More changes based on discussion with Jan and Lukas

Figures/model_g.py

@@ -124,6 +124,10 @@ def plot_g(ax, df, models, i, let_x, let_y, titlesize=10, letsize=12):
     show_spines(ax, spines='lb')
     ax.text(let_x, let_y, string.ascii_uppercase[i], transform=ax.transAxes, size=letsize, weight='bold')
     ax.set_yscale('log')
+    ax.set_xlim(-0.5, 9)
+    from matplotlib.ticker import ScalarFormatter
+    for axis in [ax.yaxis]:
+        axis.set_major_formatter(ScalarFormatter())
     return ax
 
 

Frontiers_manuscript/For_Submission/Koch_frontiers_diff.tex

@@ -1,7 +1,7 @@
 \documentclass[utf8]{FrontiersinHarvard} 
 %DIF LATEXDIFF DIFFERENCE FILE
 %DIF DEL Koch_frontiers.tex           Mon Mar 27 10:08:50 2023
-%DIF ADD Koch_frontiers_revised.tex   Fri Apr 21 20:28:26 2023
+%DIF ADD Koch_frontiers_revised.tex   Sat Apr 22 11:15:46 2023
 \DeclareUnicodeCharacter{03B2}{\(\beta\)}
 \DeclareUnicodeCharacter{03B1}{\(\alpha\)}
 \DeclareUnicodeCharacter{00C5}{\AA}
@@ -127,7 +127,7 @@ In this study, we therefore investigated how the outcome of ionic current kineti
 All modelling and simulation was done in parallel with custom written Python 3.8  (Python Programming Language; RRID:SCR\_008394) software, run on a Cent-OS 7 server with an Intel(R) Xeon (R) E5-2630 v2 CPU.
 
 \subsection{Different Neuron Models}
-A group of neuronal models representing the major classes of cortical and thalamic neurons including regular spiking pyramidal (RS pyramidal; model D), regular spiking inhibitory (RS inhibitory; model B), and fast spiking (FS; model C) neurons were used \citep{pospischil_minimal_2008}. Additionally,  a \Kv current (\IKv; \citealt{ranjan_kinetic_2019}) was added to each of these models (RS pyramidal +\Kv; model H, RS inhibitory +\Kv; model E, and FS +\Kv; model G respectively). A cerebellar stellate cell model from \citet{alexander_cerebellar_2019} is used (Cb stellate; model A) in this study. This neuron model was also extended by a \Kv current \citep{ranjan_kinetic_2019}, either in addition to the A-type potassium current (Cb stellate +\Kv; model F) or by replacing the A-type potassium current (Cb stellate \(\Delta\)\Kv; model J). A subthalamic nucleus (STN; model L) neuron model as described by \citet{otsuka_conductance-based_2004} was also used. The STN neuron model (model L) was  additionally extended by a \Kv current \citep{ranjan_kinetic_2019}, either in addition to the A-type potassium current (STN +\Kv; model I) or by replacing the A-type potassium current (STN \(\Delta\)\Kv; model K). Model letter naming corresponds to panel lettering in Figure \ref{fig:diversity_in_firing}. The \DIFaddbegin \DIFadd{anatomical origin of each model is shown in Figure \ref{fig:diversity_in_firing}~M. The }\DIFaddend properties and maximal conductances of each model are detailed in Table \ref{tab:g} and the gating properties are unaltered from the original Cb stellate (model A)  and STN (model L) models \citep{alexander_cerebellar_2019, otsuka_conductance-based_2004}. For enabling the comparison of models with the typically reported electrophysiological data fitting reported and for ease of further gating curve manipulations, a modified Boltzmann function
+A group of neuronal models representing the major classes of cortical and thalamic neurons including regular spiking pyramidal (RS pyramidal; model D), regular spiking inhibitory (RS inhibitory; model B), and fast spiking (FS; model C) neurons were used \citep{pospischil_minimal_2008}. Additionally,  a \Kv current (\IKv; \citealt{ranjan_kinetic_2019}) was added to each of these models (RS pyramidal +\Kv; model H, RS inhibitory +\Kv; model E, and FS +\Kv; model G respectively). A cerebellar stellate cell model from \citet{alexander_cerebellar_2019} is used (Cb stellate; model A) in this study. This neuron model was also extended by a \Kv current \citep{ranjan_kinetic_2019}, either in addition to the A-type potassium current (Cb stellate +\Kv; model F) or by replacing the A-type potassium current (Cb stellate \(\Delta\)\Kv; model J). A subthalamic nucleus (STN; model L) neuron model as described by \citet{otsuka_conductance-based_2004} was also used. The STN neuron model (model L) was  additionally extended by a \Kv current \citep{ranjan_kinetic_2019}, either in addition to the A-type potassium current (STN +\Kv; model I) or by replacing the A-type potassium current (STN \(\Delta\)\Kv; model K). Model letter naming corresponds to panel lettering in Figure \ref{fig:diversity_in_firing}. The \DIFaddbegin \DIFadd{anatomical origin of each model is shown in Figure \ref{fig:diversity_in_firing}~M. The }\DIFaddend properties and maximal conductances of each model are detailed in Table \ref{tab:g} and \DIFdelbegin \DIFdel{the }\DIFdelend \DIFaddbegin \DIFadd{depicted in Figure \ref{fig:model_g}. The }\DIFaddend gating properties are unaltered from the original Cb stellate (model A)  and STN (model L) models \citep{alexander_cerebellar_2019, otsuka_conductance-based_2004}. For enabling the comparison of models with the typically reported electrophysiological data fitting reported and for ease of further gating curve manipulations, a modified Boltzmann function
 
