Stochastic Induction of Long-Term Potentiation and Long-Term Depression

Antunes, Gabriela; Roque, Antônio C.; Simões-de-Souza, Fábio M.

doi:10.1038/srep30899

Cited by 18 publications

(34 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To define the parameters of the model, we used K D s (0.01 µmol.L −1 , 0.1 µmol.L −1 , 1 µmol.L −1 and 10 µmol.L −1 ) commonly found for the interactions between biomolecules 10 , 18 , 38 , 44 , 45 . We defined the kinetic parameters of the model by setting a k f of 1000 µmol −1 .L.s −1 as our upper limit, which is consistent with the second order rate constant of a diffusion limited reaction in the cellular milieu.…”

Section: Methodsmentioning

confidence: 98%

“…Signalling pathways and networks typically show high numbers of cross-talks and redundancies 2 , 5 , 6 , 9 . Often, networks that share mutual components execute opposite cellular responses 10 , 11 . Moreover, common intracellular signals trigger several competing processes 9 , 10 , 12 , 13 .…”

Section: Introductionmentioning

confidence: 99%

“…Often, networks that share mutual components execute opposite cellular responses 10 , 11 . Moreover, common intracellular signals trigger several competing processes 9 , 10 , 12 , 13 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Molecular mechanisms of detection and discrimination of dynamic signals

Antunes

Roque

Simões-de-Souza

2018

Sci Rep

Self Cite

View full text Add to dashboard Cite

Many molecules decode not only the concentration of cellular signals, but also their temporal dynamics. However, little is known about the mechanisms that underlie the detection and discrimination of dynamic signals. We used computational modelling of the interaction of a ligand with multiple targets to investigate how kinetic and thermodynamic parameters regulate their capabilities to respond to dynamic signals. Our results demonstrated that the detection and discrimination of temporal features of signal inputs occur for reactions proceeding outside mass-action equilibrium. For these reactions, thermodynamic parameters such as affinity do not predict their outcomes. Additionally, we showed that, at non-equilibrium, the association rate constants determine the amount of product formed in reversible reactions. In contrast, the dissociation rate constants regulate the time interval required for reversible reactions to achieve equilibrium and, consequently, control their ability to detect and discriminate dynamic features of cellular signals.

show abstract

Section: Methodsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Molecular mechanisms of detection and discrimination of dynamic signals

Antunes

Roque

Simões-de-Souza

2018

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Each run of a stochastic model is generated by drawing random numbers to decide when bonds are made or broken, and when changes in state occur; the variability of the model is obtained by analysing multiple runs. A simple method to simulate chemical reactions accurately is Gillespie’s stochastic simulation algorithm (SSA) [41], as used in some simulations [42].…”

Section: Biophysical Models Of Synaptic Plasticitymentioning

confidence: 99%

“…Antunes et al [42] extend this model by incorporating CaMKII and PP3 to implement LTP. They use the rule-based BioNetGen system to generate stochastic reactions that are simulated using Gillespie’s SSA.…”

