Integrated model validation - Revision history

Akberdinir@gmail.com: /* References */

2021-03-25T10:55:36Z

‎References

Akberdinir@gmail.com: /* References */

2021-03-25T10:54:15Z

‎References

Akberdinir@gmail.com: /* References */

2021-03-25T10:45:34Z

‎References

Akberdinir@gmail.com: /* Model verification */

2021-03-25T10:17:01Z

‎Model verification

Akberdinir@gmail.com: /* References */

2021-03-25T10:07:37Z

‎References

Akberdinir@gmail.com: /* Discussion */

2021-03-25T10:01:57Z

‎Discussion

Akberdinir@gmail.com: /* Model verification */

2021-03-25T09:57:22Z

‎Model verification

Akberdinir@gmail.com: /* Discussion */

2021-03-25T09:56:31Z

‎Discussion

Akberdinir@gmail.com: /* Model verification */

2021-03-25T09:54:24Z

‎Model verification

Akberdinir@gmail.com: /* Model verification */

2021-03-25T09:53:42Z

‎Model verification

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 #20 pmid=12617691	<!-- Jentjens&Jeukendrup 2003 -->
 #21 pmid=27789774	<!-- Burke et al 2017 -->
-#22 pmid=23134486	<!-- Maehlum&Hermansen 1978 -->
+#22 pmid=705238	        <!-- Maehlum&Hermansen 1978 -->

← Older revision		Revision as of 10:54, 25 March 2021
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	#17 pmid=22510711 <!-- Jensen et al 2012b -->		#17 pmid=22510711 <!-- Jensen et al 2012b -->
	#18 pmid=23134486 <!-- Palm et al 2013 -->		#18 pmid=23134486 <!-- Palm et al 2013 -->
		+	#19 pmid=7776237 <!-- Casey et al 1995 -->
		+	#20 pmid=12617691 <!-- Jentjens&Jeukendrup 2003 -->
		+	#21 pmid=27789774 <!-- Burke et al 2017 -->
		+	#22 pmid=23134486 <!-- Maehlum&Hermansen 1978 -->

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 <biblio>
 #1 pmid=12433845	<!-- Roussel et al 2003 -->
-#1 pmid=31131402	<!-- Kolpakov et al 2019 -->
+#2 pmid=17206438	<!-- Greiner et al 2007 -->
-#1 pmid=31131402	<!-- Kolpakov et al 2019 -->
+#3 pmid=PMC3764623	<!-- Cannon et al 2013 -->

← Older revision		Revision as of 10:17, 25 March 2021
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	===Simulation of dynamic responses at metabolic level induced by incremental test and high-intensity intermittent exercises===		===Simulation of dynamic responses at metabolic level induced by incremental test and high-intensity intermittent exercises===

−	<p align=justify> To validate the model we investigated a dynamic behaviour of the system in response to diverse single-bout aerobic exercises and compared them with published experimental data. Initially, we quantitatively estimated biochemical responses of essential metabolic variables (ATP, ADP, PCr, lactate concentrations and pH in muscle fibers type I and II) in the incremental ramp test which is a commonly used approach to evaluate aerobic capacity. In our simulation, muscle fibres type I are recruited after the beginning of exercise, while fibre type II - only at power over 24% of VO2max (6 min after exercise onset, Fig. 1A). Increasing the power during the test effects on various physiological variables like the volume of muscle containing fibre type I and/or II, blood flow as well as of transport and metabolic fluxes in both fibre types (Fig. 1). The model simulations reasonably well correspond to experimental measurements (Roussel et al., 2003; Greiner et al., 2007; Cannon et al., 2013) <cite>1</cite><cite>2</cite> <cite>3</cite> obtained in studies with the exercise mode (Fig. 2). However, it is worth to note that the current version of the model does not take into account an effect of the muscle exhaustion during the incremental ramp test which is physiologically observed and provides a direction for the model improvements.</p>	+	<p align=justify> To validate the model we investigated a dynamic behaviour of the system in response to diverse single-bout aerobic exercises and compared them with published experimental data. Initially, we quantitatively estimated biochemical responses of essential metabolic variables (ATP, ADP, PCr, lactate concentrations and pH in muscle fibers type I and II) in the incremental ramp test which is a commonly used approach to evaluate aerobic capacity. In our simulation, muscle fibres type I are recruited after the beginning of exercise, while fibre type II - only at power over 24% of VO2max (6 min after exercise onset, Fig. 1A). Increasing the power during the test effects on various physiological variables like the volume of muscle containing fibre type I and/or II, blood flow as well as of transport and metabolic fluxes in both fibre types (Fig. 1). The model simulations reasonably well correspond to experimental measurements (Roussel et al., 2003; Greiner et al., 2007; Cannon et al., 2013)<cite>1</cite><cite>2</cite><cite>3</cite> obtained in studies with the exercise mode (Fig. 2). However, it is worth to note that the current version of the model does not take into account an effect of the muscle exhaustion during the incremental ramp test which is physiologically observed and provides a direction for the model improvements.</p>

