Muscle models - Revision history

Akberdinir@gmail.com: /* OxP+glycolysis models */

2021-05-28T09:50:59Z

‎OxP+glycolysis models

Akberdinir@gmail.com: /* Published models */

2021-03-25T09:32:38Z

‎Published models

Akberdinir@gmail.com: /* OxP+glycolysis models */

2019-07-18T08:35:28Z

‎OxP+glycolysis models

Akberdinir@gmail.com: /* OxP+glycolysis models */

2019-07-18T08:33:41Z

‎OxP+glycolysis models

Akberdinir@gmail.com at 08:38, 14 February 2019

2019-02-14T08:38:45Z

Akberdinir@gmail.com at 08:36, 14 February 2019

2019-02-14T08:36:21Z

Akberdinir@gmail.com at 12:14, 5 October 2018

2018-10-05T12:14:07Z

Akberdinir@gmail.com: /* Biomodels */

2018-10-05T12:12:20Z

‎Biomodels

Akberdinir@gmail.com at 12:10, 5 October 2018

2018-10-05T12:10:17Z

Akberdinir@gmail.com at 11:55, 5 October 2018

2018-10-05T11:55:50Z

Show changes

@@ Line 44: / Line 44: @@
 <p align=justify>A set of integrated computational model describing different cellular factors (concentration of free ADP, Pi or pH level in the cell) regulating OxP and glycolysis has been developed (Mader, 2003) <cite>13</cite>; (Vinnakota et al., 2006) <cite>14</cite>; (Wu et al., 2007)  <cite>15</cite>. Namely, Mader <cite>13</cite> presented a mathematical description of the regulation of ATP production (or the activity of OxP) in muscle cells as a function of (1) free [ADP] as the substrate and (2) a second driving force F*deltaG (kilojoules per mole) resulting from the
-difference of free energy deltaG_ox,ap (kilojoules per mole) – deltaG_ATP,cyt (kilojoules per mole), which is necessary to sustain the flow of ATP from inside the mitochondrian to the cytosol. Results from the computational simulations demonstrated a high level of similarity with the experimentally observed dynamic behaviour of measurable parameters of OxP, glycolysis and the behaviour of the parameters of the  ytosolic high energy phosphate system. Furthermore, the computational simulations showed that complex dynamic behaviour in real biological systems can be ruled by a few general principles and a relatively simple set of equations. It is, however, emphasized by author that the model needs a more detailed implementation of the characteristics of the reactions occurring at the site of the mitochondria. In turn, Vinnakota with co-authors <cite>14</cite> developed the model, which is aimed to explain the influence of pH on biochemical reaction kinetics (Figure 4) and equilibria by prediction of experimental data from the study of Scopes <cite>16</cite> on ''in vitro'' glycogenolysis using a cell free reconstituted enzyme system (Figure 5). They found that a wide range of experimental data, 13 time series out of 15 from three experiments, are explained by adjustment of only three kinetic parameters out of nearly 100 parameters, which leads to a significant conclusion that accurate accounting for the physical chemistry (pH-dependent enzyme kinetics) leads to a model with greater predictive power.</p>
+difference of free energy deltaG_ox,ap (kilojoules per mole) – deltaG_ATP,cyt (kilojoules per mole), which is necessary to sustain the flow of ATP from inside the mitochondrian to the cytosol. Results from the computational simulations demonstrated a high level of similarity with the experimentally observed dynamic behaviour of measurable parameters of OxP, glycolysis and the behaviour of the parameters of the cytosolic high energy phosphate system. Furthermore, the computational simulations showed that complex dynamic behaviour in real biological systems can be ruled by a few general principles and a relatively simple set of equations. It is, however, emphasized by author that the model needs a more detailed implementation of the characteristics of the reactions occurring at the site of the mitochondria. In turn, Vinnakota with co-authors <cite>14</cite> developed the model, which is aimed to explain the influence of pH on biochemical reaction kinetics (Figure 4) and equilibria by prediction of experimental data from the study of Scopes <cite>16</cite> on ''in vitro'' glycogenolysis using a cell free reconstituted enzyme system (Figure 5). They found that a wide range of experimental data, 13 time series out of 15 from three experiments, are explained by adjustment of only three kinetic parameters out of nearly 100 parameters, which leads to a significant conclusion that accurate accounting for the physical chemistry (pH-dependent enzyme kinetics) leads to a model with greater predictive power.</p>
 {| align="center" style="background:#f8f9fa; border:1px solid #c8ccd1; font-size:100%; margin-center:1em" cellspacing=0 cellpadding=0

