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Lei CL, Clerx M, Gavaghan DJ, Mirams GR. Model-driven optimal experimental design for calibrating cardiac electrophysiology models. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107690. [PMID: 37478675 DOI: 10.1016/j.cmpb.2023.107690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 06/09/2023] [Accepted: 06/22/2023] [Indexed: 07/23/2023]
Abstract
BACKGROUND AND OBJECTIVE Models of the cardiomyocyte action potential have contributed immensely to the understanding of heart function, pathophysiology, and the origin of heart rhythm disturbances. However, action potential models are highly nonlinear, making them difficult to parameterise and limiting to describing 'average cell' dynamics, when cell-specific models would be ideal to uncover inter-cell variability but are too experimentally challenging to be achieved. Here, we focus on automatically designing experimental protocols that allow us to better identify cell-specific maximum conductance values for each major current type. METHODS AND RESULTS We developed an approach that applies optimal experimental designs to patch-clamp experiments, including both voltage-clamp and current-clamp experiments. We assessed the models calibrated to these new optimal designs by comparing them to the models calibrated to some of the commonly used designs in the literature. We showed that optimal designs are not only overall shorter in duration but also able to perform better than many of the existing experiment designs in terms of identifying model parameters and hence model predictive power. CONCLUSIONS For cardiac cellular electrophysiology, this approach will allow researchers to define their hypothesis of the dynamics of the system and automatically design experimental protocols that will result in theoretically optimal designs.
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Affiliation(s)
- Chon Lok Lei
- Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, China; Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Macau, China.
| | - Michael Clerx
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - David J Gavaghan
- Department of Computer Science, University of Oxford, Oxford, United Kingdom; Doctoral Training Centre, University of Oxford, Oxford, United Kingdom
| | - Gary R Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom.
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2
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Deerasooriya Y, Berecki G, Kaplan D, Forster IC, Halgamuge S, Petrou S. Estimating neuronal conductance model parameters using dynamic action potential clamp. J Neurosci Methods 2019; 325:108326. [PMID: 31265869 DOI: 10.1016/j.jneumeth.2019.108326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 06/27/2019] [Accepted: 06/28/2019] [Indexed: 10/26/2022]
Abstract
BACKGROUND Parameterization of neuronal membrane conductance models relies on data acquired from current clamp (CC) or voltage clamp (VC) recordings. Although the CC approach provides key information on a neuron's firing properties, it is often difficult to disentangle the influence of multiple conductances that contribute to the excitation properties of a real neuron. Isolation of a single conductance using pharmacological agents or heterologous expression simplifies analysis but requires extensive VC evaluation to explore the complete state behavior of the channel of interest. NEW METHOD We present an improved parameterization approach that uses data derived from dynamic action potential clamp (DAPC) recordings to extract conductance equation parameters. We demonstrate the utility of the approach by applying it to the standard Hodgkin-Huxley conductance model although other conductance models could be easily incorporated as well. RESULTS Using a fully simulated setup we show that, with as few as five action potentials previously recorded in DAPC mode, sodium conductance equation parameters can be determined with average parameter errors of less than 4% while action potential firing accuracy approaches 100%. In real DAPC experiments, we show that by "training" our model with five or fewer action potentials, subsequent firing lasting for several seconds could be predicted with ˜96% mean firing rate accuracy and 94% temporal overlap accuracy. COMPARISON WITH EXISTING METHODS Our DAPC-based approach surpasses the accuracy of VC-based approaches for extracting conductance equation parameters with a significantly reduced temporal overhead. CONCLUSION DAPC-based approach will facilitate the rapid and systematic characterization of neuronal channelopathies.
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Affiliation(s)
- Y Deerasooriya
- Department of Mechanical Engineering, The University of Melbourne, Parkville, Victoria, Australia
| | - G Berecki
- Ion Channels and Disease Group, The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - D Kaplan
- Ion Channels and Disease Group, The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - I C Forster
- Ion Channels and Disease Group, The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - S Halgamuge
- Department of Mechanical Engineering, The University of Melbourne, Parkville, Victoria, Australia; Research School of Engineering, College of Engineering & Computer Science, The Australian National University, Canberra, Australian Capital Territory, Australia
| | - S Petrou
- Ion Channels and Disease Group, The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia; Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia; ARC Centre for Integrated Brain Function, The University of Melbourne, Parkville, Victoria, Australia.
