Literature DB >> 19303886

Desynchronized vasomotion and desynchronized fiber activation pattern enhance oxygenation in a model of skeletal muscle.

Ranjan K Pradhan1, V S Chakravarthy.   

Abstract

Although the full physiological significance of vasomotion is still debated, it is generally thought to have a role in optimizing tissue oxygenation parameters. We study the effect of vasomotion rhythm in skeletal muscle on oxygen transport using a computational model. The model is used: (i) to test a novel hypothesis that "vasomotors" form a chemical network in which the rhythm adapts to meet oxygen demand in skeletal muscle and (ii) to study the contribution of desynchronized/chaotic vasomotion in optimizing oxygen delivery to skeletal muscle. We formulate a 2D grid model of skeletal muscle consisting of an interleaved arrangement of vessels and muscle fibers fired by a motor neuronal network. The vasomotors too form a network interacting by chemical means. When positive (negative) synapses dominate, the neuronal network exhibits synchronized (desynchronized) activity. Similarly, when positive (negative) chemical interactions dominate, the vessels exhibit synchronized (desynchronized) activity. Optimal oxygenation is observed when both neuronal network and vasomotor network exhibit desynchronous activity. Muscle oxygenation is thought to result by interactions between the fiber/neuron network and the vessel network; optimal oxygenation depends on precise rhythm-related conditions on the two networks. The model provides interesting insights into the phenomenon of muscle fatigue.

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Year:  2009        PMID: 19303886     DOI: 10.1016/j.jtbi.2009.02.026

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  2 in total

Review 1.  Modeling Ca2+ signaling in the microcirculation: intercellular communication and vasoreactivity.

Authors:  Adam Kapela; Sridevi Nagaraja; Jaimit Parikh; Nikolaos M Tsoukias
Journal:  Crit Rev Biomed Eng       Date:  2011

2.  Vascular Dynamics Aid a Coupled Neurovascular Network Learn Sparse Independent Features: A Computational Model.

Authors:  Ryan T Philips; Karishma Chhabria; V Srinivasa Chakravarthy
Journal:  Front Neural Circuits       Date:  2016-02-26       Impact factor: 3.492

  2 in total

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