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Linking myometrial physiology to intrauterine pressure; how tissue-level contractions create uterine contractions of labor.

Young RC, Barendse P - PLoS Comput. Biol. (2014)

Bottom Line: Other input variables are: starting and minimum pressure, burst and refractory period durations, enhanced contractile activity during an electrical burst, and reduced activity during the refractory period.The complex effects of nifedipine and oxytocin exposure are simulated.However, instead of classifying the rules, biological CAs should classify the set of input values for the rules that describe the relevant biology.

View Article: PubMed Central - PubMed

Affiliation: Department of Obstetrics and Gynecology, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America.

ABSTRACT
The mechanisms used to coordinate uterine contractions are not known. We develop a new model based on the proposal that there is a maximum distance to which action potentials can propagate in the uterine wall. This establishes "regions", where one action potential burst can rapidly recruit all the tissue. Regions are recruited into an organ-level contraction via a stretch-initiated contraction mechanism (myometrial myogenic response). Each uterine contraction begins with a regional contraction, which slightly increases intrauterine pressure. Higher pressure raises tension throughout the uterine wall, which initiates contractions of more regions and further increases pressure. The positive feedback synchronizes regional contractions into an organ-level contraction. Cellular automaton (CA) simulations are performed with Mathematica. Each "cell" is a region that is assigned an action potential threshold. An anatomy sensitivity factor converts intrauterine pressure to regional tension through the Law of Laplace. A regional contraction occurs when regional tension exceeds regional threshold. Other input variables are: starting and minimum pressure, burst and refractory period durations, enhanced contractile activity during an electrical burst, and reduced activity during the refractory period. Complex patterns of pressure development are seen that mimic the contraction patterns observed in laboring women. Emergent behavior is observed, including global synchronization, multiple pace making regions, and system memory of prior conditions. The complex effects of nifedipine and oxytocin exposure are simulated. The force produced can vary as a nonlinear function of the number of regions. The simulation directly links tissue-level physiology to human labor. The concept of a uterine pacemaker is re-evaluated because pace making activity may occur well before expression of a contraction. We propose a new classification system for biological CAs that parallels the 4-class system of Wolfram. However, instead of classifying the rules, biological CAs should classify the set of input values for the rules that describe the relevant biology.

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Related in: MedlinePlus

pseudo-Montevideo units are calculated from peak pressures and the total number of contractions expressed in 300 time steps.The number of rows (i) and columns (j) is varied, but otherwise the input values are the same as in Fig. 3B. As the total number of regions changes, the pseudo-Montevideo units (solid line) vary in a complex manner, expressing a peak between 18 and 30 regions. The mean of the total sensitivity (filled circles) is approximated from the anatomy sensitivity and the action potential threshold of all the regions. Because these values are pseudorandomly selected for each region, there is a slight variation of the mean total sensitivity, especially for 4 to 8 regions. But for 10 regions and above, the fall in pseudo-Montevideo units cannot be explained by changes of the total sensitivity.
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pcbi-1003850-g008: pseudo-Montevideo units are calculated from peak pressures and the total number of contractions expressed in 300 time steps.The number of rows (i) and columns (j) is varied, but otherwise the input values are the same as in Fig. 3B. As the total number of regions changes, the pseudo-Montevideo units (solid line) vary in a complex manner, expressing a peak between 18 and 30 regions. The mean of the total sensitivity (filled circles) is approximated from the anatomy sensitivity and the action potential threshold of all the regions. Because these values are pseudorandomly selected for each region, there is a slight variation of the mean total sensitivity, especially for 4 to 8 regions. But for 10 regions and above, the fall in pseudo-Montevideo units cannot be explained by changes of the total sensitivity.

Mentions: To build the model that incorporates both action potential propagation and mechanotransduction, we make one key assumption. That is, there is an upper limit on the distance each tissue-level action potential can travel. If true, this limitation defines functional regions of the uterine wall. The SQUID array synchronization analysis provides some evidence for the existence of these regions, and an approximate size of 8 cm×8 cm (reference 12, figure 8). Within regions, we assume, tissue recruitment is entirely by action potential propagation.