 \begin{equation}\label{eqn:Boltz}
 x_\infty = {\left(\frac{1-a}{1+{\exp\left[{\frac{V-V_{1/2}}{k}}\right]}} +a\right)^j} 
@@ -218,11 +218,12 @@ Advances in high-throughput techniques have enabled large-scale investigation in
 
 Diversity across neurons is not limited to gene expression and can also be seen electrophysiologically \citep{Tripathy2017, Gouwens2018, Tripathy2015, Scala2021, Cadwell2020, Gouwens2019, Baden2016, Berens2017} with correlations existing between gene expression and electrophysiological properties \citep{Tripathy2017}. At the ion channel level, diversity exists not only between the specific ion channels the different neuron types express but heterogeneity also exists in ion channel expression levels within neuron types \citep{marder_multiple_2011,  goaillard_ion_2021,barreiro_-current_2012}.  As ion channel properties and expression levels are key \DIFdelbegin \DIFdel{determinents }\DIFdelend \DIFaddbegin \DIFadd{determinants }\DIFaddend of neuronal dynamics and firing \citep{Balachandar2018, Gu2014, Zeberg2015, Aarhem2007, Qi2013, Gu2014a, Zeberg2010, Zhou2020, Kispersky2012} neurons with different ion channel properties and expression levels display different firing properties.
 
-To capture the diversity in neuronal ion channel expression and its relevance in the outcome of ion channel mutations, we used multiple neuronal models with different ionic currents and underlying firing dynamics here.
+To capture the diversity in neuronal ion channel expression and its relevance in the outcome of ion channel mutations, we used multiple neuronal models with different ionic currents \DIFaddbegin \DIFadd{(Figure \ref{fig:model_g}) }\DIFaddend and underlying firing dynamics \DIFaddbegin \DIFadd{(Figure \ref{fig:firing_characterization}) }\DIFaddend here.
 
 \subsection{Ionic Current Environments Determine the Effect of Ion Channel Mutations}
 
