Abstract
BACKGROUND:
The current excitation-contraction coupling model of fast-twitch skeletal muscle fibers cannot completely simulate the excitation-contraction process.
OBJECTIVE:
To solve this problem, this study proposes an excitation-contraction model of fast-twitch skeletal muscle fibers based on the physiological structure and contractile properties of half-sarcomeres.
METHODS:
The model includes the action potential model of fast-twitch fiber membranes and transverse tubule membranes, the cycle model of ๐ถ๐2+ in myofibril, the cross-bridge cycle model, and the fatigue model of metabolism.
RESULTS:
Finally, detailed analyses of the results from the simulation are conducted using the Simulink toolbox in MATLAB. Two conditions, non-coincidence and coincidence, are analyzed for both the thick and thin myofilaments.
CONCLUSIONS:
The simulation results of two groups of models are the same as the previous research results, which validates the accuracy of models.
1.Introduction
At present, the action potential of the sarcolemma and transverse tubule membranes are studied using the Hodgkin-Huxley model and the Goldman-Hodgkin-Katz equation. The inwardly rectifying potassium (Kir) channel, together with the chloride channel (ClC-1), plays a vital role in the skeletal muscle physiology. Current research primarily focuses on the fibrous surface and transverse tubular system (TTS) of amphibiansย [1], and the measurement of Kir of mammalian skeletal muscle fibersย [2, 3, 4]. The primary physiological function of mammalian skeletal muscle is to maintain the stability of resting membrane potential and to enable the excitation of electrogenic cells. It is essential to study the properties of Kir in mammalian skeletal muscle fibers to further understand the related muscular diseasesin humansย [5, 6, 7].
In vertebrate skeletal muscle fibers, action potential controls contractile activity by inducing rapid changes of the free ๐ถ๐2+ concentration in the sarcoplasm. ๐ถ๐2+, thus, plays a role in triggering and regulating skeletal muscle contraction, and its concentration affects contractile force and speed. In order to regulate ๐ถ๐2+ in a myofibril accurately, according to the physiological structure properties of the sarcomere, researchers have used half-sarcomeres and divide its spaceย [8] to analyze the change of ๐ถ๐2+ concentration in the corresponding regionsย [9, 10, 11]. The ๐ถ๐2+ indicator, with low affinity, is microinjected into the sarcoplasm to evaluate the amplitude and time historyof regional spatial variation of mean ๐ถ๐2+ concentrationย [12]. This model can be used to evaluate the movement of ๐ถ๐2+ in myofibrils, the release of ๐ถ๐2+ in sarcoplasmic reticulum, the combination of ๐ถ๐2+ in sarcoplasm, and primary buffers (such as troponin, ATP, parvalbumin, and sarcoplasmic reticulum ๐ถ๐2+ pump). The ๐ถ๐2+ in sarcoplasm is recycled to the sarcoplasmic reticulum by the ๐ถ๐2+ pump.
Skeletal muscle fibers exhibit contractility through the relative sliding of the sarcomereโs thick and thin myofilaments in the myofibril. The sliding process depends on the hydrolysis coupling of ATP and the circulation of the fine filaments, i.e., actin filaments, along the molecules on the head of myosin, forming cross-bridges. In striated muscle fibers, it is believed that tropomyosin and troponin prevent the binding of myosin and actin, which form the cross-bridge circulation and act as doormen. Tropomyosin is the basis of many of actinโs biological activities, and the movement of troponin on the surface of actin is considered critical to the cooperative allosteric regulation of actin. The different positions of the thick and thin myofilaments will affect the speed at which ๐ถ๐2+ and troponin combine and separateย [13]. Skeletal muscle fibers become fatigued during the excitation-contraction process, and many factors affect fatigue. Metabolic fatigue is a multi-factored regulation process that includes the accumulation of phosphate, cross-bridge circulation, and reduction of calcium in the sarcoplasmic reticulumย [14].
The currently existing excitation-contraction model of fast-twitch muscle fibers cannot simulate the excitation-contraction process completely. To solve this problem, this study proposes an excitation-contraction model for fast-twitch skeletal muscle fibers based on the physiological structure and contractile properties of the half-sarcomere. The study is structured as follows: (1) establishment of an action potential model for fast-twitch muscle fiber membranes and transverse tubule membranes; (2) establishment of the cycle model of ๐ถ๐2+ in myofibril; (3) establishment of the cross-bridge cycle model and the fatigue model of metabolism in half-sarcomeres; and (4) analysis of the results from the excitation-contraction model simulation.
