Gene target discovery with network analysis in Toxoplasma gondii
- Autores
- Alonso, Andrés Mariano; Corvi, Maria M.; Diambra, Luis Aníbal
- Año de publicación
- 2019
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Infectious diseases are of great relevance for global health, but needed drugs and vaccines have not been developed yet or are not effective in many cases. In fact, traditional scientific approaches with intense focus on individual genes or proteins have not been successful in providing new treatments. Hence, innovations in technology and computational methods provide new tools to further understand complex biological systems such as pathogens biology. In this work, we propose a system biology approach to analyze transcriptomic data of the parasite Toxoplasma gondii . By means of an optimization procedure, the phenotypic transitions between the stages associated with the life cycle of the parasite were embedded into the dynamics of a gene network model. Thus, through this methodology we were able to reconstruct a gene regulatory network able to emulate the life cycle of the pathogen. The community network analysis has revealed that nodes of the network can be organized in seven communities which allow us to assign putative functions to 339 previously uncharacterized genes, 25 of which are predicted as new pathogenic factors. Furthermore, we identified a small subnetwork module that controls the parasite9s life cycle. These new findings can contribute to understand of parasite pathogenesis.
Centro Regional de Estudios Genómicos - Materia
-
Ciencias Exactas
Biología
Toxoplasma gondii
pathogen biology
network analysis - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/138645
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Gene target discovery with network analysis in Toxoplasma gondiiAlonso, Andrés MarianoCorvi, Maria M.Diambra, Luis AníbalCiencias ExactasBiologíaToxoplasma gondiipathogen biologynetwork analysisInfectious diseases are of great relevance for global health, but needed drugs and vaccines have not been developed yet or are not effective in many cases. In fact, traditional scientific approaches with intense focus on individual genes or proteins have not been successful in providing new treatments. Hence, innovations in technology and computational methods provide new tools to further understand complex biological systems such as pathogens biology. In this work, we propose a system biology approach to analyze transcriptomic data of the parasite Toxoplasma gondii . By means of an optimization procedure, the phenotypic transitions between the stages associated with the life cycle of the parasite were embedded into the dynamics of a gene network model. Thus, through this methodology we were able to reconstruct a gene regulatory network able to emulate the life cycle of the pathogen. The community network analysis has revealed that nodes of the network can be organized in seven communities which allow us to assign putative functions to 339 previously uncharacterized genes, 25 of which are predicted as new pathogenic factors. Furthermore, we identified a small subnetwork module that controls the parasite9s life cycle. These new findings can contribute to understand of parasite pathogenesis.Centro Regional de Estudios Genómicos2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/138645enginfo:eu-repo/semantics/altIdentifier/issn/2045-2322info:eu-repo/semantics/altIdentifier/doi/10.1101/397398info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:31:52Zoai:sedici.unlp.edu.ar:10915/138645Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:31:52.259SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Gene target discovery with network analysis in Toxoplasma gondii |
title |
Gene target discovery with network analysis in Toxoplasma gondii |
spellingShingle |
Gene target discovery with network analysis in Toxoplasma gondii Alonso, Andrés Mariano Ciencias Exactas Biología Toxoplasma gondii pathogen biology network analysis |
title_short |
Gene target discovery with network analysis in Toxoplasma gondii |
title_full |
Gene target discovery with network analysis in Toxoplasma gondii |
title_fullStr |
Gene target discovery with network analysis in Toxoplasma gondii |
title_full_unstemmed |
Gene target discovery with network analysis in Toxoplasma gondii |
title_sort |
Gene target discovery with network analysis in Toxoplasma gondii |
dc.creator.none.fl_str_mv |
Alonso, Andrés Mariano Corvi, Maria M. Diambra, Luis Aníbal |
author |
Alonso, Andrés Mariano |
author_facet |
Alonso, Andrés Mariano Corvi, Maria M. Diambra, Luis Aníbal |
author_role |
author |
author2 |
Corvi, Maria M. Diambra, Luis Aníbal |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Exactas Biología Toxoplasma gondii pathogen biology network analysis |
topic |
Ciencias Exactas Biología Toxoplasma gondii pathogen biology network analysis |
dc.description.none.fl_txt_mv |
Infectious diseases are of great relevance for global health, but needed drugs and vaccines have not been developed yet or are not effective in many cases. In fact, traditional scientific approaches with intense focus on individual genes or proteins have not been successful in providing new treatments. Hence, innovations in technology and computational methods provide new tools to further understand complex biological systems such as pathogens biology. In this work, we propose a system biology approach to analyze transcriptomic data of the parasite Toxoplasma gondii . By means of an optimization procedure, the phenotypic transitions between the stages associated with the life cycle of the parasite were embedded into the dynamics of a gene network model. Thus, through this methodology we were able to reconstruct a gene regulatory network able to emulate the life cycle of the pathogen. The community network analysis has revealed that nodes of the network can be organized in seven communities which allow us to assign putative functions to 339 previously uncharacterized genes, 25 of which are predicted as new pathogenic factors. Furthermore, we identified a small subnetwork module that controls the parasite9s life cycle. These new findings can contribute to understand of parasite pathogenesis. Centro Regional de Estudios Genómicos |
description |
Infectious diseases are of great relevance for global health, but needed drugs and vaccines have not been developed yet or are not effective in many cases. In fact, traditional scientific approaches with intense focus on individual genes or proteins have not been successful in providing new treatments. Hence, innovations in technology and computational methods provide new tools to further understand complex biological systems such as pathogens biology. In this work, we propose a system biology approach to analyze transcriptomic data of the parasite Toxoplasma gondii . By means of an optimization procedure, the phenotypic transitions between the stages associated with the life cycle of the parasite were embedded into the dynamics of a gene network model. Thus, through this methodology we were able to reconstruct a gene regulatory network able to emulate the life cycle of the pathogen. The community network analysis has revealed that nodes of the network can be organized in seven communities which allow us to assign putative functions to 339 previously uncharacterized genes, 25 of which are predicted as new pathogenic factors. Furthermore, we identified a small subnetwork module that controls the parasite9s life cycle. These new findings can contribute to understand of parasite pathogenesis. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Articulo http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/138645 |
url |
http://sedici.unlp.edu.ar/handle/10915/138645 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/issn/2045-2322 info:eu-repo/semantics/altIdentifier/doi/10.1101/397398 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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openAccess |
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http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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application/pdf |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
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