Bayesian dynamic model selection of non-isolated cell signaling pathways

• Grant number: 19/24580-2
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• Start date: October 01, 2021
• End date: January 31, 2025
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Marcelo da Silva Reis
• Grantee: Ronaldo Nogueira de Sousa
• 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

A cell signaling pathway is composed of a set of sequential chemical reactions that take place within the cell. The information is transmitted throughout that pathway through concentration changes in the involved chemical species. The dynamics of those pathways can be studied using ordinary differential equation-based models that describe reaction kinetics. Such models should be calibrated using experimental measurements of one or more chemical species and evaluated according to a given criterion, thus allowing us to select a model that explains well the experiments. To this end, Bayesian methods are interesting criteria, since they assign a posterior probability of a model given experimental measurements, which enable us to choose a model with highest posterior probability. However, model selection methods in general assume that the modeled signaling pathway is a reasonably isolated system, a hypothesis that often is not true and might lead to model estimation errors. Therefore, in this project we propose the development, implementation and validation of a model selection method that takes into account the lack of isolation that is intrinsic to cuts of cell signaling pathways. To this end, we will depart from Bayesian computation methods that are already available, and will also use the reduction of the model selection problem to the feature selection problem that was recently developed by our group. Finally, we will test the new method on artificial instances and also on experimental measurements that are yielded in cancer cells studies at our lab. We expect that the proposed method will become a relevant tool in the studying of cell signaling pathways in many biological contexts, specially in Cancer Biology studies. (AU)

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)

SOUSA, RONALDO N.; CAMPOS, CRISTIANO G. S.; WANG, WILLIAN; HASHIMOTO, RONALDO F.; ARMELIN, HUGO A.; REIS, MARCELO S.. Exploring Identifiability in Hybrid Models of Cell Signaling Pathways. ADVANCES IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, BSB 2023, v. 13954, p. 12-pg., 2023-01-01. (19/24580-2, 13/07467-1, 21/04355-4, 15/22308-2, 19/21619-5)

MONTONI, FABIO; DE SOUSA, RONALDO N.; DE LIMA JUNIOR, MARCELO B.; CAMPOS, CRISTIANO G. S.; WANG, WILLIAN; CONSTANTINO, VIVIAN M.; SANCTOS, CASSIA S.; ARMELIN, HUGO A.; REIS, MARCELO S.; IEEE. Anguix: Cell Signaling Modeling Improvement through Sabio-RK association to Reactome. 2022 IEEE 18TH INTERNATIONAL CONFERENCE ON E-SCIENCE (ESCIENCE 2022), v. N/A, p. 2-pg., 2022-01-01. (20/08555-5, 13/07467-1, 21/04355-4, 20/10329-3, 19/24580-2, 19/21619-5)

BATISTA, MARCELO; MONTONI, FABIO; CAMPOS, CRISTIANO; NOGUEIRA, RONALDO; ARMELIN, HUGO A.; REIS, MARCELO S.. A Framework for Inference and Selection of Cell Signaling Pathway Dynamic Models. ADVANCES IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, BSB 2023, v. 13954, p. 12-pg., 2023-01-01. (20/10329-3, 19/24580-2, 13/07467-1, 21/04355-4, 20/08555-5, 19/21619-5)