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An approximate Bayesian computation-based approach to tackle the lack of isolation problem in signaling pathway modeling

Grant number: 19/20025-4
Support Opportunities:Scholarships abroad - Research
Effective date (Start): January 13, 2020
Effective date (End): February 12, 2020
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Marcelo da Silva Reis
Grantee:Marcelo da Silva Reis
Host Investigator: Juliane Liepe
Host Institution: Instituto Butantan. Secretaria da Saúde (São Paulo - Estado). São Paulo , SP, Brazil
Research place: Max Planck Society, Gottingen, Germany  
Associated research grant:13/07467-1 - CeTICS - Center of Toxins, Immune-Response and Cell Signaling, AP.CEPID


Dynamic models based on ordinary differential equations (ODEs) are useful tools for investigation of cell signaling pathways. Such models allow us to make predictions about the pathway behavior under different stimuli. ODE-based models are often calibrated using experimental data obtained by probing one or more species at several time points after a given cell stimulation. To this end, Bayesian approaches such as the approximate Bayesian computation (ABC) are relevant tools, since they allow the assessment of the uncertainty in the estimation and also are a natural way to carry out model selection. In general, those models are estimated assuming that they are relatively isolated systems with known inputs; however, in many practical situations, we do not have any guarantee that that assumption holds. Therefore, in this project, we propose to tackle the lack of isolation problem in signaling pathway modeling through the development of a new ABC-based approach, which would take into account that model inputs also must be inferred. Preliminary tests with the new approach will be executed using the ABC-SysBio framework. Finally, we will discuss about how biological experiments must be performed in order to generate a more suitable data for the application of the new approach. We expect to incorporate results obtained in this project into ongoing projects at the CeTICS center, as well as to consolidate the collaboration with a renowned, international Systems Biology research group. (AU)

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