Change search
Link to record
Permanent link

Direct link
BETA
Hellgren Kotaleski, JeanetteORCID iD iconorcid.org/0000-0002-0550-0739
Alternative names
Publications (10 of 136) Show all publications
Blackwell, K. T., Salinas, A. G., Tewatia, P., English, B., Hellgren Kotaleski, J. & Lovinger, D. M. (2019). Molecular mechanisms underlying striatal synaptic plasticity: relevance chronic alcohol consumption and seeking. European Journal of Neuroscience, 49(6), 768-783
Open this publication in new window or tab >>Molecular mechanisms underlying striatal synaptic plasticity: relevance chronic alcohol consumption and seeking
Show others...
2019 (English)In: European Journal of Neuroscience, ISSN 0953-816X, E-ISSN 1460-9568, Vol. 49, no 6, p. 768-783Article in journal (Refereed) Published
Abstract [en]

The striatum, the input structure of the basal ganglia, is a major site learning and memory for goal-directed actions and habit formation. iny projection neurons of the striatum integrate cortical, thalamic, d nigral inputs to learn associations, with cortico-striatal synaptic asticity as a learning mechanism. Signaling molecules implicated in naptic plasticity are altered in alcohol withdrawal, which may ntribute to overly strong learning and increased alcohol seeking and nsumption. To understand how interactions among signaling molecules oduce synaptic plasticity, we implemented a mechanistic model of gnaling pathways activated by dopamine D1 receptors, acetylcholine ceptors, and glutamate. We use our novel, computationally efficient mulator, NeuroRD, to simulate stochastic interactions both within and tween dendritic spines. Dopamine release during theta burst and 20-Hz imulation was extrapolated from fast-scan cyclic voltammetry data llected in mouse striatal slices. Our results show that the combined tivity of several key plasticity molecules correctly predicts the currence of either LTP, LTD, or no plasticity for numerous perimental protocols. To investigate spatial interactions, we imulate two spines, either adjacent or separated on a 20-mu m ndritic segment. Our results show that molecules underlying LTP hibit spatial specificity, whereas 2-arachidonoylglycerol exhibits a atially diffuse elevation. We also implement changes in NMDA ceptors, adenylyl cyclase, and G protein signaling that have been asured following chronic alcohol treatment. Simulations under these nditions suggest that the molecular changes can predict changes in naptic plasticity, thereby accounting for some aspects of alcohol use sorder.

Place, publisher, year, edition, pages
Wiley, 2019
Keywords
basal ganglia, computational model, long-term depression, long-term potentiation, signaling pathways, striatum
National Category
Neurosciences
Identifiers
urn:nbn:se:kth:diva-248357 (URN)10.1111/ejn.13919 (DOI)000461876600003 ()29602186 (PubMedID)2-s2.0-85051146502 (Scopus ID)
Note

QC 20190405

Available from: 2019-04-05 Created: 2019-04-05 Last updated: 2019-04-05Bibliographically approved
Suryanarayana, S. M., Hellgren Kotaleski, J., Grillner, S. & Gurney, K. N. (2019). Roles for globus pallidus externa revealed in a computational model of action selection in the basal ganglia. Neural Networks, 109, 113-136
Open this publication in new window or tab >>Roles for globus pallidus externa revealed in a computational model of action selection in the basal ganglia
2019 (English)In: Neural Networks, ISSN 0893-6080, E-ISSN 1879-2782, Vol. 109, p. 113-136Article in journal (Refereed) Published
Abstract [en]