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3
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Ni H, Morotti S, Grandi E. A Heart for Diversity: Simulating Variability in Cardiac Arrhythmia Research. Front Physiol 2018; 9:958. [PMID: 30079031 PMCID: PMC6062641 DOI: 10.3389/fphys.2018.00958] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Accepted: 06/29/2018] [Indexed: 12/31/2022] Open
Abstract
In cardiac electrophysiology, there exist many sources of inter- and intra-personal variability. These include variability in conditions and environment, and genotypic and molecular diversity, including differences in expression and behavior of ion channels and transporters, which lead to phenotypic diversity (e.g., variable integrated responses at the cell, tissue, and organ levels). These variabilities play an important role in progression of heart disease and arrhythmia syndromes and outcomes of therapeutic interventions. Yet, the traditional in silico framework for investigating cardiac arrhythmias is built upon a parameter/property-averaging approach that typically overlooks the physiological diversity. Inspired by work done in genetics and neuroscience, new modeling frameworks of cardiac electrophysiology have been recently developed that take advantage of modern computational capabilities and approaches, and account for the variance in the biological data they are intended to illuminate. In this review, we outline the recent advances in statistical and computational techniques that take into account physiological variability, and move beyond the traditional cardiac model-building scheme that involves averaging over samples from many individuals in the construction of a highly tuned composite model. We discuss how these advanced methods have harnessed the power of big (simulated) data to study the mechanisms of cardiac arrhythmias, with a special emphasis on atrial fibrillation, and improve the assessment of proarrhythmic risk and drug response. The challenges of using in silico approaches with variability are also addressed and future directions are proposed.
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Affiliation(s)
| | | | - Eleonora Grandi
- Department of Pharmacology, University of California, Davis, Davis, CA, United States
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4
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Beattie KA, Hill AP, Bardenet R, Cui Y, Vandenberg JI, Gavaghan DJ, de Boer TP, Mirams GR. Sinusoidal voltage protocols for rapid characterisation of ion channel kinetics. J Physiol 2018; 596:1813-1828. [PMID: 29573276 PMCID: PMC5978315 DOI: 10.1113/jp275733] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 02/19/2018] [Indexed: 12/21/2022] Open
Abstract
Key points Ion current kinetics are commonly represented by current–voltage relationships, time constant–voltage relationships and subsequently mathematical models fitted to these. These experiments take substantial time, which means they are rarely performed in the same cell. Rather than traditional square‐wave voltage clamps, we fitted a model to the current evoked by a novel sum‐of‐sinusoids voltage clamp that was only 8 s long. Short protocols that can be performed multiple times within a single cell will offer many new opportunities to measure how ion current kinetics are affected by changing conditions. The new model predicts the current under traditional square‐wave protocols well, with better predictions of underlying currents than literature models. The current under a novel physiologically relevant series of action potential clamps is predicted extremely well. The short sinusoidal protocols allow a model to be fully fitted to individual cells, allowing us to examine cell–cell variability in current kinetics for the first time.
Abstract Understanding the roles of ion currents is crucial to predict the action of pharmaceuticals and mutations in different scenarios, and thereby to guide clinical interventions in the heart, brain and other electrophysiological systems. Our ability to predict how ion currents contribute to cellular electrophysiology is in turn critically dependent on our characterisation of ion channel kinetics – the voltage‐dependent rates of transition between open, closed and inactivated channel states. We present a new method for rapidly exploring and characterising ion channel kinetics, applying it to the hERG potassium channel as an example, with the aim of generating a quantitatively predictive representation of the ion current. We fitted a mathematical model to currents evoked by a novel 8 second sinusoidal voltage clamp in CHO cells overexpressing hERG1a. The model was then used to predict over 5 minutes of recordings in the same cell in response to further protocols: a series of traditional square step voltage clamps, and also a novel voltage clamp comprising a collection of physiologically relevant action potentials. We demonstrate that we can make predictive cell‐specific models that outperform the use of averaged data from a number of different cells, and thereby examine which changes in gating are responsible for cell–cell variability in current kinetics. Our technique allows rapid collection of consistent and high quality data, from single cells, and produces more predictive mathematical ion channel models than traditional approaches. Ion current kinetics are commonly represented by current–voltage relationships, time constant–voltage relationships and subsequently mathematical models fitted to these. These experiments take substantial time, which means they are rarely performed in the same cell. Rather than traditional square‐wave voltage clamps, we fitted a model to the current evoked by a novel sum‐of‐sinusoids voltage clamp that was only 8 s long. Short protocols that can be performed multiple times within a single cell will offer many new opportunities to measure how ion current kinetics are affected by changing conditions. The new model predicts the current under traditional square‐wave protocols well, with better predictions of underlying currents than literature models. The current under a novel physiologically relevant series of action potential clamps is predicted extremely well. The short sinusoidal protocols allow a model to be fully fitted to individual cells, allowing us to examine cell–cell variability in current kinetics for the first time.