2.Methods
2.1Modeling of the skeletal fast muscle fiber membrane potential
A two-compartment model was used to simulate the action potential of skeletal fast muscle fibers. The total ionic current on the surface of the muscle fibers is calculated by adding ๐๐+ current (I๐๐,s), K+ delayed rectifier current (I๐ท๐
,s), K+ inward rectifier current (I๐ผ๐
,s), ๐ถ๐- current (I๐ถ๐,s), and ๐๐+-K+ current (I๐๐๐พ,s), as shown by Eq.ย (1):
Due to the difference between ion channel densities in the T tube and myolemma, the ratio of T tube membrane channel density to myolemma channel density can be represented by ฮท. The calculation of ionic current (I๐๐๐๐๐,t) on the T tube per unit area is then shown by Eq.ย (2) as:
where g๐๐,t is the ๐๐+ channel conductance, g๐ท๐
,t is the K+ channel conductance, g๐ผ๐
,t is the K+ inward rectifier channel conductance, and g๐ถ๐,t is the ๐ถ๐- channel conductance.
The changes inconcentration of ๐๐+โข(๐๐i) in cells, ๐๐+โข(๐๐t) in T tube, and ๐๐+โข(๐๐e) in intercellular space are shown in Eqsย (3)โ(5), using time as the basis. Similarly, the changes inconcentration (with respect to time) of K+โข(Ki) in cells, K+โข(Kt) in T tube, and K+โข(Ke) in intercellular space can be obtained.
In Eqsย (2)โ(5), fT represents the proportion of fiber occupied by the T tube, ฮพ1 represents the ratio of volume to surface area of the T tube, ฮพ2 represents the ratio of volume to surface area of cells, ฮพ3 represents the ratio of volume to surface area of intercellular space, ฯ๐๐1 represents diffusion time constant of the T tube, and ฯ๐๐2 represents the diffusion time constant of intercellular space.
2.2Modeling of calcium cyclingin skeletal fast muscle fiber
We divided the sarcomere into six geometric regions, which are V1, V2, V3, V4, V5, and V6, as shown in Fig.ย 1. V1=5.5%โขV, V2=3.5%โขV, V3=6%โขV, V4=85%โข(1-0.75lx)โขV, V5=85%โข(1.75lx-1)โขV, and V6=85%โข(1-1lx)โขV.
Figureย 1.
Geometric division diagram of half sarcomere.
Tableย 1
Parameter | Unit | Value | Parameter | Unit | Value |
---|
๐๐๐๐ก๐๐ก
| ฮผM | 1500 |
k๐ถ๐,๐๐๐๐๐๐
| ms-1 | 0.0005 |
---|
๐ถ๐ ๐ก๐๐ก
| ฮผM | 31000 |
k๐ถ๐,๐ด๐๐๐๐
| ฮผM-1ms-1 | 0.15 |
---|
K๐๐
| ฮผM | 1 |
k๐ถ๐,๐ด๐๐๐๐๐
| ms-1 | 30 |
---|
ฯ
๐๐
| ฮผMmsฮผ-1m-3 | 4.875 |
