Prediction of Escherichia coli phenotypes through biological networks and machine learning

• Grant number: 12/00741-8
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: April 01, 2012
• End date: January 31, 2015
• Field of knowledge: Biological Sciences - Biophysics - Biophysics of Processes and Systems
• Principal Investigator: Ney Lemke
• Grantee: Esther Camilo dos Reis
• Host Institution: Instituto de Biociências (IBB). Universidade Estadual Paulista (UNESP). Campus de Botucatu. Botucatu , SP, Brazil
• Associated scholarship(s): 12/22395-4 - Visualization of forests of trees, BE.EP.DR

Abstract

Cell differentiation, carcinogenesis, and gene essentiality are some examples of biological processes that can not be fully explained through the study of individual genes. There is now a concern of researchers in understanding how the various entities of a system interact and produce certain phenotypes using the techniques of systems biology. However, few of these approaches are genome scaled and consider the effects of environment on these networks. In this context, this project aims to predict the phenotypes of the bacterium Escherichia coli, that is, the evaluation of its growth profile in different conditions by metabolic flux balance analysis (FBA) and machine learning. The representation of the system is done by using graph where the interacting elements are represented by nodes and interactions by edges that connect pairs of nodes. From the network it will be extracted centralities data which together with the results of the FBA calculations will be used as attributes of training for the learning algorithm.

Scientific publications

(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)

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. (13/02018-4, 12/00741-8, 10/20684-3, 12/13450-1)

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. (13/02018-4, 12/00741-8, 10/20684-3, 12/13450-1)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

REIS, Esther Camilo dos. Predição de fenótipos de Escherichia coli através de redes biológicas e aprendizado de máquina. 2015. Doctoral Thesis - Universidade Estadual Paulista (Unesp). Instituto de Biociências. Botucatu Botucatu.