-To our knowledge, no comprehensive evaluation of how ionic current environment and neuron type affect the outcome of ion channel mutations have been reported. However, comparisons between the effects of such mutations between certain neuron types were described. For instance, the R1648H mutation in \textit{SCN1A} does not alter the excitability of cortical pyramidal neurons, but causes hypoexcitability of adjacent inhibitory GABAergic neurons \citep{Hedrich14874}. In the CA3 region of the hippocampus, the equivalent mutation in \textit{SCN8A}, R1627H, increases the excitability of pyramidal neurons and decreases the excitability of parvalbumin positive interneurons \cite{makinson_scn1a_2016}. Additionally, the L858H mutation in \(\text{Na}_\text{V}\text{1.7}\), associated with erythermyalgia, has been shown to cause hypoexcitability in sympathetic ganglion neurons and hyperexcitability in dorsal root ganglion neurons \citep{Waxman2007, Rush2006}. The differential effects of L858H \(\text{Na}_\text{V}\text{1.7}\) on firing is dependent on the presence or absence of another sodium channel, namely the \(\text{Na}_\text{V}\text{1.8}\) subunit \citep{Waxman2007, Rush2006}.  These findings, in concert with our findings emphasize that the ionic current environment in which a channelopathy occurs is vital in determining the outcomes of the channelopathy on firing.
+To our knowledge, no comprehensive evaluation of how ionic current environment and neuron type affect the outcome of ion channel mutations have been reported. However, comparisons between the effects of such mutations between certain neuron types were described. For instance, the R1648H mutation in \textit{SCN1A} does not alter the excitability of cortical pyramidal neurons, but causes hypoexcitability of adjacent inhibitory GABAergic neurons \citep{Hedrich14874}. In the CA3 region of the hippocampus, the equivalent mutation in \textit{SCN8A}, R1627H, increases the excitability of pyramidal neurons and decreases the excitability of parvalbumin positive interneurons \cite{makinson_scn1a_2016}. Additionally, the L858H mutation in \(\text{Na}_\text{V}\text{1.7}\), associated with erythermyalgia, has been shown to cause hypoexcitability in sympathetic ganglion neurons and hyperexcitability in dorsal root ganglion neurons \citep{Waxman2007, Rush2006}. The differential effects of L858H \(\text{Na}_\text{V}\text{1.7}\) on firing is dependent on the presence or absence of another sodium channel, namely the \(\text{Na}_\text{V}\text{1.8}\) subunit \citep{Waxman2007, Rush2006}.  These findings, in concert with our findings emphasize that the ionic current environment in which a channelopathy occurs is vital in determining the outcomes of the channelopathy on firing. \DIFaddbegin \DIFadd{However, many differences can exist between neuron types at multiple levels of scale not only in ion channel composition. Despite this complexity, the simulations performed here demonstrate that differential ion channel composition is sufficient to cause neuron type differences in the effects of ion channel mutations.
+}\DIFaddend 
 
 Neuron type specific differences in ionic current properties are important in the effects of ion channel mutations. However, within a neuron type heterogeneity in channel expression levels exists and it is often desirable to generate a population of neuronal models and to screen them for plausibility to biological data in order to capture neuronal population diversity \citep{marder_multiple_2011,OLeary2016}. The models we used here are originally generated by characterization of current gating properties and by fitting of maximal conductances to experimental data \citep{pospischil_minimal_2008, ranjan_kinetic_2019, alexander_cerebellar_2019, otsuka_conductance-based_2004}. This practice of fixing maximal conductances based on experimental data is limiting as it does not reproduce the variability in channel expression and neuronal firing behavior of a heterogeneous neuron population \citep{verma_computational_2020}. For example, a model derived from the mean conductances in a neuronal sub-population within the stomatogastric ganglion, the so-called "one-spike bursting" neurons fire three spikes instead of one per burst due to an L-shaped distribution of sodium and potassium conductances \citep{golowasch_failure_2002}.  Multiple sets of conductances can give rise to the same patterns of activity also termed degeneracy and differences in neuronal dynamics may only be evident with perturbations \citep{marder_multiple_2011, goaillard_ion_2021}. The variability in ion channel expression often correlates with the expression of other ion channels \citep{goaillard_ion_2021} and neurons whose behavior is similar may possess correlated variability across different ion channels resulting in stability in the neuronal phenotype \citep{lamb_correlated_2013, soofi_co-variation_2012, taylor_how_2009}. The variability of ionic currents and degeneracy of neurons may account, at least in part, for the observation that the effect of toxins within a neuronal type is frequently not constant \citep{khaliq_relative_2006, puopolo_roles_2007, ransdell_neurons_2013}.
 
@@ -236,7 +237,7 @@ The effects of changes in channel properties depend in part on the neuronal mode
 
 Therefore, the transfer of LOF or GOF from the current to the firing level should be used with caution; the neuron type in which the mutant ion channel is expressed may provide valuable insight into the functional consequences of an ion channel mutation. Experimental assessment of the effects of a patient's specific ion channel mutation \textit{in vivo} is not generally feasible at a large scale. Therefore, modelling approaches investigating the effects of patient specific channelopathies provide a viable method bridging between characterization of changes in biophysical properties of ionic currents and the firing consequences of these effects. In both experimental and modelling studies on the effects of ion channel mutations on neuronal firing the specific dependency on neuron type should be considered.
 