The basal ganglia are considered vital to action selection - a hypothesis supported by several biologically plausible computational models. Of the several subnuclei of the basal ganglia, the globus pallidus externa (GPe) has been thought of largely as a relay nucleus, and its intrinsic connectivity has not been incorporated in significant detail, in any model thus far. Here, we incorporate newly revealed subgroups of neurons within the GPe into an existing computational model of the basal ganglia, and investigate their role in action selection. Three main results ensued. First, using previously used metrics for selection, the new extended connectivity improved the action selection performance of the model. Second, low frequency theta oscillations were observed in the subpopulation of the GPe (the TA or 'arkypallidal' neurons) which project exclusively to the striatum. These oscillations were suppressed by increased dopamine activity - revealing a possible link with symptoms of Parkinson's disease. Third, a new phenomenon was observed in which the usual monotonic relationship between input to the basal ganglia and its output within an action 'channel' was, under some circumstances, reversed. Thus, at high levels of input, further increase of this input to the channel could cause an increase of the corresponding output rather than the more usually observed decrease. Moreover, this phenomenon was associated with the prevention of multiple channel selection, thereby assisting in optimal action selection. Examination of the mechanistic origin of our results showed the so-called 'prototypical' GPe neurons to be the principal subpopulation influencing action selection. They control the striatum via the arkypallidal neurons and are also able to regulate the output nuclei directly. Taken together, our results highlight the role of the GPe as a major control hub of the basal ganglia, and provide a mechanistic account for its control function.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD, 2019
Keywords
Action selection, Network models, Globus pallidus externa, Arkypallidal GPe neurons, Prototypical GPe neurons
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-239966 (URN)10.1016/j.neunet.2018.10.003 (DOI)000451027500012 ()30414556 (PubMedID)2-s2.0-85056160521 (Scopus ID)
Funder
Swedish Research CouncilSwedish e‐Science Research Center
Note

QC 20181211

Available from: 2018-12-11 Created: 2018-12-11 Last updated: 2018-12-11Bibliographically approved
Nair, A. G., Castro, L. R. V., El Khoury, M., Gorgievski, V., Giros, B., Tzavara, E. T., . . . Vincent, P. (2019). The high efficacy of muscarinic M4 receptor in D1 medium spiny neurons reverses striatal hyperdopaminergia. Neuropharmacology, 146, 74-83
Open this publication in new window or tab >>The high efficacy of muscarinic M4 receptor in D1 medium spiny neurons reverses striatal hyperdopaminergia
Show others...
2019 (English)In: Neuropharmacology, ISSN 0028-3908, E-ISSN 1873-7064, Vol. 146, p. 74-83Article in journal (Refereed) Published
Abstract [en]

The opposing action of dopamine and acetylcholine has long been known to play an important role in basal ganglia physiology. However, the quantitative analysis of dopamine and acetylcholine signal interaction has been difficult to perform in the native context because the striatum comprises mainly two subtypes of medium-sized spiny neurons (MSNs) on which these neuromodulators exert different actions. We used biosensor imaging in live brain slices of dorsomedial striatum to monitor changes in intracellular cAMP at the level of individual MSNs. We observed that the muscarinic agonist oxotremorine decreases cAMP selectively in the MSN sub population that also expresses D-1 dopamine receptors, an action mediated by the M-4 muscarinic receptor. This receptor has a high efficacy on cAMP signaling and can shut down the positive cAMP response induced by dopamine, at acetylcholine concentrations which are consistent with physiological levels. This supports our prediction based on theoretical modeling that acetylcholine could exert a tonic inhibition on striatal cAMP signaling, thus supporting the possibility that a pause in acetylcholine release is required for phasic dopamine to transduce a cAMP signal in D1 MSNs. In vivo experiments with acetylcholinesterase inhibitors donepezil and tacrine, as well as with the positive allosteric modulators of M-4 receptor VU0152100 and VU0010010 show that this effect is sufficient to reverse the increased locomotor activity of DAT-knockout mice. This suggests that M-4 receptors could be a novel therapeutic target to treat hyperactivity disorders.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD, 2019
Keywords
Acetylcholine, Dopamine, Biosensor imaging, Cyclic AMP, Striatum, Muscarinic receptors, M4 receptor
National Category
Neurosciences
Identifiers
urn:nbn:se:kth:diva-244501 (URN)10.1016/j.neuropharm.2018.11.029 (DOI)000457663900008 ()30468798 (PubMedID)2-s2.0-85057234306 (Scopus ID)
Note

QC 20190328

Available from: 2019-03-28 Created: 2019-03-28 Last updated: 2019-05-22Bibliographically approved
Eriksson, O., Jauhiainen, A., Sasane, S. M., Kramer, A., Nair, A. G., Sartorius, C. & Hellgren Kotaleski, J. (2019). Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models. Bioinformatics, 35(2), 284-292
Open this publication in new window or tab >>Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models
Show others...
2019 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 35, no 2, p. 284-292Article in journal (Refereed) Published
Abstract [en]