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Affiliation(s)
- Kylie A Beattie
- Computational Biology, Department of Computer Science, University of Oxford, Oxford, OX1 3QD, UK.,Division of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA
| | - Adam P Hill
- Department of Molecular Cardiology and Biophysics, Victor Chang Cardiac Research Institute, Sydney, NSW, 2010, Australia.,St Vincent's Clinical School, UNSW Sydney, Darlinghurst, NSW, 2010, Australia
| | - Rémi Bardenet
- CNRS & CRIStAL, Université de Lille, 59651 Villeneuve d'Ascq, Lille, France
| | - Yi Cui
- Safety Evaluation and Risk Management, Global Clinical Safety and Pharmacovigilance, GlaxoSmithKline, Uxbridge, UB11 1BS, UK
| | - Jamie I Vandenberg
- Department of Molecular Cardiology and Biophysics, Victor Chang Cardiac Research Institute, Sydney, NSW, 2010, Australia.,St Vincent's Clinical School, UNSW Sydney, Darlinghurst, NSW, 2010, Australia
| | - David J Gavaghan
- Computational Biology, Department of Computer Science, University of Oxford, Oxford, OX1 3QD, UK
| | - Teun P de Boer
- Department of Medical Physiology, Division of Heart & Lungs, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Gary R Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK
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Takkala P, Prescott SA. Using dynamic clamp to quantify pathological changes in the excitability of primary somatosensory neurons. J Physiol 2018; 596:2209-2227. [PMID: 29601637 PMCID: PMC5983269 DOI: 10.1113/jp275580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 03/21/2018] [Indexed: 11/08/2022] Open
Abstract
KEY POINTS Primary somatosensory neurons normally respond to somatic depolarization with transient spiking but can switch to repetitive spiking under pathological conditions. This switch in spiking pattern reflects a qualitative change in spike initiation dynamics and contributes to the hyperexcitability associated with chronic pain. Neurons can be converted to repetitive spiking by adding a virtual conductance using dynamic clamp. By titrating the conductance to determine how much must be added to cause repetitive spiking, we found that small cells are more susceptible to switching (i.e. required less added conductance) than medium-large cells. By measuring how much less conductance is required to cause repetitive spiking when dynamic clamp was combined with other pathomimetic manipulations (e.g. application of inflammatory mediators), we measured how much each manipulation facilitated repetitive spiking. Our results suggest that many pathological factors facilitate repetitive spiking but that the switch to repetitive spiking requires the cumulative effect of many co-occurring factors. ABSTRACT Primary somatosensory neurons become hyperexcitable in many chronic pain conditions. Hyperexcitability can include a switch from transient to repetitive spiking during sustained somatic depolarization. This switch results from diverse pathological processes that impact ion channel expression or function. Because multiple pathological processes co-occur, isolating how much each contributes to switching the spiking pattern is difficult. Our approach to this challenge involves adding a virtual sodium conductance via dynamic clamp. The magnitude of that conductance was titrated to determine the minimum required to enable rheobasic stimulation to evoke repetitive spiking. The minimum required conductance, termed g¯ Na ∗, was re-measured before and during manipulations designed to model various pathological processes in vitro. The reduction in g¯ Na ∗ caused by each pathomimetic manipulation reflects how much the modelled process contributes to switching the spiking pattern. We found that elevating extracellular potassium or applying inflammatory mediators reduced g¯ Na ∗ whereas direct hyperpolarization had no effect. Inflammatory mediators reduced g¯ Na ∗ more in medium-large (>30 μm diameter) neurons than in small (⩽30 μm diameter) neurons, but had equivalent effects in cutaneous and muscle afferents. The repetitive spiking induced by dynamic clamp was also found to differ between small and medium-large neurons, thus revealing latent differences in adaptation. Our study demonstrates a novel way to determine to what extent individual pathological factors facilitate repetitive spiking. Our results suggest that most factors facilitate but do not cause repetitive spiking on their own, and, therefore, that a switch to repetitive spiking results from the cumulative effect of many co-occurring factors.
