Computational Modeling of PI3K/AKT and MAPK Signaling Pathways in Melanoma Cancer.
BACKGROUND:Malignant melanoma is an aggressive tumor of the skin and seems to be resistant to current therapeutic approaches. Melanocytic transformation is thought to occur by sequential accumulation of genetic and molecular alterations able to activate the Ras/Raf/MEK/ERK (MAPK) and/or the PI3K/AKT...
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doaj-be399df6fffe4d2f87c7277baad11fad2020-11-25T01:26:48ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01113e015210410.1371/journal.pone.0152104Computational Modeling of PI3K/AKT and MAPK Signaling Pathways in Melanoma Cancer.Francesco PappalardoGiulia RussoSaverio CandidoMarzio PennisiSalvatore CavalieriSanto MottaJames A McCubreyFerdinando NicolettiMassimo LibraBACKGROUND:Malignant melanoma is an aggressive tumor of the skin and seems to be resistant to current therapeutic approaches. Melanocytic transformation is thought to occur by sequential accumulation of genetic and molecular alterations able to activate the Ras/Raf/MEK/ERK (MAPK) and/or the PI3K/AKT (AKT) signalling pathways. Specifically, mutations of B-RAF activate MAPK pathway resulting in cell cycle progression and apoptosis prevention. According to these findings, MAPK and AKT pathways may represent promising therapeutic targets for an otherwise devastating disease. RESULT:Here we show a computational model able to simulate the main biochemical and metabolic interactions in the PI3K/AKT and MAPK pathways potentially involved in melanoma development. Overall, this computational approach may accelerate the drug discovery process and encourages the identification of novel pathway activators with consequent development of novel antioncogenic compounds to overcome tumor cell resistance to conventional therapeutic agents. The source code of the various versions of the model are available as S1 Archive.http://europepmc.org/articles/PMC4807832?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Francesco Pappalardo Giulia Russo Saverio Candido Marzio Pennisi Salvatore Cavalieri Santo Motta James A McCubrey Ferdinando Nicoletti Massimo Libra |
spellingShingle |
Francesco Pappalardo Giulia Russo Saverio Candido Marzio Pennisi Salvatore Cavalieri Santo Motta James A McCubrey Ferdinando Nicoletti Massimo Libra Computational Modeling of PI3K/AKT and MAPK Signaling Pathways in Melanoma Cancer. PLoS ONE |
author_facet |
Francesco Pappalardo Giulia Russo Saverio Candido Marzio Pennisi Salvatore Cavalieri Santo Motta James A McCubrey Ferdinando Nicoletti Massimo Libra |
author_sort |
Francesco Pappalardo |
title |
Computational Modeling of PI3K/AKT and MAPK Signaling Pathways in Melanoma Cancer. |
title_short |
Computational Modeling of PI3K/AKT and MAPK Signaling Pathways in Melanoma Cancer. |
title_full |
Computational Modeling of PI3K/AKT and MAPK Signaling Pathways in Melanoma Cancer. |
title_fullStr |
Computational Modeling of PI3K/AKT and MAPK Signaling Pathways in Melanoma Cancer. |
title_full_unstemmed |
Computational Modeling of PI3K/AKT and MAPK Signaling Pathways in Melanoma Cancer. |
title_sort |
computational modeling of pi3k/akt and mapk signaling pathways in melanoma cancer. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2016-01-01 |
description |
BACKGROUND:Malignant melanoma is an aggressive tumor of the skin and seems to be resistant to current therapeutic approaches. Melanocytic transformation is thought to occur by sequential accumulation of genetic and molecular alterations able to activate the Ras/Raf/MEK/ERK (MAPK) and/or the PI3K/AKT (AKT) signalling pathways. Specifically, mutations of B-RAF activate MAPK pathway resulting in cell cycle progression and apoptosis prevention. According to these findings, MAPK and AKT pathways may represent promising therapeutic targets for an otherwise devastating disease. RESULT:Here we show a computational model able to simulate the main biochemical and metabolic interactions in the PI3K/AKT and MAPK pathways potentially involved in melanoma development. Overall, this computational approach may accelerate the drug discovery process and encourages the identification of novel pathway activators with consequent development of novel antioncogenic compounds to overcome tumor cell resistance to conventional therapeutic agents. The source code of the various versions of the model are available as S1 Archive. |
url |
http://europepmc.org/articles/PMC4807832?pdf=render |
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