Motivation: Dynamical models describing intracellular phenomena are increasing in size and complexity as more information is obtained from experiments. These models are often over-parameterized with respect to the quantitative data used for parameter estimation, resulting in uncertainty in the individual parameter estimates as well as in the predictions made from the model. Here we combine Bayesian analysis with global sensitivity analysis (GSA) in order to give better informed predictions; to point out weaker parts of the model that are important targets for further experiments, as well as to give guidance on parameters that are essential in distinguishing different qualitative output behaviours. Results: We used approximate Bayesian computation (ABC) to estimate the model parameters from experimental data, as well as to quantify the uncertainty in this estimation (inverse uncertainty quantification), resulting in a posterior distribution for the parameters. This parameter uncertainty was next propagated to a corresponding uncertainty in the predictions (forward uncertainty propagation), and a GSA was performed on the predictions using the posterior distribution as the possible values for the parameters. This methodology was applied on a relatively large model relevant for synaptic plasticity, using experimental data from several sources. We could hereby point out those parameters that by themselves have the largest contribution to the uncertainty of the prediction as well as identify parameters important to separate between qualitatively different predictions. This approach is useful both for experimental design as well as model building.

Place, publisher, year, edition, pages
Oxford University Press, 2019
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-245950 (URN)10.1093/bioinformatics/bty607 (DOI)000459314900013 ()30010712 (PubMedID)2-s2.0-85060038208 (Scopus ID)
Note

QC 20190313

Available from: 2019-03-13 Created: 2019-03-13 Last updated: 2019-03-13Bibliographically approved
Yapo, C., Nair, A. G., Hellgren Kotaleski, J., Vincent, P. & Castro, L. R. V. (2018). Switch-like PKA responses in the nucleus of striatal neurons. Journal of Cell Science, 131(14), Article ID UNSP jcs216556.
Open this publication in new window or tab >>Switch-like PKA responses in the nucleus of striatal neurons
Show others...
2018 (English)In: Journal of Cell Science, ISSN 0021-9533, E-ISSN 1477-9137, Vol. 131, no 14, article id UNSP jcs216556Article in journal (Refereed) Published
Abstract [en]

Although it is known that protein kinase A (PKA) in the nucleus regulates gene expression, the specificities of nuclear PKA signaling remain poorly understood. Here, we combined computational modeling and live-cell imaging of PKA-dependent phosphorylation in mouse brain slices to investigate how transient dopamine signals are translated into nuclear PKA activity in cortical pyramidal neurons and striatal medium spiny neurons. We observed that the nuclear PKA signal in striatal neurons featured an ultrasensitive responsiveness, associated with fast all-or-none responses, which is not consistent with the commonly accepted theory of a slow and passive diffusion of catalytic PKA in the nucleus. Our numerical model suggests that a positive feed-forward mechanism inhibiting nuclear phosphatase activity - possibly mediated by DARPP-32 (also known as PPP1R1B) - could be responsible for this non-linear pattern of nuclear PKA response, allowing for a better detection of the transient dopamine signals that are often associated with reward-mediated learning.

Place, publisher, year, edition, pages
Company of Biologists Ltd, 2018
Keywords
Protein kinase A, Biosensor imaging, Modeling, Nucleus, Signal integration
National Category
Cell Biology
Identifiers
urn:nbn:se:kth:diva-235141 (URN)10.1242/jcs.216556 (DOI)000443435600009 ()29967033 (PubMedID)2-s2.0-85050756664 (Scopus ID)
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceEU, Horizon 2020, 720270
Note

QC 20180920

Available from: 2018-09-20 Created: 2018-09-20 Last updated: 2018-09-20Bibliographically approved
Balbi, P., Massobrio, P. & Hellgren Kotaleski, J. (2017). A single Markov-type kinetic model accounting for the macroscopic currents of all human voltage-gated sodium channel isoforms. PloS Computational Biology, 13(9), Article ID e1005737.
Open this publication in new window or tab >>A single Markov-type kinetic model accounting for the macroscopic currents of all human voltage-gated sodium channel isoforms
2017 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 13, no 9, article id e1005737Article in journal (Refereed) Published
Abstract [en]