-\DIFdelbegin \DIFdel{The }\DIFdelend \DIFaddbegin \DIFadd{Our simulations demonstrate that the }\DIFaddend effects of altered ion channel properties on firing is generally influenced by the other ionic currents present in the neuron \DIFaddbegin \DIFadd{as summarized in Figure \ref{fig:summary}}\DIFaddend . In channelopathies the effect of a given ion channel mutation on neuronal firing therefore depends on the neuron type in which those changes occur \citep{Hedrich14874, makinson_scn1a_2016, Waxman2007, Rush2006}. Although certain complexities of neurons such as differences in neuron-type sensitivities to current property changes, interactions between ionic currents, cell morphology and subcellular ion channel distribution are neglected here, it is likely that this increased complexity \textit{in vivo} would contribute to the neuron-type dependent effects on neuronal firing. The complexity and nuances of the nervous system, including neuron-type dependent firing effects of channelopathies explored here, likely underlie shortcomings in treatment approaches in patients with channelopathies. Accounting for neuron-type dependent firing effects provides an opportunity to improve the efficacy and precision in personalized medicine approaches. \DIFaddbegin \DIFadd{Although this is not experimentally feasible, improved modelling and simulations methods to predict neuron-type dependent effects may provide an opportunity to inform therapeutic strategies that are more specific and thus have greater efficacy.
+\DIFdelbegin \DIFdel{The }\DIFdelend \DIFaddbegin \DIFadd{Our simulations demonstrate that the }\DIFaddend effects of altered ion channel properties on firing is generally influenced by the other ionic currents present in the neuron \DIFaddbegin \DIFadd{as illustrated in Figure \ref{fig:summary}}\DIFaddend . In channelopathies the effect of a given ion channel mutation on neuronal firing therefore depends on the neuron type in which those changes occur \citep{Hedrich14874, makinson_scn1a_2016, Waxman2007, Rush2006}. Although certain complexities of neurons such as differences in neuron-type sensitivities to current property changes, interactions between ionic currents, cell morphology and subcellular ion channel distribution are neglected here, it is likely that this increased complexity \textit{in vivo} would contribute to the neuron-type dependent effects on neuronal firing. The complexity and nuances of the nervous system, including neuron-type dependent firing effects of channelopathies explored here, likely underlie shortcomings in treatment approaches in patients with channelopathies. Accounting for neuron-type dependent firing effects provides an opportunity to improve the efficacy and precision in personalized medicine approaches. \DIFaddbegin \DIFadd{Although this is not experimentally feasible, improved modelling and simulations methods to predict neuron-type dependent effects may provide an opportunity to inform therapeutic strategies that are more specific and thus have greater efficacy.
 }\DIFaddend 
 
 With this study we suggest that neuron-type specific effects are vital to a full understanding of the effects of channelopathies at the level of neuronal firing. Furthermore, we highlight the use of modelling approaches to enable relatively fast and efficient insight into channelopathies.
@@ -260,10 +261,19 @@ With this study we suggest that neuron-type specific effects are vital to a full
         \label{fig:diversity_in_firing}
 \end{figure}
 
+\begin{figure}[!ht]
+        \centering
+        \DIFaddbeginFL \includegraphics[width=\linewidth]{model_g.jpg}
+        \linespread{1.}\selectfont
+        \caption[]{\DIFaddFL{Diversity in Neuronal Model Current Composition. Distributions of maximal current conductances (\(\mathrm{g}_{\mathrm{max}}\)) for Cb stellate  }\textbf{\DIFaddFL{(A)}}\DIFaddFL{, RS inhibitory  }\textbf{\DIFaddFL{(B)}}\DIFaddFL{, FS }\textbf{\DIFaddFL{(C)}}\DIFaddFL{, RS pyramidal  }\textbf{\DIFaddFL{(D)}}\DIFaddFL{, RS inhibitory +}\Kv  \textbf{\DIFaddFL{(E)}}\DIFaddFL{, Cb stellate +}\Kv  \textbf{\DIFaddFL{(F)}}\DIFaddFL{, FS +}\Kv  \textbf{\DIFaddFL{(G)}}\DIFaddFL{, RS pyramidal +}\Kv  \textbf{\DIFaddFL{(H)}}\DIFaddFL{, STN +}\Kv  \textbf{\DIFaddFL{(I)}}\DIFaddFL{, Cb stellate \(\Delta\)}\Kv  \textbf{\DIFaddFL{(J)}}\DIFaddFL{, STN \(\Delta\)}\Kv  \textbf{\DIFaddFL{(K)}}\DIFaddFL{, and STN  }\textbf{\DIFaddFL{(L)}} \DIFaddFL{neuron models. Models are sorted as in Figure \ref{fig:diversity_in_firing}.}} 
+        \label{fig:model_g}
+\end{figure}
+
+
 
