Model: Q10 Temperature Dependence
Demonstrates how ion channel rate constants scale with temperature using the Q10 coefficient: \(k(T) = k(T_{\text{ref}}) \cdot Q_{10}^{(T - T_{\text{ref}})/10}\) .
At network temperature \(T = 22°C\) with \(T_{\text{ref}} = 32°C\) and \(Q_{10} = 3\) :
\[\text{factor} = 3^{(22 - 32)/10} = 3^{-1} \approx 0.333\]
All gating rates are slowed by a factor of 3 compared to reference temperature. The cell is otherwise standard HH with the same conductances as Ex1/Ex5.
1. Define in TVBO
from tvbo import SimulationExperiment# Q10 factor = 3^((22-32)/10) = 1/3 # All standard HH rates multiplied by Q10_factor = 0.3333 # Same conductances as Ex1/Ex5: g_Na=1200nS, g_K=360nS, g_L=3nS, C=10pF = SimulationExperiment.from_string(""" label: "NeuroML Ex10: Q10 Temperature Dependence" dynamics: name: HodgkinHuxley_Q10 iri: neuroml:pointCellCondBased description: > HH cell with Q10 temperature scaling on all gates. Network at 22°C, experimental temp 32°C → Q10 = 3^(-1) ≈ 0.333. parameters: C: { value: 10, unit: pF } v0: { value: -65, unit: mV } thresh: { value: 20, unit: mV } pulse_delay: { value: 100, unit: ms } pulse_duration: { value: 100, unit: ms } I_amp: { value: 0.08, unit: nA } network_temperature: { value: 22, unit: degC } components: passive: name: passive iri: neuroml:ionChannelPassive parameters: conductance: { value: 10, unit: pS } number: { value: 300 } erev: { value: -54.3, unit: mV } na: name: na iri: neuroml:ionChannelHH parameters: conductance: { value: 10, unit: pS } number: { value: 120000 } erev: { value: 50, unit: mV } components: m: name: m iri: neuroml:gateHHrates parameters: instances: { value: 3 } components: q10Settings: name: q10Settings iri: neuroml:q10ExpTemp parameters: q10Factor: { value: 3 } experimentalTemp: { value: 32, unit: degC } forwardRate: name: forwardRate iri: neuroml:HHExpLinearRate parameters: rate: { value: 1, unit: per_ms } midpoint: { value: -40, unit: mV } scale: { value: 10, unit: mV } reverseRate: name: reverseRate iri: neuroml:HHExpRate parameters: rate: { value: 4, unit: per_ms } midpoint: { value: -65, unit: mV } scale: { value: -18, unit: mV } h: name: h iri: neuroml:gateHHrates parameters: instances: { value: 1 } components: q10Settings: name: q10Settings iri: neuroml:q10ExpTemp parameters: q10Factor: { value: 3 } experimentalTemp: { value: 32, unit: degC } forwardRate: name: forwardRate iri: neuroml:HHExpRate parameters: rate: { value: 0.07, unit: per_ms } midpoint: { value: -65, unit: mV } scale: { value: -20, unit: mV } reverseRate: name: reverseRate iri: neuroml:HHSigmoidRate parameters: rate: { value: 1, unit: per_ms } midpoint: { value: -35, unit: mV } scale: { value: 10, unit: mV } k: name: k iri: neuroml:ionChannelHH parameters: conductance: { value: 10, unit: pS } number: { value: 36000 } erev: { value: -77, unit: mV } components: n: name: n iri: neuroml:gateHHrates parameters: instances: { value: 4 } components: q10Settings: name: q10Settings iri: neuroml:q10ExpTemp parameters: q10Factor: { value: 3 } experimentalTemp: { value: 32, unit: degC } forwardRate: name: forwardRate iri: neuroml:HHExpLinearRate parameters: rate: { value: 0.1, unit: per_ms } midpoint: { value: -55, unit: mV } scale: { value: 10, unit: mV } reverseRate: name: reverseRate iri: neuroml:HHExpRate parameters: rate: { value: 0.125, unit: per_ms } midpoint: { value: -65, unit: mV } scale: { value: -80, unit: mV } integration: step_size: 0.01 duration: 300.0 time_scale: ms """ )print (f"Model: { exp. dynamics. name if exp. dynamics else 'network' } " )print (f"Q10 scaling: rates × { 1 / 3 :.4f} " )
Model: HodgkinHuxley_Q10
Q10 scaling: rates × 0.3333
2. Render LEMS XML
= exp.render("lems" )print (xml[:1500 ])
<Lems>
<Target component="sim_NeuroML_Ex10__Q10_Temperature_Dependence"/>
<Include file="Cells.xml"/>
<Include file="Networks.xml"/>
<Include file="Inputs.xml"/>
<Include file="Simulation.xml"/>
<ionChannelHH id="k" conductance="10 pS">
<gateHHrates id="n" instances="4">
<forwardRate type="HHExpLinearRate" midpoint="-55 mV" rate="0.1 per_ms" scale="10 mV"/>
<q10Settings type="q10ExpTemp" experimentalTemp="32 degC" q10Factor="3"/>
<reverseRate type="HHExpRate" midpoint="-65 mV" rate="0.125 per_ms" scale="-80 mV"/>
</gateHHrates>
</ionChannelHH>
<ionChannelHH id="na" conductance="10 pS">
<gateHHrates id="h" instances="1">
<forwardRate type="HHExpRate" midpoint="-65 mV" rate="0.07 per_ms" scale="-20 mV"/>
<q10Settings type="q10ExpTemp" experimentalTemp="32 degC" q10Factor="3"/>
<reverseRate type="HHSigmoidRate" midpoint="-35 mV" rate="1 per_ms" scale="10 mV"/>
</gateHHrates>
<gateHHrates id="m" instances="3">
<forwardRate type="HHExpLinearRate" midpoint="-40 mV" rate="1 per_ms" scale="10 mV"/>
<q10Settings type="q10ExpTemp" experimentalTemp="32 degC" q10Factor="3"/>
<reverseRate type="HHExpRate" midpoint="-65 mV" rate="4 per_ms" scale="-18 mV"/>
</gateHHrates>
</ionChannelHH>
<ionChannelPassive id="passive" conductance="10 pS"/>
<pointCellCondBased id="HodgkinHuxley_Q10" C="10 pF" v0="-
3. Run Reference
from tvbo.adapters.neuroml import run_lems_example= run_lems_example("LEMS_NML2_Ex10_Q10.xml" )for name, arr in ref_outputs.items():print (f" { name} : shape= { arr. shape} " )
hhq10_v.dat: shape=(30001, 2)
4. Run TVBO Version
= exp.run("neuroml" )= result.integration.dataprint (f"TVBO: { da. dims} , shape= { da. shape} " )
TVBO: ('time', 'variable'), shape=(30001, 1)
5. Compare & Plot
from tvbo.adapters.neuroml import plot_lems_comparison"LEMS_NML2_Ex10_Q10.xml" , ref_outputs, result.integration.data, title_prefix= "Ex10" )