Modelling ionic channels represents a fundamental step towards developing biologically detailed neuron models. Until recently, the voltage-gated ion channels have been mainly modelled according to the formalism introduced by the seminal works of Hodgkin and Huxley (HH). However, following the continuing achievements in the biophysical and molecular comprehension of these pore-forming transmembrane proteins, the HH formalism turned out to carry limitations and inconsistencies in reproducing the ion-channels electrophysiological behaviour. At the same time, Markov-type kinetic models have been increasingly proven to successfully replicate both the electrophysiological and biophysical features of different ion channels. However, in order to model even the finest non-conducting molecular conformational change, they are often equipped with a considerable number of states and related transitions, which make them computationally heavy and less suitable for implementation in conductance-based neurons and large networks of those. In this purely modelling study we develop a Markov-type kinetic model for all human voltage-gated sodium channels (VGSCs). The model framework is detailed, unifying (i.e., it accounts for all ion-channel isoforms) and computationally efficient (i.e. with a minimal set of states and transitions). The electrophysiological data to be modelled are gathered from previously published studies on whole-cell patch-clamp experiments in mammalian cell lines heterologously expressing the human VGSC subtypes (from NaV1.1 to NaV1.9). By adopting a minimum sequence of states, and using the same state diagram for all the distinct isoforms, the model ensures the lightest computational load when used in neuron models and neural networks of increasing complexity. The transitions between the states are described by original ordinary differential equations, which represent the rate of the state transitions as a function of voltage (i.e., membrane potential). The kinetic model, developed in the NEURON simulation environment, appears to be the simplest and most parsimonious way for a detailed phenomenological description of the human VGSCs electrophysiological behaviour.

Place, publisher, year, edition, pages
Public Library of Science, 2017
Keywords
isoprotein, voltage gated sodium channel, biological model, biology, cell line, chemistry, computer simulation, human, kinetics, Markov chain, metabolism, physiology, procedures, Computational Biology, Humans, Markov Chains, Models, Neurological, Protein Isoforms, Voltage-Gated Sodium Channels
National Category
Bioinformatics (Computational Biology) Biophysics
Identifiers
urn:nbn:se:kth:diva-216195 (URN)10.1371/journal.pcbi.1005737 (DOI)000411981000029 ()2-s2.0-85030469258 (Scopus ID)
Note

QC 20171220

Available from: 2017-12-20 Created: 2017-12-20 Last updated: 2018-01-13Bibliographically approved
Du, K., Wu, Y.-W. -., Lindroos, R., Liu, Y., Rózsa, B., Katona, G., . . . Hellgren Kotaleski, J. (2017). Cell-type–specific inhibition of the dendritic plateau potential in striatal spiny projection neurons. Proceedings of the National Academy of Sciences of the United States of America, 114(36), E7612-E7621
Open this publication in new window or tab >>Cell-type–specific inhibition of the dendritic plateau potential in striatal spiny projection neurons
Show others...
2017 (English)In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 114, no 36, p. E7612-E7621Article in journal (Refereed) Published
Abstract [en]

Striatal spiny projection neurons (SPNs) receive convergent excitatory synaptic inputs from the cortex and thalamus. Activation of spatially clustered and temporally synchronized excitatory inputs at the distal dendrites could trigger plateau potentials in SPNs. Such supralinear synaptic integration is crucial for dendritic computation. However, how plateau potentials interact with subsequent excitatory and inhibitory synaptic inputs remains unknown. By combining computational simulation, two-photon imaging, optogenetics, and dual-color uncaging of glutamate and GABA, we demonstrate that plateau potentials can broaden the spatiotemporal window for integrating excitatory inputs and promote spiking. The temporal window of spiking can be delicately controlled by GABAergic inhibition in a cell-type–specific manner. This subtle inhibitory control of plateau potential depends on the location and kinetics of the GABAergic inputs and is achieved by the balance between relief and reestablishment of NMDA receptor Mg2+ block. These findings represent a mechanism for controlling spatiotemporal synaptic integration in SPNs.