 \begin{figure}[!ht]
         \centering
-	\includegraphics[width=0.5\linewidth]{firing_characterization_arrows.jpg}
+	\DIFaddendFL \includegraphics[width=0.5\linewidth]{firing_characterization_arrows.jpg}
         \linespread{1.}\selectfont
         \caption[]{Characterization of firing with AUC and rheobase. \textbf{(A)} The area under the curve (AUC) of the repetitive firing frequency-current (fI) curve.  \textbf{(B)}
 Changes in firing as characterized by \(\Delta\)AUC and \(\Delta\)rheobase occupy four quadrants separated by no changes in AUC and rheobase. Representative schematic fI curves in red with respect to a reference (or wild type) fI curve (blue) depict the general changes associated with each quadrant. \DIFaddbeginFL \DIFaddFL{Square root functions are used as fI curves for illustration purposes.}\DIFaddendFL }
@@ -298,7 +308,7 @@ Changes in firing as characterized by \(\Delta\)AUC and \(\Delta\)rheobase occup
         \centering
         \includegraphics[width=\linewidth]{summary_fig.jpg}
         \linespread{1.}\selectfont
-	\caption[]{\DIFaddFL{Summary of neuron type dependence of channelopathies. The wildtype channel (blue) is mutated (red) the effects of it in different neuron types (green, orange and grey) each with a unique set of ion channels determines the effect of firing seen on the right by the shift from the blue wildtype fI curve to the red fI curve for the mutated ion channel.}}
+	\caption[]{\DIFaddFL{Summary of neuron type dependence of channelopathies. The wild type channel (WT, blue) is mutated (Mutant, red) the effects of it in different neuron types (green, orange and grey) each with a unique set of ion channels (see inset axes) determines the effect of firing seen on the right by the shift from the blue wild type fI curve to the red fI curve for the mutated ion channel. Square root functions are used as fI curves for illustration purposes.}}
         \label{fig:summary}
 \DIFaddendFL \end{figure}
 

Frontiers_manuscript/For_Submission/Koch_frontiers_revised.tex

@@ -174,11 +174,11 @@ Advances in high-throughput techniques have enabled large-scale investigation in
 
 Diversity across neurons is not limited to gene expression and can also be seen electrophysiologically \citep{Tripathy2017, Gouwens2018, Tripathy2015, Scala2021, Cadwell2020, Gouwens2019, Baden2016, Berens2017} with correlations existing between gene expression and electrophysiological properties \citep{Tripathy2017}. At the ion channel level, diversity exists not only between the specific ion channels the different neuron types express but heterogeneity also exists in ion channel expression levels within neuron types \citep{marder_multiple_2011,  goaillard_ion_2021,barreiro_-current_2012}.  As ion channel properties and expression levels are key determinants of neuronal dynamics and firing \citep{Balachandar2018, Gu2014, Zeberg2015, Aarhem2007, Qi2013, Gu2014a, Zeberg2010, Zhou2020, Kispersky2012} neurons with different ion channel properties and expression levels display different firing properties.
 
-To capture the diversity in neuronal ion channel expression and its relevance in the outcome of ion channel mutations, we used multiple neuronal models with different ionic currents and underlying firing dynamics here.
+To capture the diversity in neuronal ion channel expression and its relevance in the outcome of ion channel mutations, we used multiple neuronal models with different ionic currents (Figure \ref{fig:model_g}) and underlying firing dynamics (Figure \ref{fig:firing_characterization}) here.
 
 \subsection{Ionic Current Environments Determine the Effect of Ion Channel Mutations}
 