← Older revision		Revision as of 09:32, 25 March 2021
Line 105:		Line 105:

	<p align=justify> Analysis of the model suggested that metabolic activation and recruitment of muscle fibers are closely related, but the degree of metabolic activation inferred from metabolite changes may differ from that of the fiber recruitment. Simulations with a mechanistic, mathematical model demonstrated that the activation as measured by metabolic response in single fibers is distinct from fiber recruitment that is characterized by the number (or mass) of each fiber type involved during a specific exercise. The results from this study underline the need for critical experiments that measure fiber recruitment and metabolism in order to simulate and quantify the contributions of type I and II fibers to the regulation of energy metabolism. Such experimental techniques could be used in combination with the computational model to investigate the relationships between the extents of metabolic activation, number of fibers recruited, and muscle groups engaged at different intensity exercise.</p>		<p align=justify> Analysis of the model suggested that metabolic activation and recruitment of muscle fibers are closely related, but the degree of metabolic activation inferred from metabolite changes may differ from that of the fiber recruitment. Simulations with a mechanistic, mathematical model demonstrated that the activation as measured by metabolic response in single fibers is distinct from fiber recruitment that is characterized by the number (or mass) of each fiber type involved during a specific exercise. The results from this study underline the need for critical experiments that measure fiber recruitment and metabolism in order to simulate and quantify the contributions of type I and II fibers to the regulation of energy metabolism. Such experimental techniques could be used in combination with the computational model to investigate the relationships between the extents of metabolic activation, number of fibers recruited, and muscle groups engaged at different intensity exercise.</p>
−

	===Biomodels===		===Biomodels===

@@ Line 72: / Line 72: @@
 |}
-<span style="font-size:90%; align="center"">'''Figure 7 from (Wu et al., 2007)''' <cite>15</cite>. Model prediction of ADP concentration and inorganic phosphate concentration in cytoplasm as a function of ATP hydrolysis rate in the healthy subjects. A: plot of predicted ADP concentration in cytoplasm, [ADP]c, as a function of ATP hydrolysis rate, ATPase flux. B: plot of predicted Pi concentration in cytoplasm, [Pi]c, as a function of ATP hydrolysis rate, ATPase flux. The solid and dashed lines represent model-predicted results. The circles represent experimentally measured estimation from Jeneson et al. <cite>18</cite>. Solid line correspond to the optimal value of the total pool of exchangeable phosphate (TPP) = 36.8 mM; dashed lines correspond to TPP = 40.5 mM (10% greater than the optimal value).</span>
+<span style="font-size:90%">'''Figure 7 from (Wu et al., 2007)''' <cite>15</cite>. Model prediction of ADP concentration and inorganic phosphate concentration in cytoplasm as a function of ATP hydrolysis rate in the healthy subjects. A: plot of predicted ADP concentration in cytoplasm, [ADP]c, as a function of ATP hydrolysis rate, ATPase flux. B: plot of predicted Pi concentration in cytoplasm, [Pi]c, as a function of ATP hydrolysis rate, ATPase flux. The solid and dashed lines represent model-predicted results. The circles represent experimentally measured estimation from Jeneson et al. <cite>18</cite>. Solid line correspond to the optimal value of the total pool of exchangeable phosphate (TPP) = 36.8 mM; dashed lines correspond to TPP = 40.5 mM (10% greater than the optimal value).</span>
 ====Cabrera models====