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Affiliation(s)
- Petri Takkala
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada, M5G 0A4.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada, M5S 1A8
| | - Steven A Prescott
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada, M5G 0A4.,Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada, M5S 1A8.,Department of Physiology and Institute of Biomaterials and Biomedical Engineering, University of Toronto, Ontario, Canada, M5S 1A8
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Gong JQX, Shim JV, Núñez-Acosta E, Sobie EA. I love it when a plan comes together: Insight gained through convergence of competing mathematical models. J Mol Cell Cardiol 2016; 102:31-33. [PMID: 27913283 DOI: 10.1016/j.yjmcc.2016.10.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Accepted: 10/26/2016] [Indexed: 01/01/2023]
Affiliation(s)
- Jingqi Q X Gong
- Department of Pharmacological Sciences, Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jaehee V Shim
- Department of Pharmacological Sciences, Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Elisa Núñez-Acosta
- Department of Pharmacological Sciences, Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eric A Sobie
- Department of Pharmacological Sciences, Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Flourakis M, Kula-Eversole E, Hutchison AL, Han TH, Aranda K, Moose DL, White KP, Dinner AR, Lear BC, Ren D, Diekman CO, Raman IM, Allada R. A Conserved Bicycle Model for Circadian Clock Control of Membrane Excitability. Cell 2016; 162:836-48. [PMID: 26276633 DOI: 10.1016/j.cell.2015.07.036] [Citation(s) in RCA: 148] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Revised: 05/17/2015] [Accepted: 07/06/2015] [Indexed: 01/04/2023]
Abstract
Circadian clocks regulate membrane excitability in master pacemaker neurons to control daily rhythms of sleep and wake. Here, we find that two distinctly timed electrical drives collaborate to impose rhythmicity on Drosophila clock neurons. In the morning, a voltage-independent sodium conductance via the NA/NALCN ion channel depolarizes these neurons. This current is driven by the rhythmic expression of NCA localization factor-1, linking the molecular clock to ion channel function. In the evening, basal potassium currents peak to silence clock neurons. Remarkably, daily antiphase cycles of sodium and potassium currents also drive mouse clock neuron rhythms. Thus, we reveal an evolutionarily ancient strategy for the neural mechanisms that govern daily sleep and wake.
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Affiliation(s)
- Matthieu Flourakis
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | | | - Alan L Hutchison
- Medical Scientist Training Program, James Franck Institute, Department of Chemistry, Institute for Biophysical Dynamics, University of Chicago, Chicago, IL 60637, USA
| | - Tae Hee Han
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Kimberly Aranda
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Devon L Moose
- Department of Biology, University of Iowa, Iowa City, IA 52242, USA
| | - Kevin P White
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA
| | - Aaron R Dinner
- Medical Scientist Training Program, James Franck Institute, Department of Chemistry, Institute for Biophysical Dynamics, University of Chicago, Chicago, IL 60637, USA
| | - Bridget C Lear
- Department of Biology, University of Iowa, Iowa City, IA 52242, USA
| | - Dejian Ren
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Casey O Diekman
- Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Indira M Raman
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Ravi Allada
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA.