-To our knowledge, no comprehensive evaluation of how ionic current environment and neuron type affect the outcome of ion channel mutations have been reported. However, comparisons between the effects of such mutations between certain neuron types were described. For instance, the R1648H mutation in \textit{SCN1A} does not alter the excitability of cortical pyramidal neurons, but causes hypoexcitability of adjacent inhibitory GABAergic neurons \citep{Hedrich14874}. In the CA3 region of the hippocampus, the equivalent mutation in \textit{SCN8A}, R1627H, increases the excitability of pyramidal neurons and decreases the excitability of parvalbumin positive interneurons \cite{makinson_scn1a_2016}. Additionally, the L858H mutation in \(\text{Na}_\text{V}\text{1.7}\), associated with erythermyalgia, has been shown to cause hypoexcitability in sympathetic ganglion neurons and hyperexcitability in dorsal root ganglion neurons \citep{Waxman2007, Rush2006}. The differential effects of L858H \(\text{Na}_\text{V}\text{1.7}\) on firing is dependent on the presence or absence of another sodium channel, namely the \(\text{Na}_\text{V}\text{1.8}\) subunit \citep{Waxman2007, Rush2006}.  These findings, in concert with our findings emphasize that the ionic current environment in which a channelopathy occurs is vital in determining the outcomes of the channelopathy on firing.
+To our knowledge, no comprehensive evaluation of how ionic current environment and neuron type affect the outcome of ion channel mutations have been reported. However, comparisons between the effects of such mutations between certain neuron types were described. For instance, the R1648H mutation in \textit{SCN1A} does not alter the excitability of cortical pyramidal neurons, but causes hypoexcitability of adjacent inhibitory GABAergic neurons \citep{Hedrich14874}. In the CA3 region of the hippocampus, the equivalent mutation in \textit{SCN8A}, R1627H, increases the excitability of pyramidal neurons and decreases the excitability of parvalbumin positive interneurons \cite{makinson_scn1a_2016}. Additionally, the L858H mutation in \(\text{Na}_\text{V}\text{1.7}\), associated with erythermyalgia, has been shown to cause hypoexcitability in sympathetic ganglion neurons and hyperexcitability in dorsal root ganglion neurons \citep{Waxman2007, Rush2006}. The differential effects of L858H \(\text{Na}_\text{V}\text{1.7}\) on firing is dependent on the presence or absence of another sodium channel, namely the \(\text{Na}_\text{V}\text{1.8}\) subunit \citep{Waxman2007, Rush2006}.  These findings, in concert with our findings emphasize that the ionic current environment in which a channelopathy occurs is vital in determining the outcomes of the channelopathy on firing. However, many differences can exist between neuron types at multiple levels of scale not only in ion channel composition. Despite this complexity, the simulations performed here demonstrate that differential ion channel composition is sufficient to cause neuron type differences in the effects of ion channel mutations.
 
 Neuron type specific differences in ionic current properties are important in the effects of ion channel mutations. However, within a neuron type heterogeneity in channel expression levels exists and it is often desirable to generate a population of neuronal models and to screen them for plausibility to biological data in order to capture neuronal population diversity \citep{marder_multiple_2011,OLeary2016}. The models we used here are originally generated by characterization of current gating properties and by fitting of maximal conductances to experimental data \citep{pospischil_minimal_2008, ranjan_kinetic_2019, alexander_cerebellar_2019, otsuka_conductance-based_2004}. This practice of fixing maximal conductances based on experimental data is limiting as it does not reproduce the variability in channel expression and neuronal firing behavior of a heterogeneous neuron population \citep{verma_computational_2020}. For example, a model derived from the mean conductances in a neuronal sub-population within the stomatogastric ganglion, the so-called "one-spike bursting" neurons fire three spikes instead of one per burst due to an L-shaped distribution of sodium and potassium conductances \citep{golowasch_failure_2002}.  Multiple sets of conductances can give rise to the same patterns of activity also termed degeneracy and differences in neuronal dynamics may only be evident with perturbations \citep{marder_multiple_2011, goaillard_ion_2021}. The variability in ion channel expression often correlates with the expression of other ion channels \citep{goaillard_ion_2021} and neurons whose behavior is similar may possess correlated variability across different ion channels resulting in stability in the neuronal phenotype \citep{lamb_correlated_2013, soofi_co-variation_2012, taylor_how_2009}. The variability of ionic currents and degeneracy of neurons may account, at least in part, for the observation that the effect of toxins within a neuronal type is frequently not constant \citep{khaliq_relative_2006, puopolo_roles_2007, ransdell_neurons_2013}.
 
@@ -259,7 +259,7 @@ Changes in firing as characterized by \(\Delta\)AUC and \(\Delta\)rheobase occup
         \centering
         \includegraphics[width=\linewidth]{summary_fig.jpg}
         \linespread{1.}\selectfont
-	\caption[]{Summary of neuron type dependence of channelopathies. The wildtype channel (blue) is mutated (red) the effects of it in different neuron types (green, orange and grey) each with a unique set of ion channels determines the effect of firing seen on the right by the shift from the blue wildtype fI curve to the red fI curve for the mutated ion channel.}
+	\caption[]{Summary of neuron type dependence of channelopathies. The wild type channel (WT, blue) is mutated (Mutant, red) the effects of it in different neuron types (green, orange and grey) each with a unique set of ion channels (see inset axes) determines the effect of firing seen on the right by the shift from the blue wild type fI curve to the red fI curve for the mutated ion channel. Square root functions are used as fI curves for illustration purposes.}
         \label{fig:summary}
 \end{figure}
 

Frontiers_manuscript/For_Submission/Koch_reviewer_comments.tex

@@ -66,7 +66,8 @@
 
 \textit{Although the proposed study for publication presents an up-to-date approach, it does not bring any innovation beyond the previous studies (Hedrich et al., 2014; Makinson et al., 2016; Waxman, 2007; Rush et al., 2006).}
 