@@ Line 72: / Line 72: @@
 |}
 <span style="font-size:90%; align="center"">'''Figure 7 from (Wu et al., 2007)''' <cite>15</cite>. Model prediction of ADP concentration and inorganic phosphate concentration in cytoplasm as a function of ATP hydrolysis rate in the healthy subjects. A: plot of predicted ADP concentration in cytoplasm, [ADP]c, as a function of ATP hydrolysis rate, ATPase flux. B: plot of predicted Pi concentration in cytoplasm, [Pi]c, as a function of ATP hydrolysis rate, ATPase flux. The solid and dashed lines represent model-predicted results. The circles represent experimentally measured estimation from Jeneson et al. <cite>18</cite>. Solid line correspond to the optimal value of the total pool of exchangeable phosphate (TPP) = 36.8 mM; dashed lines correspond to TPP = 40.5 mM (10% greater than the optimal value).</span>
 ====Cabrera models====

@@ Line 6: / Line 6: @@
 ====Korzeniewski models====
-<p align=justify>One of the first theoretical investigation in this field was conducted by Bernard Korzeniewski (Korzeniewski, 1998,1999) <cite>6</cite><cite>7</cite>. He developed the  computational model of oxidative phosphorylation (OxP) in skeletal muscle mitochondria in order to decipher a regulatory mechanism of the adjustment of ATP production to ATP consumption in contracting muscle. During the transition from the resting state of muscles to their maximal exercise, there is a great increase in energy demand (ATP consumption). Mitochondrial OxP is the main process responsible for ATP production in
+<p align=justify>One of the first theoretical investigation in this field was conducted by Bernard Korzeniewski (Korzeniewski, 1998,1999) <cite>6</cite><cite>7</cite>. He developed the  computational model of oxidative phosphorylation (OxP) in skeletal muscle mitochondria in order to decipher a regulatory mechanism of the adjustment of ATP production to ATP consumption in contracting muscle. During the transition from the resting state of muscles to their maximal exercise, there is a great increase in energy demand (ATP consumption). Mitochondrial OxP is the main process responsible for ATP production in most muscle fibre types under most conditions. Therefore, mitochondria have to ‘know’ in some way how fast should they produce ATP in a given moment of time to meet the rate of energy consumption and to avoid a drastic decrease in cytosolic phosphorylation potential which would hinder muscle contraction.Two alternative mechanisms of the adjusting the energy (ATP) production rate to the energy consumption rate were postulated. They can be called the ‘negative feedback’ and ‘parallel activation’ (Figure 1).</p>
 [[File:Korzeniewski1998 fig1 mechanisms ATP concentration.png|center]]

@@ Line 1: / Line 1: @@
 ===Introduction===
-<p align=justify>Skeletal muscle is one of the most abundant tissues in mammals, accounting for up to 40% of the total mass ofthe human body (Janssen et al., 2000)<cite>1</cite>. The contraction–relaxation cycle in muscle requires energy that is mostly generated aerobically by mitochondria particularly abundant in adult muscle fibres. It is worth to note that skeletal muscle can maintain ATP concentration constant during the transition from rest to exercise, whereas metabolic reaction rates may increase substantially (Kunz, 2001) <cite>2</cite>. Although it is well known that skeletal muscle adaptations to exercise depend on duration, intensity, and frequency, changes in muscle proteins associated with different types of exercise have not been well characterized (Gonzalez‐Freire et al., 2017) <cite>3</cite>. Moreover, the quantitative contributions of different fiber types to the energy demand and detailed dynamics of metabolic responses of the skeletal muscle in response to different exercise
+<p align=justify>Skeletal muscle is one of the most abundant tissues in mammals, accounting for up to 40% of the total mass ofthe human body (Janssen et al., 2000) <cite>1</cite>. The contraction–relaxation cycle in muscle requires energy that is mostly generated aerobically by mitochondria particularly abundant in adult muscle fibres. It is worth to note that skeletal muscle can maintain ATP concentration constant during the transition from rest to exercise, whereas metabolic reaction rates may increase substantially (Kunz, 2001) <cite>2</cite>. Although it is well known that skeletal muscle adaptations to exercise depend on duration, intensity, and frequency, changes in muscle proteins associated with different types of exercise have not been well characterized (Gonzalez‐Freire et al., 2017) <cite>3</cite>. Moreover, the quantitative contributions of different fiber types to the energy demand and detailed dynamics of metabolic responses of the skeletal muscle in response to different exercise
 intensities are unknown. Indeed, accurate measurements to quantify the recruitment and metabolic activation of muscle fibers ''in vivo'' have not been possible to date (Li et al., 2012) <cite>4</cite>. So due to a shortage of dynamic ''in vivo'' human data, the regulatory mechanisms of functioning of the skeletal muscle metabolism are poorly understood. To quantitatively interpret the limited data, a physiologically based mathematical modeling approach can be applied (Li et al., 2010) <cite>5</cite>.</p>