Place, publisher, year, edition, pages
National Academy of Sciences, 2017
Keywords
Dendritic computation, Inhibition, Plateau potential, Striatum, Synaptic integration, 4 aminobutyric acid, glutamic acid, magnesium ion, n methyl dextro aspartic acid receptor, adult, animal cell, animal tissue, Article, cell specificity, cell synchronization, dendrite, female, GABAergic system, male, mouse, nerve cell, nerve cell excitability, nonhuman, optogenetics, priority journal, simulation, spike, striatal spiny projection neuron, synaptic transmission
National Category
Neurosciences
Identifiers
urn:nbn:se:kth:diva-218816 (URN)10.1073/pnas.1704893114 (DOI)000409182200023 ()28827326 (PubMedID)2-s2.0-85029221172 (Scopus ID)
Funder
Swedish e‐Science Research CenterSwedish Research CouncilEU, Horizon 2020, 720270
Note

QC 20180117

Available from: 2018-01-17 Created: 2018-01-17 Last updated: 2019-02-08Bibliographically approved
Bahuguna, J., Tetzlaff, T., Kumar, A., Hellgren Kotaleski, J. & Morrison, A. (2017). Homologous Basal Ganglia Network Models in Physiological and Parkinsonian Conditions. Frontiers in Computational Neuroscience, 11, Article ID 79.
Open this publication in new window or tab >>Homologous Basal Ganglia Network Models in Physiological and Parkinsonian Conditions
Show others...
2017 (English)In: Frontiers in Computational Neuroscience, ISSN 1662-5188, E-ISSN 1662-5188, Vol. 11, article id 79Article in journal (Refereed) Published
Abstract [en]

The classical model of basal ganglia has been refined in recent years with discoveries of subpopulations within a nucleus and previously unknown projections. One such discovery is the presence of subpopulations of arkypallidal and prototypical neurons in external globus pallidus, which was previously considered to be a primarily homogeneous nucleus. Developing a computational model of these multiple interconnected nuclei is challenging, because the strengths of the connections are largely unknown. We therefore use a genetic algorithm to search for the unknown connectivity parameters in a firing rate model. We apply a binary cost function derived from empirical firing rate and phase relationship data for the physiological and Parkinsonian conditions. Our approach generates ensembles of over 1,000 configurations, or homologies, for each condition, with broad distributions for many of the parameter values and overlap between the two conditions. However, the resulting effective weights of connections from or to prototypical and arkypallidal neurons are consistent with the experimental data. We investigate the significance of the weight variability by manipulating the parameters individually and cumulatively, and conclude that the correlation observed between the parameters is necessary for generating the dynamics of the two conditions. We then investigate the response of the networks to a transient cortical stimulus, and demonstrate that networks classified as physiological effectively suppress activity in the internal globus pallidus, and are not susceptible to oscillations, whereas parkinsonian networks show the opposite tendency. Thus, we conclude that the rates and phase relationships observed in the globus pallidus are predictive of experimentally observed higher level dynamical features of the physiological and parkinsonian basal ganglia, and that the multiplicity of solutions generated by our method may well be indicative of a natural diversity in basal ganglia networks. We propose that our approach of generating and analyzing an ensemble of multiple solutions to an underdetermined network model provides greater confidence in its predictions than those derived from a unique solution, and that projecting such homologous networks on a lower dimensional space of sensibly chosen dynamical features gives a better chance than a purely structural analysis at understanding complex pathologies such as Parkinson's disease.

Place, publisher, year, edition, pages
FRONTIERS MEDIA SA, 2017
Keywords
basal ganglia, network models, degeneracy, oscillations, Parkinson's disease
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-214328 (URN)10.3389/fncom.2017.00079 (DOI)000408054600001 ()2-s2.0-85031997295 (Scopus ID)
Note

QC 20170914

Available from: 2017-09-14 Created: 2017-09-14 Last updated: 2018-01-13Bibliographically approved
Belic, J., Kumar, A. & Hellgren Kotaleski, J. (2017). Interactions in the Striatal Network with Different Oscillation Frequencies. In: Artificial Neural Networks and Machine Learning – ICANN. Lecture Notes in Computer Science: . Paper presented at Artificial Neural Networks and Machine Learning – ICANN 2017. (pp. 129-136). Springer, 10613
Open this publication in new window or tab >>Interactions in the Striatal Network with Different Oscillation Frequencies
2017 (English)In: Artificial Neural Networks and Machine Learning – ICANN. Lecture Notes in Computer Science, Springer, 2017, Vol. 10613, p. 129-136Conference paper, Published paper (Refereed)
Abstract [en]