Linking myometrial physiology to intrauterine pressure; how tissue-level contractions create uterine contractions of labor.

Young RC, Barendse P - PLoS Comput. Biol. (2014)

pseudo-Montevideo units are calculated from peak pressures and the total number of contractions expressed in 300 time steps.The number of rows (i) and columns (j) is varied, but otherwise the input values are the same as in Fig. 3B. As the total number of regions changes, the pseudo-Montevideo units (solid line) vary in a complex manner, expressing a peak between 18 and 30 regions. The mean of the total sensitivity (filled circles) is approximated from the anatomy sensitivity and the action potential threshold of all the regions. Because these values are pseudorandomly selected for each region, there is a slight variation of the mean total sensitivity, especially for 4 to 8 regions. But for 10 regions and above, the fall in pseudo-Montevideo units cannot be explained by changes of the total sensitivity.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4199482&req=5

pcbi-1003850-g008: pseudo-Montevideo units are calculated from peak pressures and the total number of contractions expressed in 300 time steps.The number of rows (i) and columns (j) is varied, but otherwise the input values are the same as in Fig. 3B. As the total number of regions changes, the pseudo-Montevideo units (solid line) vary in a complex manner, expressing a peak between 18 and 30 regions. The mean of the total sensitivity (filled circles) is approximated from the anatomy sensitivity and the action potential threshold of all the regions. Because these values are pseudorandomly selected for each region, there is a slight variation of the mean total sensitivity, especially for 4 to 8 regions. But for 10 regions and above, the fall in pseudo-Montevideo units cannot be explained by changes of the total sensitivity.
Mentions: To build the model that incorporates both action potential propagation and mechanotransduction, we make one key assumption. That is, there is an upper limit on the distance each tissue-level action potential can travel. If true, this limitation defines functional regions of the uterine wall. The SQUID array synchronization analysis provides some evidence for the existence of these regions, and an approximate size of 8 cm×8 cm (reference 12, figure 8). Within regions, we assume, tissue recruitment is entirely by action potential propagation.

Bottom Line: Other input variables are: starting and minimum pressure, burst and refractory period durations, enhanced contractile activity during an electrical burst, and reduced activity during the refractory period.The complex effects of nifedipine and oxytocin exposure are simulated.However, instead of classifying the rules, biological CAs should classify the set of input values for the rules that describe the relevant biology.

View Article: PubMed Central - PubMed

Affiliation: Department of Obstetrics and Gynecology, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America.

ABSTRACT
The mechanisms used to coordinate uterine contractions are not known. We develop a new model based on the proposal that there is a maximum distance to which action potentials can propagate in the uterine wall. This establishes "regions", where one action potential burst can rapidly recruit all the tissue. Regions are recruited into an organ-level contraction via a stretch-initiated contraction mechanism (myometrial myogenic response). Each uterine contraction begins with a regional contraction, which slightly increases intrauterine pressure. Higher pressure raises tension throughout the uterine wall, which initiates contractions of more regions and further increases pressure. The positive feedback synchronizes regional contractions into an organ-level contraction. Cellular automaton (CA) simulations are performed with Mathematica. Each "cell" is a region that is assigned an action potential threshold. An anatomy sensitivity factor converts intrauterine pressure to regional tension through the Law of Laplace. A regional contraction occurs when regional tension exceeds regional threshold. Other input variables are: starting and minimum pressure, burst and refractory period durations, enhanced contractile activity during an electrical burst, and reduced activity during the refractory period. Complex patterns of pressure development are seen that mimic the contraction patterns observed in laboring women. Emergent behavior is observed, including global synchronization, multiple pace making regions, and system memory of prior conditions. The complex effects of nifedipine and oxytocin exposure are simulated. The force produced can vary as a nonlinear function of the number of regions. The simulation directly links tissue-level physiology to human labor. The concept of a uterine pacemaker is re-evaluated because pace making activity may occur well before expression of a contraction. We propose a new classification system for biological CAs that parallels the 4-class system of Wolfram. However, instead of classifying the rules, biological CAs should classify the set of input values for the rules that describe the relevant biology.

Show MeSH
Related in: MedlinePlus