← Older revision		Revision as of 12:14, 5 October 2018
Line 199:		Line 199:

	<span style="font-size: 90%"> '''Table1'''. The result of search in BioModels database using query "''skeletal muscle''".</span>		<span style="font-size: 90%"> '''Table1'''. The result of search in BioModels database using query "''skeletal muscle''".</span>
		+

	===Conclusion===		===Conclusion===

@@ Line 131: / Line 131: @@
 | <p align=justify><span style="font-size: 90%"> '''Relevant'''</span></p>
 |-
-| <p align=justify><span style="font-size: 90%"> [https://www.ebi.ac.uk/biomodels-main/BIOMD00000005740 BIOMD0000000574]</span></p>
+| <p align=justify><span style="font-size: 90%"> [https://www.ebi.ac.uk/biomodels-main/BIOMD0000000574 BIOMD0000000574]</span></p>
 | <p align=justify><span style="font-size: 90%"> Lai2014 - Hemiconcerted MWC model of intact calmodulin with two targets.</span></p>
 | <p align=justify><span style="font-size: 90%"> Calmodulin is a calcium-binding protein ubiquitous in eukaryotic cells, involved in numerous calcium-regulated biological phenomena, such as synaptic plasticity, muscle contraction, cell cycle, and circadian rhythms. Authors built a mechanistic allosteric model of calmodulin, based on an hemiconcerted framework: each calmodulin lobe can exist in two conformations in thermodynamic equilibrium, with different affinities for calcium and different affinities for each target. Each lobe was allowed to switch conformation on its own. The model was parameterised and validated against experimental data from the literature. In spite of its simplicity, a two-state allosteric model was able to satisfactorily represent several sets of experiments, in particular the binding of calcium on intact and truncated calmodulin and the effect of different skMLCK peptides on calmodulin's saturation curve. The model can also be readily extended to include multiple targets.</span></p>

