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]}