Simultaneous oscillations in different frequency bands are implicated in the striatum, and understanding their interactions will bring us one step closer to restoring the spectral characteristics of striatal activity that correspond to the healthy state. We constructed a computational model of the striatum in order to investigate how different, simultaneously present, and externally induced oscillations propagate through striatal circuitry and which stimulation parameters have a significant contribution. Our results show that features of these oscillations and their interactions can be influenced via amplitude, input frequencies, and the phase offset between different external inputs. Our findings provide further untangling of the oscillatory activity that can be seen within the striatal network.

Place, publisher, year, edition, pages
Springer, 2017
Keywords
Corticostriatal network, Network oscillations, GABAergic transmission, Basal ganglia, Cortex
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-217110 (URN)10.1007/978-3-319-68600-4_16 (DOI)2-s2.0-85034229782 (Scopus ID)978-3-319-68599-1 (ISBN)
Conference
Artificial Neural Networks and Machine Learning – ICANN 2017.
Note

QC 20171101

Available from: 2017-10-31 Created: 2017-10-31 Last updated: 2018-02-09Bibliographically approved
Brocke, E., Djurfeldt, M., Bhalla, U. S., Hellgren Kotaleski, J. & Hanke, M. (2017). Multirate method for co-simulation of electrical-chemical systems in multiscale modeling. Journal of Computational Neuroscience, 42(3), 245-256
Open this publication in new window or tab >>Multirate method for co-simulation of electrical-chemical systems in multiscale modeling
Show others...
2017 (English)In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 42, no 3, p. 245-256Article in journal (Refereed) Published
Abstract [en]

Multiscale modeling by means of co-simulation is a powerful tool to address many vital questions in neuroscience. It can for example be applied in the study of the process of learning and memory formation in the brain. At the same time the co-simulation technique makes it possible to take advantage of interoperability between existing tools and multi-physics models as well as distributed computing. However, the theoretical basis for multiscale modeling is not sufficiently understood. There is, for example, a need of efficient and accurate numerical methods for time integration. When time constants of model components are different by several orders of magnitude, individual dynamics and mathematical definitions of each component all together impose stability, accuracy and efficiency challenges for the time integrator. Following our numerical investigations in Brocke et al. (Frontiers in Computational Neuroscience, 10, 97, 2016), we present a new multirate algorithm that allows us to handle each component of a large system with a step size appropriate to its time scale. We take care of error estimates in a recursive manner allowing individual components to follow their discretization time course while keeping numerical error within acceptable bounds. The method is developed with an ultimate goal of minimizing the communication between the components. Thus it is especially suitable for co-simulations. Our preliminary results support our confidence that the multirate approach can be used in the class of problems we are interested in. We show that the dynamics ofa communication signal as well as an appropriate choice of the discretization order between system components may have a significant impact on the accuracy of the coupled simulation. Although, the ideas presented in the paper have only been tested on a single model, it is likely that they can be applied to other problems without loss of generality. We believe that this work may significantly contribute to the establishment of a firm theoretical basis and to the development of an efficient computational framework for multiscale modeling and simulations.

Place, publisher, year, edition, pages
Springer-Verlag New York, 2017
Keywords
Adaptive time step integration, Backward differentiation formula, Co-simulation, Coupled integration, Coupled system, Multirate integration, Multiscale modeling, Multiscale simulation, Parallel numerical integration
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-207312 (URN)10.1007/s10827-017-0639-7 (DOI)000400077500003 ()2-s2.0-85017136818 (Scopus ID)
Funder
EU, FP7, Seventh Framework Programme, 604102EU, Horizon 2020, 720270Swedish Research CouncilSwedish e‐Science Research CenterScience for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20170609

Available from: 2017-06-09 Created: 2017-06-09 Last updated: 2018-01-13Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0550-0739

Search in DiVA

Show all publications