Identification of cell signaling pathways based on biochemical reaction kinetics repositories

• Grant number: 17/20575-9
• Support Opportunities: Scholarships in Brazil - Master
• Start date: January 01, 2018
• End date: December 31, 2019
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Marcelo da Silva Reis
• Grantee: Gustavo Estrela de Matos
• Host Institution: Instituto Butantan. Secretaria da Saúde (São Paulo - Estado). São Paulo , SP, Brazil
• Associated research grant: 13/07467-1 - CeTICS - Center of Toxins, Immune-Response and Cell Signaling, AP.CEPID

Abstract

Cell signaling pathways are composed of a set of biochemical reactions that are associated with signal transmission within the cell and its surroundings. Traditionally, these pathways are identified through statistical analyses on results from biological assays, in which involved chemical species are quantified. However, once generally it is measured only a few time points for a fraction of the chemical species, to effectively tackle this problem it is required to design and simulate functional dynamic models. Recently, it was introduced a method to design functional models, which is based on systematic modifications of an initial model through the inclusion of biochemical reactions, which in turn were obtained from the interactome repository KEGG. Nevertheless, this method presents some shortcomings that impair the estimated model; among them are the incompleteness of the information extracted from KEGG, the absence of rate constants, the usage of sub-optimal search algorithms and an unsatisfactory overfitting penalization. In this project, we propose a new methodology for identification of cell signaling pathways, which will make use of a myriad of public interactome and biochemical reaction kinetics repositories to deal with the incompleteness of a priori information. Moreover, we will use optimal algorithms for model selection, as well as more effective cost functions for overfitting penalization. The new methodology will be tested on artificial instances and also on cell signaling pathways identification in our case study, the Y1 mouse adrenocortical tumor cell line. (AU)

Academic Publications

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

MATOS, Gustavo Estrela de. Identification of cell signaling pathways based on biochemical reaction kinetics repositories. 2021. Master's Dissertation - Universidade de São Paulo (USP). Instituto de Matemática e Estatística (IME/SBI) São Paulo.