Biochemically detailed model of LTP and LTD in a cortical spine (Maki-Marttunen et al 2020)

 Download zip file 
Help downloading and running models
Accession:260971
"Signalling pathways leading to post-synaptic plasticity have been examined in many types of experimental studies, but a unified picture on how multiple biochemical pathways collectively shape neocortical plasticity is missing. We built a biochemically detailed model of post-synaptic plasticity describing CaMKII, PKA, and PKC pathways and their contribution to synaptic potentiation or depression. We developed a statistical AMPA-receptor-tetramer model, which permits the estimation of the AMPA-receptor-mediated maximal synaptic conductance based on numbers of GluR1s and GluR2s predicted by the biochemical signalling model. We show that our model reproduces neuromodulator-gated spike-timing-dependent plasticity as observed in the visual cortex and can be fit to data from many cortical areas, uncovering the biochemical contributions of the pathways pinpointed by the underlying experimental studies. Our model explains the dependence of different forms of plasticity on the availability of different proteins and can be used for the study of mental disorder-associated impairments of cortical plasticity."
Reference:
1 . Mäki-Marttunen T, Iannella N, Edwards AG, Einevoll GT, Blackwell KT (2020) A unified computational model for cortical post-synaptic plasticity. Elife [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex spiking regular (RS) neuron;
Channel(s): I Calcium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Glutamate; Norephinephrine; Acetylcholine;
Simulation Environment: NEURON; NeuroRD;
Model Concept(s): Long-term Synaptic Plasticity;
Implementer(s): Maki-Marttunen, Tuomo [tuomomm at uio.no];
Search NeuronDB for information about:  I Calcium; Acetylcholine; Norephinephrine; Glutamate;
/
synaptic
NEURON
fitfiles
README.html
#drawfig3.py#
calcconds.py
calcconds_dimerdimer.py
drawfig11.py
drawfig2.py
drawfig3.py
drawfig3_1.py
drawfig4.py
drawfig5.py
drawfig9abc.py
emoo.py
fit_cAMP_withdiss_1d.py
fits_goodparams.mat
fits_goodparams_manyb.mat
fitter_fewer.py
fitter_fewer_check.py
fitter_fewer_check_given.py *
fitter_fewer1withCK_check_given.py *
fitter_manyb_check_given.py
mesh_general.out *
model_nrn_altered_noU.py
model_nrn_altered_noU_extfilename_lowmem_recall.py
model_nrn_altered_noU_noninterp.py
model_nrn_altered_noU_simpleflux_extfilename_lowmem.py
model_nrn_oldCaM_altered_noU.py
model_nrn_oldCaM_altered_noU_extfilename_lowmem_recall.py
model_nrn_oldPKA_altered_noU.py
model_nrn_paired_contnm_var.py
model_nrn_paired_contnm_var_noninterp.py
model_nrn_testPKA_withdiss.py
model_nrn_testPKA_withdiss_williamson_varyrates.py
mytools.py
protocols_many.py
protocols_many_78withoutCK.py
protocols_many_78withoutCK_1withCK.py
reactionGraph.mat
runfig11.sh
runfig2.sh
runfig3_1.sh
runfig3-4.sh
runfig5.sh
runfig9.sh
simsteadystate_flexible.py
simsteadystate_li2020.py
simsteadystate_oldCaM_li2020.py
                            
TSHORT=5000000
ONSET=4040000

#              (PKC activation rate altered) (S831 phosph. by PKC blocked)   (GluR2 insertion rate)
#              (Fig. 1D-G)                   (Fig. 1J)                       (Fig. 1A)             
ALTEREDS=(      411 411 411 411 411           166-169-181-184-218-221-233-236 385-387-389          )
ALTEREDCOEFFS=( 0.1 0.3 1.0 3.0 10.0          0.0-0.0-0.0-0.0-0.0-0.0-0.0-0.0 22.4-22.4-22.4       )

CAFLUX=1900
GLUFLUX=20.0
ACHFLUX=20.0
LFLUX=10.0
#control 4xHFS, used in Fig. 11A,B,I,J
echo "python model_nrn_altered_noU.py ${TSHORT} 1e-6 $ONSET 100 100 3.0 $CAFLUX $LFLUX $GLUFLUX $ACHFLUX 4 4000 None"
python model_nrn_altered_noU.py ${TSHORT} 1e-6 $ONSET 100 100 3.0 $CAFLUX $LFLUX $GLUFLUX $ACHFLUX 4 4000 None