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Groenendaal W, Ortega FA, Kherlopian AR, Zygmunt AC, Krogh-Madsen T, Christini DJ. Cell-specific cardiac electrophysiology models. PLoS Comput Biol 2015; 11:e1004242. [PMID: 25928268 PMCID: PMC4415772 DOI: 10.1371/journal.pcbi.1004242] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Accepted: 03/16/2015] [Indexed: 01/25/2023] Open
Abstract
The traditional cardiac model-building paradigm involves constructing a composite model using data collected from many cells. Equations are derived for each relevant cellular component (e.g., ion channel, exchanger) independently. After the equations for all components are combined to form the composite model, a subset of parameters is tuned, often arbitrarily and by hand, until the model output matches a target objective, such as an action potential. Unfortunately, such models often fail to accurately simulate behavior that is dynamically dissimilar (e.g., arrhythmia) to the simple target objective to which the model was fit. In this study, we develop a new approach in which data are collected via a series of complex electrophysiology protocols from single cardiac myocytes and then used to tune model parameters via a parallel fitting method known as a genetic algorithm (GA). The dynamical complexity of the electrophysiological data, which can only be fit by an automated method such as a GA, leads to more accurately parameterized models that can simulate rich cardiac dynamics. The feasibility of the method is first validated computationally, after which it is used to develop models of isolated guinea pig ventricular myocytes that simulate the electrophysiological dynamics significantly better than does a standard guinea pig model. In addition to improving model fidelity generally, this approach can be used to generate a cell-specific model. By so doing, the approach may be useful in applications ranging from studying the implications of cell-to-cell variability to the prediction of intersubject differences in response to pharmacological treatment. Mathematical models of cardiac cell electrophysiology are widely used as predictive and illuminatory tools, but have been developed for decades using a suboptimal process. The models are typically constructed by manual adjustment of parameters to fit simple data and therefore often underperform when used to predict complex behavior such as arrhythmias. We present a novel method of model parameterization using automated optimization and dynamically rich fitting data and then demonstrate that this approach is better at finding the “real” model of a cell. Application of the method to cardiac myocytes leads to cell-specific models, which may enable well-controlled studies of both cellular- and subject-level population heterogeneity in disease propensity and response to therapies.
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Affiliation(s)
- Willemijn Groenendaal
- Greenberg Division of Cardiology, Weill Cornell Medical College, New York, New York, United States of America
| | - Francis A. Ortega
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, United States of America
| | - Armen R. Kherlopian
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, United States of America
| | | | - Trine Krogh-Madsen
- Greenberg Division of Cardiology, Weill Cornell Medical College, New York, New York, United States of America
- Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York, United States of America
| | - David J. Christini
- Greenberg Division of Cardiology, Weill Cornell Medical College, New York, New York, United States of America
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, United States of America
- Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York, United States of America
- * E-mail:
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Meliza CD, Kostuk M, Huang H, Nogaret A, Margoliash D, Abarbanel HDI. Estimating parameters and predicting membrane voltages with conductance-based neuron models. BIOLOGICAL CYBERNETICS 2014; 108:495-516. [PMID: 24962080 DOI: 10.1007/s00422-014-0615-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2013] [Accepted: 06/09/2014] [Indexed: 05/07/2023]
Abstract
Recent results demonstrate techniques for fully quantitative, statistical inference of the dynamics of individual neurons under the Hodgkin-Huxley framework of voltage-gated conductances. Using a variational approximation, this approach has been successfully applied to simulated data from model neurons. Here, we use this method to analyze a population of real neurons recorded in a slice preparation of the zebra finch forebrain nucleus HVC. Our results demonstrate that using only 1,500 ms of voltage recorded while injecting a complex current waveform, we can estimate the values of 12 state variables and 72 parameters in a dynamical model, such that the model accurately predicts the responses of the neuron to novel injected currents. A less complex model produced consistently worse predictions, indicating that the additional currents contribute significantly to the dynamics of these neurons. Preliminary results indicate some differences in the channel complement of the models for different classes of HVC neurons, which accords with expectations from the biology. Whereas the model for each cell is incomplete (representing only the somatic compartment, and likely to be missing classes of channels that the real neurons possess), our approach opens the possibility to investigate in modeling the plausibility of additional classes of channels the cell might possess, thus improving the models over time. These results provide an important foundational basis for building biologically realistic network models, such as the one in HVC that contributes to the process of song production and developmental vocal learning in songbirds.
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Affiliation(s)
- C Daniel Meliza
- Department of Organismal Biology and Anatomy, University of Chicago, 1027 E 57th Street, Chicago, IL, 60637, USA,
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