Section: Biophysical Models Of Synaptic Plasticitymentioning

confidence: 99%

Analysis of proteins in computational models of synaptic plasticity

Heil

Wysocka

Sorokina

et al. 2018

Preprint

View full text Add to dashboard Cite

The desire to explain how synaptic plasticity arises from interactions between ions, proteins and other signalling molecules has propelled the development of biophysical models of molecular pathways in hippocampal, striatal and cerebellar synapses. The experimental data underpinning such models is typically obtained from low-throughput, hypothesis-driven experiments. We used high-throughput proteomic data and bioinformatics datasets to assess the coverage of biophysical models.To determine which molecules have been modelled, we surveyed biophysical models of synaptic plasticity, identifying which proteins are involved in each model. We were able to map 4.2% of previously reported synaptic proteins to entities in biophysical models. Linking the modelled protein list to Gene Ontology terms shows that modelled proteins are focused on functions such as calmodulin binding, cellular responses to glucagon stimulus, G-alpha signalling and DARPP-32 events.We cross-linked the set of modelled proteins with sets of genes associated with common neurological diseases. We find some examples of disease-associated molecules that are well represented in models, such as voltage-dependent calcium channel family (CACNA1C ), dopamine D1 receptor, and glutamate ionotropic NMDA type 2A and 2B receptors. Many other disease-associated genes have not been included in models of synaptic plasticity, for example catechol-O-methyltransferase (COMT ) and MAOA. By incorporating pathway enrichment results, we identify LAMTOR, a gene uniquely associated with Schizophrenia, which is closely linked to the MAPK pathway found in some models.Our analysis provides a map of how molecular pathways underpinning neurological diseases relate to synaptic biophysical models that can in turn be used to explore how these molecular events might bridge scales into cellular processes and beyond. The map illustrates disease areas where biophysical models have good coverage as well as domain gaps that require significant further research. Author summaryThe 100 billion neurons in the human brain are connected by a billion trillion structures called synapses. Each synapse contains hundreds of different proteins. Some proteins sense the activity of the neurons connecting the synapse. Depending on what they sense, . 28, 2018; the proteins in the synapse are rearranged and new proteins are synthesised. This changes how strongly the synapse influences its target neuron, and underlies learning and memory. Scientists build computational models to reason about the complex interactions between proteins. Here we list the proteins that have been included in computational models to date. For good reasons, models do not always specify proteins precisely, so to make the list we had to translate the names used for proteins in models to gene names, which are used to identify proteins. Our translation could be used to label computational models in the future. We found that the list of modelled proteins contains only 4.2% of proteins associated with synapses, suggesting mor...

show abstract

Stochastic reaction-diffusion modeling of calcium dynamics in 3D dendritic spines of Purkinje cells

Friedhoff

Antunes

Falcke

et al. 2021

Biophysical Journal

View full text Add to dashboard Cite

Calcium (Ca 2þ ) is a second messenger assumed to control changes in synaptic strength in the form of both longterm depression and long-term potentiation at Purkinje cell dendritic spine synapses via inositol trisphosphate (IP 3 )-induced Ca 2þ release. These Ca 2þ transients happen in response to stimuli from parallel fibers (PFs) from granule cells and climbing fibers (CFs) from the inferior olivary nucleus. These events occur at low numbers of free Ca 2þ , requiring stochastic single-particle methods when modeling them. We use the stochastic particle simulation program MCell to simulate Ca 2þ transients within a three-dimensional Purkinje cell dendritic spine. The model spine includes the endoplasmic reticulum, several Ca 2þ transporters, and endogenous buffer molecules. Our simulations successfully reproduce properties of Ca 2þ transients in different dynamical situations. We test two different models of the IP 3 receptor (IP 3 R). The model with nonlinear concentration response of binding of activating Ca 2þ reproduces experimental results better than the model with linear response because of the filtering of noise. Our results also suggest that Ca 2þ -dependent inhibition of the IP 3 R needs to be slow to reproduce experimental results. Simulations suggest the experimentally observed optimal timing window of CF stimuli arises from the relative timing of CF influx of Ca 2þ and IP 3 production sensitizing IP 3 R for Ca 2þ -induced Ca 2þ release. We also model ataxia, a loss of fine motor control assumed to be the result of malfunctioning information transmission at the granule to Purkinje cell synapse, resulting in a decrease or loss of Ca 2þ transients. Finally, we propose possible ways of recovering Ca 2þ transients under ataxia.

show abstract

Stochastic Induction of Long-Term Potentiation and Long-Term Depression

Cited by 18 publications

References 57 publications

Molecular mechanisms of detection and discrimination of dynamic signals

Molecular mechanisms of detection and discrimination of dynamic signals

Analysis of proteins in computational models of synaptic plasticity

Stochastic reaction-diffusion modeling of calcium dynamics in 3D dendritic spines of Purkinje cells

Contact Info

Product

Resources

About