An adapted particle swarm optimization algorithm as a model for exploring premyofibril formation

While the fundamental steps outlining myofibril formation share a similar scheme for different cell and species types, various granular details involved in the development of a functional contractile muscle are not well understood. Many studies of myofibrillogenesis focus on the protein interactions...

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Main Authors: William Sherman, Anna Grosberg
Format: Article
Language:English
Published: AIP Publishing LLC 2020-04-01
Series:AIP Advances
Online Access:http://dx.doi.org/10.1063/1.5145010
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spelling doaj-78e33f5ee029494287dda9db530053ca2020-11-25T04:05:15ZengAIP Publishing LLCAIP Advances2158-32262020-04-01104045126045126-1110.1063/1.5145010An adapted particle swarm optimization algorithm as a model for exploring premyofibril formationWilliam Sherman0Anna Grosberg1Center for Complex Biological Systems, University of California Irvine, Irvine, California 92697-2280, USACenter for Complex Biological Systems, University of California Irvine, Irvine, California 92697-2280, USAWhile the fundamental steps outlining myofibril formation share a similar scheme for different cell and species types, various granular details involved in the development of a functional contractile muscle are not well understood. Many studies of myofibrillogenesis focus on the protein interactions that are involved in myofibril maturation with the assumption that there is a fully formed premyofibril at the start of the process. However, there is little known regarding how the premyofibril is initially constructed. Fortunately, the protein α-actinin, which has been consistently identified throughout the maturation process, is found in premyofibrils as punctate aggregates known as z-bodies. We propose a theoretical model based on the particle swarm optimization algorithm that can explore how these α-actinin clusters form into the patterns observed experimentally. Our algorithm can produce different pattern configurations by manipulating specific parameters that can be related to α-actinin mobility and binding affinity. These patterns, which vary experimentally according to species and muscle cell type, speak to the versatility of α-actinin and demonstrate how its behavior may be altered through interactions with various regulatory, signaling, and metabolic proteins. The results of our simulations invite speculation that premyofibrils can be influenced toward developing different patterns by altering the behavior of individual α-actinin molecules, which may be linked to key differences present in different cell types.http://dx.doi.org/10.1063/1.5145010
collection DOAJ
language English
format Article
sources DOAJ
author William Sherman
Anna Grosberg
spellingShingle William Sherman
Anna Grosberg
An adapted particle swarm optimization algorithm as a model for exploring premyofibril formation
AIP Advances
author_facet William Sherman
Anna Grosberg
author_sort William Sherman
title An adapted particle swarm optimization algorithm as a model for exploring premyofibril formation
title_short An adapted particle swarm optimization algorithm as a model for exploring premyofibril formation
title_full An adapted particle swarm optimization algorithm as a model for exploring premyofibril formation
title_fullStr An adapted particle swarm optimization algorithm as a model for exploring premyofibril formation
title_full_unstemmed An adapted particle swarm optimization algorithm as a model for exploring premyofibril formation
title_sort adapted particle swarm optimization algorithm as a model for exploring premyofibril formation
publisher AIP Publishing LLC
series AIP Advances
issn 2158-3226
publishDate 2020-04-01
description While the fundamental steps outlining myofibril formation share a similar scheme for different cell and species types, various granular details involved in the development of a functional contractile muscle are not well understood. Many studies of myofibrillogenesis focus on the protein interactions that are involved in myofibril maturation with the assumption that there is a fully formed premyofibril at the start of the process. However, there is little known regarding how the premyofibril is initially constructed. Fortunately, the protein α-actinin, which has been consistently identified throughout the maturation process, is found in premyofibrils as punctate aggregates known as z-bodies. We propose a theoretical model based on the particle swarm optimization algorithm that can explore how these α-actinin clusters form into the patterns observed experimentally. Our algorithm can produce different pattern configurations by manipulating specific parameters that can be related to α-actinin mobility and binding affinity. These patterns, which vary experimentally according to species and muscle cell type, speak to the versatility of α-actinin and demonstrate how its behavior may be altered through interactions with various regulatory, signaling, and metabolic proteins. The results of our simulations invite speculation that premyofibrils can be influenced toward developing different patterns by altering the behavior of individual α-actinin molecules, which may be linked to key differences present in different cell types.
url http://dx.doi.org/10.1063/1.5145010
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