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 ===References===
 <biblio>
 #1 pmid=31131402	<!-- Kolpakov et al 2019 -->

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 ===The Integrated modular model comprises three hierarchical levels (metabolic, signaling pathways and regulation of gene expression)===
-<p align=justify> We have previously developed a multi-compartmental mathematical model describing the dynamics of intracellular species concentrations and fluxes in human muscle at rest and intracellular metabolic rearrangements in exercising skeletal muscles during an aerobic exercise on a cycle ergometer (Kiselev et al., 2019)<cite>11</cite>. As an initial model for this study, we have used a more complex model of energy metabolism in the human skeletal muscle developed by Li and coauthors and considered two types of the muscle fibers (Li et al., 2012). We have proposed a modular representation of the complex model using BioUML platform (Kolpakov et al., 2019). The modular representation provides the possibility of rapid expansion and modification of the model compartments to account for the complex organization of muscle cells and the limitations of the rate of diffusion of metabolites between intracellular compartments. This feature allowed us to integrate modules of signalling pathways modulating downstream regulatory processes of early response and genes with delayed response during the exercise and recovery. The parametric fitting of the modular model to published experimental data (Krustrup et al., 2004; Greiner et al., 2007; Barker et al., 2008; Cannon et al., 2014; Bartlett et al., 2020) showed the validity of the modular modeling approach implemented in BioUML. Furthermore, the integrated modular model provides an absolutely novel in silico platform to predict molecular responses of the human skeletal muscle cells to diverse modes of the exercise on levels of metabolic, signaling pathways and gene expression regulation experimental precise measurements of which are currently methodologically limited or even remain elusive.</p>
+<p align=justify> We have previously developed a multi-compartmental mathematical model describing the dynamics of intracellular species concentrations and fluxes in human muscle at rest and intracellular metabolic rearrangements in exercising skeletal muscles during an aerobic exercise on a cycle ergometer (Kiselev et al., 2019)<cite>12</cite>. As an initial model for this study, we have used a more complex model of energy metabolism in the human skeletal muscle developed by Li and coauthors and considered two types of the muscle fibers (Li et al., 2012)<cite>13</cite>. We have proposed a modular representation of the complex model using BioUML platform (Kolpakov et al., 2019)<cite>14</cite>. The modular representation provides the possibility of rapid expansion and modification of the model compartments to account for the complex organization of muscle cells and the limitations of the rate of diffusion of metabolites between intracellular compartments. This feature allowed us to integrate modules of signalling pathways modulating downstream regulatory processes of early response and genes with delayed response during the exercise and recovery. The parametric fitting of the modular model to published experimental data (Krustrup et al., 2004; Greiner et al., 2007; Barker et al., 2008; Cannon et al., 2014; Bartlett et al., 2020)<cite>4</cite><cite>2</cite><cite>5</cite><cite>6</cite><cite>7</cite> showed the validity of the modular modeling approach implemented in BioUML. Furthermore, the integrated modular model provides an absolutely novel in silico platform to predict molecular responses of the human skeletal muscle cells to diverse modes of the exercise on levels of metabolic, signaling pathways and gene expression regulation experimental precise measurements of which are currently methodologically limited or even remain elusive.</p>
 ===Model constraints and further ways for the development===
-<p align=justify> GS activity is regulated through multiple mechanisms, including feedbacks mediated by glycogen, blood glucose concentration,rate of glucose uptake, insulin, epinephrine and GS phosphorylation state (Jensen & Lai, 2009; Prats et al., 2009; Jensen et al., 2012b; Palm et al., 2013). However, in the current model GS activity depends on glycogen content only. In our model the post-exercise glycogen synthesis is lower than estimated in the majority of studies (Casey et al., 1995; Jentjens & Jeukendrup, 2003; Burke et al., 2017) because many factors are omitted, such as feeding and associated rises in blood glucose concentration, rate of glucose uptake, sensitivity to and changes in insulin, etc. At the same time, glycogen synthesis is higher than observed during exercise recovery in a fasted state (Maehlum & Hermansen, 1978).</p>
+<p align=justify> GS activity is regulated through multiple mechanisms, including feedbacks mediated by glycogen, blood glucose concentration,rate of glucose uptake, insulin, epinephrine and GS phosphorylation state (Jensen & Lai, 2009; Prats et al., 2009; Jensen et al., 2012b; Palm et al., 2013)<cite>15</cite><cite>16</cite><cite>17</cite><cite>18</cite>. However, in the current model GS activity depends on glycogen content only. In our model, the post-exercise glycogen synthesis is lower than estimated in the majority of studies (Casey et al., 1995; Jentjens & Jeukendrup, 2003; Burke et al., 2017)<cite>19</cite><cite>20</cite><cite>21</cite> because many factors are omitted, such as feeding and associated rises in blood glucose concentration, rate of glucose uptake, sensitivity to and changes in insulin, etc. At the same time, glycogen synthesis is higher than observed during exercise recovery in a fasted state (Maehlum & Hermansen, 1978)<cite>22</cite>.</p>
 ===References===