k๐๐,๐๐๐๐๐
| ฮผM-1ms-1 | 3.3 ร 10-5 |
---|
Le
| ฮผm3ms-1 | 2 ร 10-5 |
k๐๐,๐๐๐๐๐๐
| ms-1 | 0.003 |
---|
ฯR=ฯR๐๐
| ฮผm3ms-1 | 0.75 |
k๐๐,๐ด๐๐๐๐
| ฮผM-1ms-1 | 1.5 ร 10-3 |
---|
ฯ๐ด๐๐
| ฮผm3ms-1 | 0.375 |
k๐๐,๐ด๐๐๐๐๐
| ms-1 | 0.15 |
---|
ฯ๐๐
| ฮผm3ms-1 | 1.5 |
k๐ถ๐,๐ถ๐ ๐๐
| ฮผM-1ms-1 | 4 ร 10-6 |
---|
k๐ถ๐,๐๐๐๐๐
| ฮผM-1ms-1 | 0.0417 |
k๐ถ๐,๐ถ๐ ๐๐๐
| ms-1 | 0.005 |
---|
i
| ฮผm3ms-1 | 300 | | | |
---|
A 10-state model is used to describe the release process from the T tube voltage to the Reynolds channel. The ten states consist of five states of four voltage sensor molecules and two states of a Reynolds channel (including five closed states (C0-C4) and five open states). Based on the division of the half-sarcomere regions and the ๐ถ๐2+ cycle, the change in ๐ถ๐2+ concentration in V1-V6 regions are analyzed, as shown in Eqsย (6)โ(11), and the modelโs parameters are shown in Tableย 1:
(6)
๐๐ถ๐1๐๐ก=ฯ
๐๐
โข๐ถ๐3(๐ถ๐3+K๐๐
)โขV1-Leโข(๐ถ๐1-๐ถ๐3)V1-ฯR๐๐
โข(๐ถ๐1-๐ถ๐2)V1
(7)
๐๐ถ๐2๐๐ก=-iโข(O0+O1+O2+O3+O4)โข(Cโขa2-Cโขa3)V2+ฯ
๐๐
โข๐ถ๐3(๐ถ๐3+K๐๐
)โขV2-Leโข(๐ถ๐2-๐ถ๐3)V2+ฯR๐๐
โข(๐ถ๐1-๐ถ๐2)V2+(k๐ถ๐,๐ถ๐ ๐๐๐โข๐ถ๐2๐ถ๐ -k๐ถ๐,๐ถ๐ ๐๐โข๐ถ๐2โข(๐ถ๐ ๐ก๐๐ก-๐ถ๐2๐ถ๐ ))
(8)
๐๐ถ๐3๐๐ก=iโข(O0+O1+O2+O3+O4)โข(๐ถ๐2-๐ถ๐3)V3-2โขฯ
๐๐
โข๐ถ๐3(๐ถ๐3+K๐๐
)โขV3+Leโข(๐ถ๐1-๐ถ๐3)V3+Leโข(๐ถ๐2-๐ถ๐3)V3-ฯRโข(3โข๐ถ๐3-๐ถ๐4-๐ถ๐5-๐ถ๐6)V3-(k๐ถ๐,๐๐๐๐๐โข๐ถ๐3โข(๐๐๐๐ก๐๐ก-๐ถ๐3๐๐๐-๐๐3๐๐๐)-k๐ถ๐,๐๐๐๐๐๐โข๐ถ๐3๐๐๐)-(k๐ถ๐,๐ด๐๐๐๐โข๐ถ๐3โข๐ด๐๐3-k๐ถ๐,๐ด๐๐๐๐๐โข๐ถ๐3๐ด๐๐)
(9)
๐๐ถ๐4๐๐ก=ฯRโข(๐ถ๐3-2โข๐ถ๐4+๐ถ๐5)V4-(k๐ถ๐,๐๐๐๐๐โข๐ถ๐4โข(๐๐๐๐ก๐๐ก-๐ถ๐4๐๐๐-๐๐4๐๐๐)-k๐ถ๐,๐๐๐๐๐๐โข๐ถ๐4๐๐๐)-(k๐ถ๐,๐ด๐๐๐๐โข๐ถ๐4โข๐ด๐๐4-k๐ถ๐,๐ด๐๐๐๐๐โข๐ถ๐4๐ด๐๐)-F1โข(๐ถ๐4,Tn)
(10)
๐๐ถ๐5๐๐ก=ฯRโข(๐ถ๐3+๐ถ๐4-3โข๐ถ๐5+๐ถ๐6)V5-(k๐ถ๐,๐๐๐๐๐โข๐ถ๐5โข(๐๐๐๐ก๐๐ก-๐ถ๐5๐๐๐-๐๐5๐๐๐)-k๐ถ๐,๐๐๐๐๐๐โข๐ถ๐5๐๐๐)-(k๐ถ๐,๐ด๐๐๐๐โข๐ถ๐5โข๐ด๐๐5-k๐ถ๐,๐ด๐๐๐๐๐โข๐ถ๐5๐ด๐๐)-F2โข(๐ถ๐5,Tn)
(11)
๐๐ถ๐6๐๐ก=ฯRโข(๐ถ๐3+๐ถ๐5-2โข๐ถ๐6)V6-(k๐ถ๐,๐๐๐๐๐โข๐ถ๐6โข(๐๐๐๐ก๐๐ก-๐ถ๐6๐๐๐-๐๐6๐๐๐)-k๐ถ๐,๐๐๐๐๐๐โข๐ถ๐6๐๐๐)-(k๐ถ๐,๐ด๐๐๐๐โข๐ถ๐6โข๐ด๐๐6-k๐ถ๐,๐ด๐๐๐๐๐โข๐ถ๐6๐ด๐๐)
2.3Modeling of the cross-bridge dynamic in skeletal fast muscle fiber
A 6-state model is used when ๐ถ๐2+ and troponin combine in the non-coincidence region (V4), and the binding site of free troponin (T0) is shown in Eq.ย (12):
