The use of automated parameter searches to improve ion channel kinetics for neural modeling
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The voltage and time dependence of ion channels can be regulated, notably by phosphorylation, interaction with phospholipids, and binding to auxiliary subunits. Many parameter variation studies have set conductance densities free while leaving kinetic channel properties fixed as the experimental constraints on the latter are usually better than on the former. Because individual cells can tightly regulate their ion channel properties, we suggest that kinetic parameters may be profitably set free during model optimization in order to both improve matches to data and refine kinetic parameters. To this end, we analyzed the parameter optimization of reduced models of three electrophysiologically characterized and morphologically reconstructed globus pallidus neurons. We performed two automated searches with different types of free parameters. First, conductance density parameters were set free. Even the best resulting models exhibited unavoidable problems which were due to limitations in our channel kinetics. We next set channel kinetics free for the optimized density matches and obtained significantly improved model performance. Some kinetic parameters consistently shifted to similar new values in multiple runs across three models, suggesting the possibility for tailored improvements to channel models. These results suggest that optimized channel kinetics can improve model matches to experimental voltage traces, particularly for channels characterized under different experimental conditions than recorded data to be matched by a model. The resulting shifts in channel kinetics from the original template provide valuable guidance for future experimental efforts to determine the detailed kinetics of channel isoforms and possible modulated states in particular types of neurons.
- Achard, P., & De Schutter, E. (2006). Complex parameter landscape for a complex neuron model. PLoS Computational Biology, 2, 794–804. CrossRef
- Allen, M., Heinzmann, A., Noguchi, E., Abecasis, G., Broxholme, J., Ponting, C. P., et al. (2003). Positional cloning of a novel gene influencing asthma from chromosome 2q14. Nature Genetics, 35, 258–263. CrossRef
- Bar-Yehuda, D., & Korngreen, A. (2008). Space-clamp problems when voltage clamping neurons expressing voltage-gated conductances. Journal of Neurophysiology, 99, 1127–1136. CrossRef
- Bush, K., Knight, J., & Anderson, C. (2005). Optimizing conductance parameters of cortical neural models via electrotonic partitions. Neural Networks, 18, 488–496. CrossRef
- Clerc, M., & Kennedy, J. (2002). The particle swarm—Explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6, 58–73. CrossRef
- Colbert, C. M., & Johnston, D. (1996). Axonal action-potential initiation and Na+ channel densities in the soma and axon initial segment of subicular pyramidal neurons. The Journal of Neuroscience, 16, 6676–6686.
- Davison, A. P., Feng, J. F., & Brown, D. (2000). A reduced compartmental model of the mitral cell for use in network models of the olfactory bulb. Brain Research Bulletin, 51, 393–399. CrossRef
- Druckmann, S., Berger, T. K., Hill, S., Schurmann, F., Markram, H., & Segev, I. (2008). Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data. Biological Cybernetics, 99, 371–379. CrossRef
- Gentet, L. J., Stuart, G. J., & Clements, J. D. (2000). Direct measurement of specific membrane capacitance in neurons. Biophysical Journal, 79, 314–320. CrossRef
- Gerken, W. C., Purvis, L. K., & Butera, R. J. (2006). Genetic algorithm for optimization and specification of a neuron model. Neurocomputing, 69, 1039–1042. CrossRef
- Grieco, T. M., Afshari, F. S., & Raman, I. M. (2002). A role for phosphorylation in the maintenance of resurgent sodium current in cerebellar Purkinje neurons. The Journal of Neuroscience, 22, 3100–3107.
- Gunay, C., Edgerton, J. R., & Jaeger, D. (2008). Channel density distributions explain spiking variability in the globus pallidus: a combined physiology and computer simulation database approach. The Journal of Neuroscience, 28, 7476–7491. CrossRef
- Hanson, J. E., Smith, Y., & Jaeger, D. (2004). Sodium channels and dendritic spike initiation at excitatory synapses in globus pallidus neurons. The Journal of Neuroscience, 24, 329–340. CrossRef
- Hendrickson, E. B., Edgerton, J. R., & Jaeger, D. (2010). The capabilities and limitations of conductance-based compartmental neuron models with reduced branched or unbranched morphologies and active dendrites. Journal of Computational Neuroscience, Online First™, 10 July 2010.
- Herzog, R. I., Liu, C. J., Waxman, S. G., & Cummins, T. R. (2003). Calmodulin binds to the C terminus of sodium channels Na(v)1.4 and Na(v)1.6 and differentially modulates their functional properties. The Journal of Neuroscience, 23, 8261–8270.
- Hoffman, D. A., & Johnston, D. (1998). Downregulation of transient K+ channels in dendrites of hippocampal CA1 pyramidal neurons by activation of PKA and PKC. The Journal of Neuroscience, 18, 3521–3528.
