Mathematical Model of Dynamic Protein Interactions Regulating p53 Protein Stability for Tumor Suppression

In the field of cancer biology, numerous genes or proteins form extremely complex regulatory network, which determines cancer cell fate and cancer cell survival. p53 is a major tumor suppressor that is lost in more than 50% of human cancers. It has been well known that a variety of proteins regulate...

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Main Authors: Hua Wang, Guang Peng
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:Computational and Mathematical Methods in Medicine
Online Access:http://dx.doi.org/10.1155/2013/358980
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spelling doaj-ad98aa65133649feab78c8b117447c7b2020-11-24T22:51:09ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182013-01-01201310.1155/2013/358980358980Mathematical Model of Dynamic Protein Interactions Regulating p53 Protein Stability for Tumor SuppressionHua Wang0Guang Peng1Mathematical Sciences, Georgia Southern University, Statesboro, GA 30458, USADepartment of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USAIn the field of cancer biology, numerous genes or proteins form extremely complex regulatory network, which determines cancer cell fate and cancer cell survival. p53 is a major tumor suppressor that is lost in more than 50% of human cancers. It has been well known that a variety of proteins regulate its protein stability, which is essential for its tumor suppressive function. It remains elusive how we could understand and target p53 stabilization process through network analysis. In this paper we discuss the use of random walk and stationary distribution to measure the compound effect of a network of genes or proteins. This method is applied to the network of nine proteins that influence the protein stability of p53 via regulating the interaction between p53 and its regulator MDM2. Our study identifies that some proteins such as HDAC1 in the network of p53 regulators may have more profound effects on p53 stability, agreeing with the established findings on HDAC1. This work shows the importance of using mathematical analysis to dissect the complexity of biology networks in cancer.http://dx.doi.org/10.1155/2013/358980
collection DOAJ
language English
format Article
sources DOAJ
author Hua Wang
Guang Peng
spellingShingle Hua Wang
Guang Peng
Mathematical Model of Dynamic Protein Interactions Regulating p53 Protein Stability for Tumor Suppression
Computational and Mathematical Methods in Medicine
author_facet Hua Wang
Guang Peng
author_sort Hua Wang
title Mathematical Model of Dynamic Protein Interactions Regulating p53 Protein Stability for Tumor Suppression
title_short Mathematical Model of Dynamic Protein Interactions Regulating p53 Protein Stability for Tumor Suppression
title_full Mathematical Model of Dynamic Protein Interactions Regulating p53 Protein Stability for Tumor Suppression
title_fullStr Mathematical Model of Dynamic Protein Interactions Regulating p53 Protein Stability for Tumor Suppression
title_full_unstemmed Mathematical Model of Dynamic Protein Interactions Regulating p53 Protein Stability for Tumor Suppression
title_sort mathematical model of dynamic protein interactions regulating p53 protein stability for tumor suppression
publisher Hindawi Limited
series Computational and Mathematical Methods in Medicine
issn 1748-670X
1748-6718
publishDate 2013-01-01
description In the field of cancer biology, numerous genes or proteins form extremely complex regulatory network, which determines cancer cell fate and cancer cell survival. p53 is a major tumor suppressor that is lost in more than 50% of human cancers. It has been well known that a variety of proteins regulate its protein stability, which is essential for its tumor suppressive function. It remains elusive how we could understand and target p53 stabilization process through network analysis. In this paper we discuss the use of random walk and stationary distribution to measure the compound effect of a network of genes or proteins. This method is applied to the network of nine proteins that influence the protein stability of p53 via regulating the interaction between p53 and its regulator MDM2. Our study identifies that some proteins such as HDAC1 in the network of p53 regulators may have more profound effects on p53 stability, agreeing with the established findings on HDAC1. This work shows the importance of using mathematical analysis to dissect the complexity of biology networks in cancer.
url http://dx.doi.org/10.1155/2013/358980
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AT guangpeng mathematicalmodelofdynamicproteininteractionsregulatingp53proteinstabilityfortumorsuppression
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