In the non-coincidence region (V4) the function of combining ๐ถ๐2+ and troponin, F1โข(๐ถ๐4,Tn), is calculated according to Eq.ย (13):
In the coincidence region (V5), an 8-state model is used. During the process of cross-bridge cycling, the binding site of free troponin (T0) is shown in Eq.ย (14):
Tableย 2
Parameter | Unit | Value | Parameter | Unit | Value |
---|
T๐ก๐๐ก
| ฮผM | 140 |
k2๐๐
| ms-1 | 0.15 |
k๐ถ๐,T๐๐
| ฮผM-1ms-1 | 0.04425 |
k2๐๐๐
| ms-1 | 0.05 |
k๐ถ๐,T๐๐๐
| ms-1 | 0.115 |
fp
| ms-1 | 15 |
k0๐๐
| ms-1 | 0 |
f0
| ms-1 | 1.5 |
k0๐๐๐
| ms-1 | 0.15 |
hp
| ms-1 | 0.18 |
k1๐๐
| ms-1 | 0 |
h0
| ms-1 | 0.24 |
k1๐๐๐
| ms-1 | 0.12 |
g0
| ms-1 | 0.12 |
In the coincidence region (V5), during the process of cross-bridge cycling, the function of combining ๐ถ๐2+ and troponin, F2โข(๐ถ๐5,Tn), is calculated according to Eq.ย (15), and the modelโs parameters are shown in Tableย 2.
2.4The fatigue modeling of metabolismin skeletal fast muscle fiber
During the cross-bridge cycle, when the cross-bridge attains a strong binding state from a weak binding state, the ATP will be hydrolyzedto generate ADP and Pi. When the product of solubilities of P๐๐๐
and ๐ถ๐1 in sarcoplasmic reticulum exceeds 6 mM2, it is considered that the Pi in sarcoplasmgoes through the passive channel and is transported to the sarcoplasmic reticulum,at a speed ranging from 30 to 170 ฮผm/s, and combines with ๐ถ๐2+ in the sarcoplasmic reticulum to generate precipitation. A Pi of 20 mM can reduce 29% of ๐ถ๐2+ released by sarcoplasmic reticulum. When the muscle fiber contraction ends, Pi in sarcoplasm can slowly be removed, and the sarcoplasm would then recover to a resting state of 3 mM.
3.Results and discussion
3.1Analysis of the simulated excitation-contraction model of skeletal fast muscle fiber in conditions of non-coincidence of the thick and thin myofilaments
When skeletal fast muscle fibers are in a resting state, the number of ions in the myofibrilremains constant, and their concentrations are shown in Tableย 3. Calsequestrin in the sarcoplasmic reticulum combines a large amount of calcium ions to be stored. Magnesium ions in sarcoplasm combine at most of the ATP binding sites. The percentages of calcium and magnesium ions in binding sites, respectively, are shown in Tableย 4.