- Jerng, H. H., Qian, Y., & Pfaffinger, P. J. (2004). Modulation of Kv4.2 channel expression and gating by dipeptidyl peptidase 10 (DPP10). Biophysical Journal, 87, 2380–2396. CrossRef
- Keren, N., Peled, N., & Korngreen, A. (2005). Constraining compartmental models using multiple voltage recordings and genetic algorithms. Journal of Neurophysiology, 94, 3730–3742. CrossRef
- Kole, M. H. P., Ilschner, S. U., Kampa, B. M., Williams, S. R., Ruben, P. C., & Stuart, G. J. (2008). Action potential generation requires a high sodium channel density in the axon initial segment. Nature Neuroscience, 11, 178–186. CrossRef
- McCormick, D. A., Shu, Y. S., & Yu, Y. G. (2007). Hodgkin and Huxley model—still standing? Nature, 445, E1–E2. CrossRef
- Mercer, J. N., Chan, C. S., Tkatch, T., Held, J., & Surmeier, D. J. (2007). Nav1.6 sodium channels are critical to pacemaking and fast spiking in globus pallidus neurons. The Journal of Neuroscience, 27, 13552–13566. CrossRef
- Myers, J. L., & Well, A. D. (2003). Research design and statistical analysis (2nd ed.). Mahwah: Lawrence Erlbaum Associates, Inc.
- Newton, A. C. (1995). Protein-kinase-c—structure, function, and regulation. The Journal of Biological Chemistry, 270, 28495–28498. CrossRef
- Oltedal, L., Veruki, M. L., & Hartveit, E. (2009). Passive membrane properties and electrotonic signal processing in retinal rod bipolar cells. Journal of Physiology, London, 587, 829–849. CrossRef
- Park, K. S., Mohapatra, D. P., Misonou, H., & Trimmer, J. S. (2006). Graded regulation of the Kv2.1 potassium channel by variable phosphorylation. Science, 313, 976–979. CrossRef
- Park, K. S., Yang, J. W., Seikel, E., & Trimmer, J. S. (2008). Potassium channel phosphorylation in excitable cells: providing dynamic functional variability to a diverse family of ion channels. Physiology, 23, 49–57. CrossRef
- Prinz, A. A., Billimoria, C. P., & Marder, E. (2003). Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. Journal of Neurophysiology, 90, 3998–4015. CrossRef
- Rossie, S. (1999). Regulation of voltage-sensitive sodium and calcium channels by phosphorylation. In Ion channel regulation (pp. 23–48).
- Roth, A., & Hausser, M. (2001). Compartmental models of rat cerebellar Purkinje cells based on simultaneous somatic and dendritic patch-clamp recordings. Journal of Physiology, London, 535, 445–472. CrossRef
- Routh, B. N., Johnston, D., Harris, K., & Chitwood, R. A. (2009). Anatomical and electrophysiological comparison of CA1 pyramidal neurons of the rat and mouse. Journal of Neurophysiology, 102, 2288–2302. CrossRef
- Rush, A. M., Wittmack, E. K., Tyrrell, L., Black, J. A., Dib-Hajj, S. D., & Waxman, S. G. (2006). Differential modulation of sodium channel Na(v)1.6 by two members of the fibroblast growth factor homologous factor 2 subfamily. The European Journal of Neuroscience, 23, 2551–2562. CrossRef
- Rusnak, F., & Mertz, P. (2000). Calcineurin: form and function. Physiological Reviews, 80, 1483–1521.
- Schulz, D. J., Goaillard, J. M., & Marder, E. (2006). Variable channel expression in identified single and electrically coupled neurons in different animals. Nature Neuroscience, 9, 356–362. CrossRef
- Stuart, G., & Spruston, N. (1998). Determinants of voltage attenuation in neocortical pyramidal neuron dendrites. The Journal of Neuroscience, 18, 3501–3510.
- Taylor, A. L., Goaillard, J. M., & Marder, E. (2009). How multiple conductances determine electrophysiological properties in a multicompartment model. The Journal of Neuroscience, 29, 5573–5586. CrossRef
- Tien, J. H., & Guckenheimer, J. (2008). Parameter estimation for bursting neural models. Journal of Computational Neuroscience, 24, 358–373. CrossRef
- Tkatch, T., Baranauskas, G., & Surmeier, D. J. (2000). Kv4.2 mRNA abundance and A-type K+ current amplitude are linearly related in basal ganglia and basal forebrain neurons. The Journal of Neuroscience, 20, 579–588.
- Van Geit, W., De Schutter, E., & Achard, P. (2008). Automated neuron model optimization techniques: a review. Biological Cybernetics, 99, 241–251. CrossRef
- Vanier, M. C., & Bower, J. M. (1999). A comparative survey of automated parameter-search methods for compartmental neural models. Journal of Computational Neuroscience, 7, 149–171. CrossRef
- Weaver, C. M., & Wearne, S. L. (2006). The role of action potential shape and parameter constraints in optimization of compartment models. Neurocomputing, 69, 1053–1057. CrossRef
- The use of automated parameter searches to improve ion channel kinetics for neural modeling
Journal of Computational Neuroscience
Volume 31, Issue 2 , pp 329-346
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