-We thank Reviewer 1 for their comments. Although previous studies (Hedrich et al., 2014; Makinson et al., 2016; Waxman, 2007; Rush et al., 2006) referred to by the reviewer and discussed in our manuscript investigate differences in outcomes of ion channel mutations in different neuronal types, these studies have addressed specific instances in which this is the case. For instance, Waxman, 2007 and Rush et al., 2006 demonstrate the dependence of Nav1.7 mutations on the presence/absence of Nav1.8. However whether the outcome of the mutation is graded with different levels of Nav1.8 or whether this effect is modulated by other channels is not investigated nor known. In this context, our work furthers the current knowledge that certain ion channel mutations can have different effects on firing in specific contexts (i.e. cell types) by generalizing this viewpoint to demonstrate that the effects of any ion channel mutation will depend in the neuron-type and its ion channel composition. This suggests that holistic understanding of the effects of ion channel mutations likely requires a more specific neuron-type dependent level of understanding than is generally used currently.  \textcolor{red}{Change anything in manuscript?}
+We thank the reviewer for their comments. Although previous studies (Hedrich et al., 2014; Makinson et al., 2016; Waxman, 2007; Rush et al., 2006) referred to by the reviewer and discussed in our manuscript investigate differences in outcomes of ion channel mutations in different neuronal types, these studies have addressed specific instances in which this is the case. For instance, Waxman, 2007 and Rush et al., 2006 demonstrate the dependence of Nav1.7 mutations on the presence/absence of Nav1.8. However whether the outcome of the mutation is graded with different levels of Nav1.8 or whether this effect is modulated by other channels is not investigated nor known. In this context, our work furthers the current knowledge that certain ion channel mutations can have different effects on firing in specific contexts (i.e. cell types) by generalizing this viewpoint to demonstrate that the effects of any ion channel mutation will depend in the neuron-type and its ion channel composition. This suggests that holistic understanding of the effects of ion channel mutations likely requires a more specific neuron-type dependent level of understanding than is generally used currently.  Although many differences can exist between neuron types, this study demonstrates that different ion channel composition alone is sufficient to cause neuron type specific ion channel mutation firing effects. The ``Ionic Current Environments Determine the Effect of Ion Channel Mutations'' section of the discussion has been updated to clarify this.
+
 
 % reduce down to changes in currents, in vivo lots of other changes, but show that it happens already on the level of currents - experiments don't show this
 
@@ -77,7 +78,7 @@ Currently personalized medicine approaches, although promising, have limited eff
 
 \textit{Considering that even a single mutation in the organism will affect many neuron types (cells) and the signaling pathways to which it is associated, how can all other possible effects be eliminated?}
 
-We thank the reviewer for their question. The complexity noted in the reviewers question is, in our opinion, vital to the outcome of any ion channel mutation. With this study we demonstrate this to be the case at a neuron-type level and suggest that distillation of this complexity into a LOF or GOF characterization in terms of firing is not meaningful unless tied to a neuron type. Although we make no claim as to at which point certain aspects of the complexity become less relevant or even irrelevant to the outcome of an ion channel mutation,  we use the simulation presented within this study to encourage investigation into neuron type specific effects and to use LOF/GOF firing characterizations with caution. \textcolor{red}{Change anything in manuscript?}
+We thank the reviewer for their question. The complexity noted in the reviewers question is, in our opinion, vital to the outcome of any ion channel mutation. With this study we demonstrate this to be the case at a neuron-type ion channel composition level and suggest that distillation of this complexity into a LOF or GOF characterization in terms of firing is not meaningful unless tied to a neuron type. Although we make no claim as to at which point certain aspects of the complexity become less relevant or even irrelevant to the outcome of an ion channel mutation, we use the simulation presented within this study to encourage investigation into neuron type specific effects and to use LOF/GOF firing characterizations with caution.
 
 \textit{Failure to analyze the effects of the obtained data on living organisms casts a shadow over the reliability of the results. The study would be much more valuable if the authors could at least support their results with animal experiments. Otherwise, it will not go beyond being an evaluation study based on already known mutations. This limits the originality of the study.}
 
@@ -91,21 +92,22 @@ Although we agree that data obtained from animal experiments is invaluable in th
 
 \textit{Authors should include a schematic representing the general biological characteristics of each model tested. Even this schematic could be appended to the model description section}
 
-Figure 1 has been updated to reflect the general anatomical characteristics of each model and Figure 2 has been added to reflect the ion current composition of each model. The manuscript has been updated to reflect these changes.
-
-\begin{figure}[!h]
-        \centering
-        \includegraphics[width=\linewidth]{diversity_in_firing_diagram.jpg}
-        \linespread{1.}\selectfont
-	\caption[]{}
-\end{figure}
+We thank the reviewer for their comments. Figure 1 has been updated to reflect the general anatomical characteristics of each model and Figure 2 has been added to reflect the ion current composition of each model. The manuscript has been updated to reflect these changes.
 