Tableย 3
Ion concentration in the myofibril at resting
Ions | Concentration | Unit |
---|
Calsequestrin (sarcoplasmic reticulum) | 31000 | ฮผM |
๐ถ๐2+ (sarcoplasmic reticulum) | 1500 | ฮผM |
๐ถ๐2+ (sarcoplasm) | 0.05 | ฮผM |
๐๐2+ (sarcoplasm) | 1000 | ฮผM |
ATP (sarcoplasm) | 8000 | ฮผM |
Parvalbumin (sarcoplasm) | 1500 | ฮผM |
Troponin (sarcoplasm) | 240 | ฮผM |
Tableย 4
The percentage of ions in the binding site at resting
[height=0.8cm,width=3.4cm]IonsConjugates |
๐ถ๐2+
|
๐๐2+
|
---|
Calsequestrin (sarcoplasmic reticulum) | 54.55% | โ |
Troponin (sarcoplasm) | 7.1% | โ |
Parvalbumin (sarcoplasm) | 41% | 54.1% |
ATP (sarcoplasm) | โ | 90.9% |
Figureย 2.
Release rate of calcium ion in terminal cistern.
Figureย 3.
The simulation module of the binding of calcium ion and troponin.
Figureย 4.
The simulation system of the excitation-contraction model of fast-twitch fiber of skeletal muscle when non-coincidence of the thick and thin myofilaments occurs.
When a single action potential is transmitted to the transverse tubule membranes, the dihydropyridine voltage receptor (DHPR) on the transverse tubule membranes is coupled to the Reynolds channel on the terminal cistern to control the release of ๐ถ๐2+ on the terminal cistern. According to the mathematical model established in this study, a simulation module is built in Simulink to simulate the release of ๐ถ๐2+ on the terminal cistern membrane under the control of a single action potential. The simulation results are shown in Fig.ย 2. The terminal cistern began to release the ๐ถ๐2+ 1.4 ms after the action potential was generated, the peak value of the release flow was 204 ฮผM/ms, and the time corresponding to the peak value was 2.8 ms. The simulation results were the same asthe mean value detected by Baylor et al., upon calculation of the ๐ถ๐2+ fluoresce in of 11 muscle fibers. When ๐ถ๐2+ was released into the sarcoplasm, it bound to the binding sites of the buffer in the sarcoplasm, such as ATP and parvalbumin. In the V4 region, besides ATP and parvalbumin, troponin was also included. Considering the V4 region as an example, the simulation process carried out in Simulink can be described as follows: (1) in the V4 region, the calcium ion and troponin are bound with a 6-state model; (2) a blank model editing window is opened in Simulink; (3) the corresponding modules are created; (4) the parameters of the module are set and the values are shown in Tableย 4; and (5) the modules are connected, as shown in Fig.ย 3. The simulation processes for the other regions are similar to those employed for the V4 region. The modules built in Simulink are connected to form the excitation-contraction model of fast-twitch skeletal muscle fibers when non-coincidence of the thick and thin myofilaments occur. The module system is shown in Fig.ย 4. The system parameters of the model are set, the start time of the simulation is set to 0, the stop time of the simulation is set to 30, and the type is set to Variable-stepย [15].
Figureย 5.
The average concentration of free calcium ions in sarcoplasm.
Figureย 6.
Changes in the concentration of the free troponin binding sites and the binding of troponin and calcium ions in V4.
The simulation results showed that the concentrations of calcium ions in V3, V4, and V5 regions increased after 1.4 ms, and the concentrations of free calcium ions in V3 reached a peak value of 24 ฮผM at 3.5 ms. With the diffusion of free calcium ions, the concentrations of calcium ions in V4 and V5 also reached peak values. When the action potential was stopped, the calcium pump began to transport the calcium ions from the sarcoplasm to the sarcoplasmic reticulum, which led to the decrease of the calcium ion concentration in the sarcoplasm, upon which it attained resting state. As shown in Fig.ย 5, Baylor and Hollingworth measured the change of calcium ion concentration by injecting the calcium ion indicator, furaptra, into the fast-twitch muscle fibers of mice. The peak value of free calcium ion concentration measured was 17 ฮผM, and the time corresponding to the peak value was 4.2 ms. Baylorโs simulation by a multi-compartment model showed that the average free calcium ion concentration in sarcoplasm was 16.3ย ฮผM, and the corresponding time was 3.5 ms. The simulation results in this study show that the average free calcium ion concentration in sarcoplasm is 16 ฮผM, and the time corresponding to the peak value is 3.2 ms. The free ๐ถ๐2+ concentration amplitude in this study are similar to the experimental results and Baylorโs simulation results, and the time taken for free calcium ion concentration to reach the peak value is slightly smaller than that in Baylorโs study. The small size of the half-sarcomere used in this model may have led to the free calcium ion concentration in the sarcoplasm reaching the peak value more quickly. In the region of V4, ๐ถ๐2+ binds to troponin. Since the thick and thin myofilaments do not overlap, there is no cross-bridge activation, and the 6-state model is used to combine ๐ถ๐2+ and troponin. The simulation results of the binding of ๐ถ๐2+ and troponin in region V4 are shown in Fig.