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Development of machine learning approaches based on biological networks for prediction and determination of rules governing the emergence of phenotypes of interest

Grant number: 10/20684-3
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): April 01, 2011
Effective date (End): February 28, 2014
Field of knowledge:Biological Sciences - Biophysics
Principal Investigator:Ney Lemke
Grantee:Marcio Luis Acencio
Home Institution: Instituto de Biociências (IBB). Universidade Estadual Paulista (UNESP). Campus de Botucatu. Botucatu , SP, Brazil

Abstract

The reductionist approach has been effective in explaining the functioning of several biologicalprocesses. However, these processes have been shown to be extremely complex and to have emergent properties that cannot be explained, or even predicted, by such approach. To overcome these limitations, researches have adopted the systems biology approach, a new biology field that investigate how properties emerge from the nonlinear interaction of multiple components of biological processes. These interactions can be represented by a mathematical object called graph or network, where interacting elements are represented by nodes and interactions are represented by edges connecting pairs of nodes. Based on systems biology principles, we propose in this project (i) to construct a Saccharomyces cerevisiae integrated network of gene nteractions simultaneously containing experimentally verified protein physical interactions, metabolic interactions and transcriptional regulation interactions, (ii) to calculate the centrality measures of this network and (iii) to use these centrality measures for predicting and describing, by means of machine learning approaches, the essentiality of each S. cerevisiae's gene and gene interaction in different growth conditions and the associations between gene interactions and phenotypes or biological processes of interest. Moreover, we also propose to develop a machine learning and network-based method to build signaling pathway networks related to biological processes of interest in S. cerevisiae.

Scientific publications (6)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
POLLO-OLIVEIRA, LETICIA; POST, HARM; ACENCIO, MARCIO LUIS; LEMKE, NEY; VAN DEN TOORN, HENK; TRAGANTE, VINICIUS; HECK, ALBERT J. R.; ALTELAAR, A. F. MAARTEN; YATSUDA, ANA PATRICIA. Unravelling the Neospora caninum secretome through the secreted fraction (ESA) and quantification of the discharged tachyzoite using high-resolution mass spectrometry-based proteomics. PARASITES & VECTORS, v. 6, NOV 23 2013. Web of Science Citations: 8.
ACENCIO, MARCIO LUIS; BOVOLENTA, LUIZ AUGUSTO; CAMILO, ESTHER; LEMKE, NEY. Prediction of Oncogenic Interactions and Cancer-Related Signaling Networks Based on Network Topology. PLoS One, v. 8, n. 10 OCT 25 2013. Web of Science Citations: 5.
CAMILO, ESTHER; BOVOLENTA, LUIZ A.; ACENCIO, MARCIO L.; RYBARCZYK-FILHO, JOSE L.; CASTRO, MAURO A. A.; MOREIRA, JOSE C. F.; LEMKE, NEY. GALANT: a Cytoscape plugin for visualizing data as functional landscapes projected onto biological networks. Bioinformatics, v. 29, n. 19, p. 2505-2506, OCT 1 2013. Web of Science Citations: 3.
COSTA, PEDRO RAFAEL; ACENCIO, MARCIO LUIS; LEMKE, NEY. Cooperative RNA Polymerase Molecules Behavior on a Stochastic Sequence-Dependent Model for Transcription Elongation. PLoS One, v. 8, n. 2 FEB 21 2013. Web of Science Citations: 5.
BOVOLENTA, LUIZ A.; ACENCIO, MARCIO L.; LEMKE, NEY. HTRIdb: an open-access database for experimentally verified human transcriptional regulation interactions. BMC Genomics, v. 13, AUG 17 2012. Web of Science Citations: 113.
BOVOLENTA‚ L.; ACENCIO‚ M.; LEMKE‚ N. The development of an open-access database for human transcriptional regulation interactions. BMC Bioinformatics, v. 12, n. Suppl 11, p. A10, 2011.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.
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