-\begin{figure}[!h]
-        \centering
-        \includegraphics[width=\linewidth]{model_g.jpg}
-        \linespread{1.}\selectfont
-	\caption[]{\(g_{max}\) distribution figure}
-\end{figure}
+%\begin{figure}[!h]
+%        \centering
+%        \includegraphics[width=\linewidth]{diversity_in_firing_diagram.jpg}
+%        \linespread{1.}\selectfont
+%	\caption[]{Diversity in Neuronal Model Firing. Spike trains (left), frequency-current (fI) curves (right) for Cb stellate  \textbf{(A)}, RS inhibitory  \textbf{(B)}, FS \textbf{(C)}, RS pyramidal  \textbf{(D)}, RS inhibitory +\Kv  \textbf{(E)}, Cb stellate +\Kv  \textbf{(F)}, FS +\Kv  \textbf{(G)}, RS pyramidal +\Kv  \textbf{(H)}, STN +\Kv  \textbf{(I)}, Cb stellate \(\Delta\)\Kv  \textbf{(J)}, STN \(\Delta\)\Kv  \textbf{(K)}, and STN  \textbf{(L)} neuron models. Models are sorted qualitatively based on their fI curves. Black markers on the fI curves indicate the current step at which the spike train occurs. The green marker indicates the current at which firing begins in response to an ascending current ramp, whereas the red marker indicates the current at which firing ceases in response to a descending current ramp (see Supplementary Figure S1). A schematic illustrating the anatomical locations of the models is included \textbf{(M)}, however single compartment models are used for each cell type.} 
+%        \label{fig:diversity_in_firing}
+%\end{figure}
+%
+%\begin{figure}[!h]
+%        \centering
+%        \includegraphics[width=\linewidth]{model_g.jpg}
+%        \linespread{1.}\selectfont
+%	\caption[]{Diversity in Neuronal Model Current Composition. Distributions of maximal current conductances (\(\mathrm{g}_{\mathrm{max}}\)) for Cb stellate  \textbf{(A)}, RS inhibitory  \textbf{(B)}, FS \textbf{(C)}, RS pyramidal  \textbf{(D)}, RS inhibitory +\Kv  \textbf{(E)}, Cb stellate +\Kv  \textbf{(F)}, FS +\Kv  \textbf{(G)}, RS pyramidal +\Kv  \textbf{(H)}, STN +\Kv  \textbf{(I)}, Cb stellate \(\Delta\)\Kv  \textbf{(J)}, STN \(\Delta\)\Kv  \textbf{(K)}, and STN  \textbf{(L)} neuron models. Models are sorted as in Figure \ref{fig:diversity_in_firing}.}
+%\end{figure}
 
 
 
@@ -123,16 +125,16 @@ Although hysteresis is important both from a dynamical systems perspective and i
 
 \textit{A final outline summarizing the main results of the main models and the general limitations of this approach could be included.}
 
-A summary figure has been added as Figure 6 and the discussion updated to reflect this.
+A summary figure has been added as Figure 7 and the discussion updated accordingly.
 
-\begin{figure}[!h]
-        \centering
-        \includegraphics[width=\linewidth]{summary_fig.jpg}
-        \linespread{1.}\selectfont
-	\caption[]{Something like this for a summary figure?}
-\end{figure}
-
-\FloatBarrier
+%\begin{figure}[!h]
+%        \centering
+%        \includegraphics[width=\linewidth]{summary_fig.jpg}
+%        \linespread{1.}\selectfont
+%	\caption[]{Summary of neuron type dependence of channelopathies. The wildtype channel (blue) is mutated (red) the effects of it in different neuron types (green, orange and grey) each with a unique set of ion channels (see inset axes) determines the effect of firing seen on the right by the shift from the blue wildtype fI curve to the red fI curve for the mutated ion channel.}
+%\end{figure}
+%
+%\FloatBarrier
 
 \section{Reviewer 3}
 \textit{With this study, authors aimed to investigate the impact of neuronal cell type on the firing outcome of ion channel mutations. Study were conducted with simulations on conductance-based neuron models.