ย 6. With the combination of ๐ถ๐2+ and troponin, the concentration of tropomyosin-binding site of free troponin decreased, and its lowest value was approximately 170 ฮผM, which was 71% of the concentration of free binding site of troponin in the resting state. The concentration of combining ๐ถ๐2+ and troponin increased, and its maximum value was found to be 70 ฮผM, which was 29% of the combination of calcium ions and troponin at resting state, and the corresponding time was 7 ms. Zot et al. simulated the combination of ๐ถ๐2+ and troponin, and the peak value of free troponin binding site was 75% of that in its resting state. The peak value of the combination of calcium ion and troponin was 25% of that in its resting state, and the time corresponding to the peak value was 10 ms. In comparison to the simulation results of Zot et al.ย [16], the peak value obtained in this study is slightly higher, and the time corresponding to the peak value is a little shorter. However, the time corresponding to the peak value in this study is closer to that of Baylor et al. The simulation results of the relationship between troponin and tropomyosin regulated unit concentration is shown in Fig.ย 7. Only a small number of tropomyosin structures have conformational changes when a single action potential is stimulated.
Figureย 7.
Changes in the concentration of troponin and tropomyosin regulated the units in V4.
Figureย 8.
Changes in the concentration of troponin and tropomyosin regulated the units.
3.2Simulation analysis of the excitation-contraction model of skeletal fast muscle fiber in the condition of coincidence of the thick and thin myofilaments
When the half-sarcomereโs length was lxโ[1.1,1.75)ฮผm, the thick and thin myofilaments coincided. With the shortening of the half-sarcomere, the number of the coincident thick and thin myofilaments increased; at that time, the structure of the half-sarcomere was divided into 6 regions. In region V4, a 6-state model was used when ๐ถ๐2+ and troponin combines. In region V5, due to the coincidence of thick and thin myofilaments, when troponin was combined with two ๐ถ๐2+ ions, the cross-bridge was activated, so an 8-state model was used in this region.
When lx= 1.1 ฮผm, the thick and thin myofilaments coincided completely. The conformations of troponin and tropomyosin regulated units are changed by the binding of ๐ถ๐2+ and troponin during a single contraction. The head of myosin (cross bridge) on the thick myofilament binds to the myosin-binding site on the thin myofilament, actin, by hydrolyzing ATP. The simulation results of the combination of ๐ถ๐2+ and troponin, and cross-bridge activation are shown in Fig.ย 8. In comparison to Fig.ย 7, the concentration of troponin and tropomyosin regulated units increased, and the combination of myosin to actin changed the rate of binding and separation of ๐ถ๐2+ and troponin. This promoted the conformation change of tropomyosin and decreased the rate of separation of ๐ถ๐2+ and troponin. The result was consistent with that of Zotโs. The simulation results of the degree of cross-bridge activation are shown in Fig.ย 9. The degree A2 of cross-bridge activation represents the intensity of the excitation-contraction of the skeletal fast muscle fibers. The simulation results of ATP hydrolysis are shown in Fig.ย 10, with the circulation of the cross-bridge, and the phosphoric acid produced by hydrolysis of ATP accumulating continuously.
Figureย 9.
Degree of cross-bridge activation.
Figureย 10.
ATP hydrolyzed to produce phosphate.
When the length of the half-sarcomere is lxโ(1.1,1.75), the model can be used to simulate the change of free calcium ion concentration in the sarcoplasm, the degree of cross-bridge activation, and the Pi produced by ATP hydrolysis in the conditions of different degrees of coincidence of thick and thin myofilaments.
Acknowledgments
This research was supported by NSFC (No. 61572159, No. 61972117), the Natural Science Foundation of Heilongjiang Province of China (ZD2019E007).
Conflict of interest
None to report.
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