva-3999 (URN)91-7178-368-7 (ISBN)
Presentation
2006-06-14, E32, Lindstedsvägen 3, Stockholm, 14:00 (English)
Opponent
Supervisors
Note
QC 20101116Available from: 2006-05-29 Created: 2006-05-29 Last updated: 2010-11-16Bibliographically approved

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