@@ Line 179: / Line 179: @@
 | <p align=justify><span style="font-size: 90%"> '''Irrelevant'''</span></p>
 |-
-| <p align=justify><span style="font-size: 90%"> [https://www.ebi.ac.uk/biomodels-main/MODEL1012220002 MODEL1012220002]</span></p>
+| <p align=justify><span style="font-size: 90%"> [https://www.ebi.ac.uk/biomodels-main/MODEL1310110011 MODEL1310110011]</span></p>
-| <p align=justify><span style="font-size: 90%"> Caron2010_mTOR_SignalingNetwork.</span></p>
+| <p align=justify><span style="font-size: 90%"> Thiele2013 - Skeletal muscle myocytes.</span></p>
-| <p align=justify><span style="font-size: 90%"> . </span></p>
+| <p align=justify><span style="font-size: 90%"> The model of skeletal muscle myocytes metabolism is derived from the community-driven global reconstruction of human metabolism (version 2.02, [http://identifiers.org/biomodels.db/MODEL1109130000 MODEL1109130000]). </span></p>
-| <p align=justify><span style="font-size: 90%">  <cite>34</cite> </span></p>
+| <p align=justify><span style="font-size: 90%"> Thiele et al., 2013 <cite>34</cite> </span></p>
 | <p align=justify><span style="font-size: 90%"> '''Irrelevant'''</span></p>
 |}
@@ Line 189: / Line 201: @@
 ===Conclusion===
-<p align=justify> The model approaches discussed above address various spatial, temporal and biochemical resolutions and are conceptually complementary. However, they were not yet linked in an integrated computational framework and executed simultaneously using a modular approach (Snoep et al., 2006) <cite>34</cite> that has been implemented in [http://wiki.biouml.org/index.php/BioUML_wiki BioUML platform]. Computational modeling of metabolic responses of skeletal muscle to physiological stresses (e.g., hypoxia, ischemia, and exercise) is intended to quantitatively elucidate regulatory mechanisms. Although more detailed mechanisms can be included, these cannot be effectively validated without appropriate ''in vivo'' experimental data. With the availability of additional data, the model could incorporate and test alternative mechanisms of the regulation. The fact that available models do not account for blood flow heterogeneity and all fiber type distributions associated with skeletal muscle cellular metabolism constrains the application of virtual physiological modelling to sports or space travel.But the most significant limitation of the present models is that they do not account for detailed interconnetction between signaling pathways activated by physiological stresses and downstream changes on coupled transcriptional and metabolic levels using positive and negative feedbacks. In order to overcome these shortcomings, we are going to extend previously developed model (Li et al., 2009) <cite>26</cite> incorporating essential steps of [http://virtualbiology.biouml.org/index.php/AMPK AMPK signaling pathway] in skeletal muscle which is regulated in a distinct manner during contraction depending on exercise intensity and duration.</p>
+<p align=justify> The model approaches discussed above address various spatial, temporal and biochemical resolutions and are conceptually complementary. However, they were not yet linked in an integrated computational framework and executed simultaneously using a modular approach (Snoep et al., 2006) <cite>37</cite> that has been implemented in [http://wiki.biouml.org/index.php/BioUML_wiki BioUML platform]. Computational modeling of metabolic responses of skeletal muscle to physiological stresses (e.g., hypoxia, ischemia, and exercise) is intended to quantitatively elucidate regulatory mechanisms. Although more detailed mechanisms can be included, these cannot be effectively validated without appropriate ''in vivo'' experimental data. With the availability of additional data, the model could incorporate and test alternative mechanisms of the regulation. The fact that available models do not account for blood flow heterogeneity and all fiber type distributions associated with skeletal muscle cellular metabolism constrains the application of virtual physiological modelling to sports or space travel.But the most significant limitation of the present models is that they do not account for detailed interconnetction between signaling pathways activated by physiological stresses and downstream changes on coupled transcriptional and metabolic levels using positive and negative feedbacks. In order to overcome these shortcomings, we are going to extend previously developed model (Li et al., 2009) <cite>26</cite> incorporating essential steps of [http://virtualbiology.biouml.org/index.php/AMPK AMPK signaling pathway] in skeletal muscle which is regulated in a distinct manner during contraction depending on exercise intensity and duration.</p>
@@ Line 227: / Line 239: @@
 #32 pmid=21179025       <!-- Caron et al 2010 -->
 #33 https://www.tandfonline.com/doi/abs/10.1080/00207178608933494       <!-- De Paor and Timmons 1986 -->
+#34 pmid=23455439       <!-- Thiele et al 2013 -->
-#34 pmid=16242236       <!-- Snoep et al 2006 -->
+#35 https://link.springer.com/article/10.1007%2Fs11306-016-1051-4       <!-- Swainston et al 2016 -->
 </biblio>