← Older revision		Revision as of 09:57, 25 March 2021
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−	<p align=justify> Development of the integrated model incorporating signaling and gene expression modules enables to simulate expression dynamics of early and delayed response genes on mRNA levels (Fig. 6). Numerical analysis of the model enabled to reveal crucial steps in this signal transduction pathway for the adaptation and demonstrated the necessity of consideration of additional transcription factors modulating transcription of genes with delayed response in order to adequately reproduce gene expression data that were taken in human vastus lateralis muscle during and after acute cycling exercise (Fig. 6C-D). Bioinformatics analysis of the original transcriptomics data, in turn, proposed that CREB-like proteins from FOS and JUN families forming heterodimer complexes with transcription factor CREB1 are indeed these intermediate regulators of genes with delayed response (Akberdin et al., 2020).</p>	+	<p align=justify> Development of the integrated model incorporating signaling and gene expression modules enables to simulate expression dynamics of early and delayed response genes on mRNA levels (Fig. 6). Numerical analysis of the model enabled to reveal crucial steps in this signal transduction pathway for the adaptation and demonstrated the necessity of consideration of additional transcription factors modulating transcription of genes with delayed response in order to adequately reproduce gene expression data that were taken in human vastus lateralis muscle during and after acute cycling exercise (Fig. 6C-D). Bioinformatics analysis of the original transcriptomics data, in turn, proposed that CREB-like proteins from FOS and JUN families forming heterodimer complexes with transcription factor CREB1 are indeed these intermediate regulators of genes with delayed response (Akberdin et al., 2020)<cite>11</cite>.</p>

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    Fig 5.png|<br><span style="font-size: 90%; align="right"">'''Figure 5.''' Simulation results for low (50% VO2max, A-F) and moderate intensity (70% VO2max, G-L) continuous exercises. (A,G) Exercise power and fibers recruitment pattern: total power, W (orange), power generated by type I (red) and II (blue) fibers; (B,H) ATP concentrations in type I (red, dotted) and II (blue, dotted) fibers and in the muscle tissue, mmol/kg ww (orange, solid); (C,I) ADP concentrations in type I (red, dotted) and II (blue, dotted) fibers and in the muscle tissue, mmol/kg ww (orange, solid); (D,J) PCr concentrations in type I (red, dotted) and II (blue, dotted) fibers and in the muscle tissue, mmol/kg ww (orange, solid); (E,K) Lactate concentrations in type I (red, dotted) and II (blue, dotted) fibers and in the muscle tissue, mmol/kg ww (orange, solid); (F,L) pH changes in type I (red, dotted) and II (blue, dotted) fibers and in the muscle tissue, mmol/kg ww (orange, solid).</span>
 </gallery>