#Fig. 11A: 4xHFS with altered (old) GluR2 membrane insertion rated
echo "python model_nrn_altered_noU.py ${TSHORT} 1e-6 $ONSET 100 100 3.0 $CAFLUX $LFLUX $GLUFLUX $ACHFLUX 4 4000 None Ca 1.0 ${ALTEREDS[6]} ${ALTEREDCOEFFS[6]}"
python model_nrn_altered_noU.py ${TSHORT} 1e-6 $ONSET 100 100 3.0 $CAFLUX $LFLUX $GLUFLUX $ACHFLUX 4 4000 None  Ca 1.0 ${ALTEREDS[6]} ${ALTEREDCOEFFS[6]}

#Fig. 11B: Old vs. new CaM activation model
echo "python model_nrn_oldCaM_altered_noU.py ${TSHORT} 1e-6 $ONSET 100 100 3.0 $CAFLUX $LFLUX $GLUFLUX $ACHFLUX 4 4000 None CaM 0.55"
python model_nrn_oldCaM_altered_noU.py ${TSHORT} 1e-6 $ONSET 100 100 3.0 $CAFLUX $LFLUX $GLUFLUX $ACHFLUX 4 4000 None CaM 0.55

#Fig. 11C: Old vs. new CaM activation model
echo "python simsteadystate_li2020.py 0.0 50.0 0.0"
python simsteadystate_li2020.py 0.0 50.0 0.0
echo "python simsteadystate_oldCaM_li2020.py 0.0 50.0 0.0"
python simsteadystate_oldCaM_li2020.py 0.0 50.0 0.0

CAFLUX=1900
#Fig. 11D--G: PKCp activation, rate fitted to data from LFS experiments
for ialtered in 0 1 2 3 4
do
  echo "python model_nrn_altered_noU.py ${TSHORT} 1e-6 $ONSET 900 5.0 3.0 $CAFLUX $LFLUX $GLUFLUX $ACHFLUX 1 100000 None Ca 1.0 ${ALTEREDS[ialtered]} ${ALTEREDCOEFFS[ialtered]}"
  python model_nrn_altered_noU.py ${TSHORT} 1e-6 $ONSET 900 5.0 3.0 $CAFLUX $LFLUX $GLUFLUX $ACHFLUX 1 100000 None Ca 1.0 ${ALTEREDS[ialtered]} ${ALTEREDCOEFFS[ialtered]}
done

#Fig. 11H: Fit the PKA-cAMP binding rate
#1) Calculate the PKA activation time series using the original model
echo "python model_nrn_testPKA_withdiss.py 22000 1e-08 800.0 1 1.0 16000.0 0.64"
python model_nrn_testPKA_withdiss.py 22000 1e-08 800.0 1 1.0 16000.0 0.64

#2) Calculate the PKA activation time series using the single-step PKA activation model with different reaction rates and see which fits best
echo "python fit_cAMP_withdiss_1d.py 0.64"
python fit_cAMP_withdiss_1d.py 0.64

#Fig. 11I: Old vs. new PKA activation model
echo "python model_nrn_oldPKA_altered_noU.py ${TSHORT} 1e-6 $ONSET 100 100 3.0 $CAFLUX $LFLUX $GLUFLUX $ACHFLUX 4 4000 None"
python model_nrn_oldPKA_altered_noU.py ${TSHORT} 1e-6 $ONSET 100 100 3.0 $CAFLUX $LFLUX $GLUFLUX $ACHFLUX 4 4000 None

#Fig. 11J: PKC does not phosphorylate S831
echo "python model_nrn_altered_noU.py ${TSHORT} 1e-6 $ONSET 100 100 3.0 $CAFLUX $LFLUX $GLUFLUX $ACHFLUX 4 4000 None Ca 1.0 ${ALTEREDS[5]} ${ALTEREDCOEFFS[5]}"
python model_nrn_altered_noU.py ${TSHORT} 1e-6 $ONSET 100 100 3.0 $CAFLUX $LFLUX $GLUFLUX $ACHFLUX 4 4000 None  Ca 1.0 ${ALTEREDS[5]} ${ALTEREDCOEFFS[5]}


Loading data, please wait...