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    Fig 2.png|<br><span style="font-size: 90%; align="right"">'''Figure 2.''' Model validation. The model simulations were compared to experimental data collected from incremental ramp and low intensity, moderate intensity exercises. (A) Model-predicted dynamic changes of PCr concentration in muscle tissue cells during incremental (blue line), low intensity(green line) and moderate intensity (red line) exercises (axis X - time of the exercise in minutes) and experimental measurements of PCr concentration changes during incremental (blue dots, Greiner et al., 2007)<cite>2</cite>, low-intensity (dark red dots  (Krustrup et al., 2004)<cite>4</cite>, green dots (Barker et al., 2008)<cite>5</cite>, pink dots (Cannon et al., 2014)<cite>6</cite>) and high-intensity (dark pink dots (Cannon et al., 2014)<cite>6</cite> exercises; (B) Model-predicted dynamic changes of lactate concentration in muscle tissue cells during incremental (blue line), low intensity(green line) and moderate intensity (red line) exercises (axis X - time of the exercise in minutes) and experimental measurements of lactate concentration changes during low-intensity (dark red dots  (Krustrup et al., 2004)<cite>4</cite>) exercise; (C) Model-predicted dynamic changes of ATP concentration in muscle tissue cells during incremental (blue line), low intensity(green line) and moderate intensity (red line) exercises (axis X - time of the exercise in minutes) and experimental measurements of ATP concentration changes during low-intensity (dark red dots  (Krustrup et al., 2004)<cite>4</cite>) and all-out intensity (red dots (Bartlett et al., 2020)<cite>7</cite>) exercises; (D) Model-predicted dynamic changes of pH in muscle tissue cells during incremental (blue line), low intensity(green line) and moderate intensity (red line) exercises (axis X - time of the exercise in minutes) and experimental measurements of ATP concentration changes during low-intensity (dark red dots (means ± SD)  (Krustrup et al., 2004)<cite>4</cite>) exercise.</span>
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    Fig 4.png|<br><span style="font-size: 90%; align="right"">'''Figure 4.''' Simulation results for high-intensity intermittent exercise (Kappenstein et al., 2013)<cite>9</cite>. (A, C, E) Exercise power and fibers recruitment pattern: total power (A: Wmax=250; C: Wmax=500; E: Wmax=500), W (orange), power generated by type I (red, A-C: Wmax=125) and II (blue, A: Wmax=125; C: Wmax=375;  E: Wmax=375 and successive decline) fibers; (B) PCr concentration (mmol/kg ww) in Type I (red) and Type II fibers (blue) with total power at Wmax=250; (D) PCr concentration (mmol/kg ww) in Type I (red) and Type II fibers (blue) with total power at Wmax=500; (F) PCr concentration (mmol/kg ww) in Type I (red) and Type II fibers (blue) with total power at Wmax=500 and successive decline of the power on each bout of 30-s exercise.</span>
 </gallery>
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-|<gallery mode="packed" heights="600px">
+|<gallery mode="packed" heights="1400px">
    Fig 5.png|<br><span style="font-size: 90%; align="right"">'''Figure 5.''' Simulation results for low (50% VO2max, A-F) and moderate intensity (70% VO2max, G-L) continuous exercises. (A,G) Exercise power and fibers recruitment pattern: total power, W (orange), power generated by type I (red) and II (blue) fibers; (B,H) ATP concentrations in type I (red, dotted) and II (blue, dotted) fibers and in the muscle tissue, mmol/kg ww (orange, solid); (C,I) ADP concentrations in type I (red, dotted) and II (blue, dotted) fibers and in the muscle tissue, mmol/kg ww (orange, solid); (D,J) PCr concentrations in type I (red, dotted) and II (blue, dotted) fibers and in the muscle tissue, mmol/kg ww (orange, solid); (E,K) Lactate concentrations in type I (red, dotted) and II (blue, dotted) fibers and in the muscle tissue, mmol/kg ww (orange, solid); (F,L) pH changes in type I (red, dotted) and II (blue, dotted) fibers and in the muscle tissue, mmol/kg ww (orange, solid).</span>
 </gallery>
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    Fig 6.png|<br><span style="font-size: 90%; align="right"">'''Figure 6.''' Simulation results for moderate intensity (70% VO2max) continuous exercises without (A-B) and with (C-D) intermediate X factor regulating expression of PPARGC1A gene. Expression of (A,C) NR4A3 (solid), NR4A2 (dashed), PPARGC1A (dot-dashed) in Type I fibers, (B,D) the same indexes in type II fibers (in Fold changes).</span>
 </gallery>