A Framework for Inference and Selection of Cell Signaling Pathway Dynamic Models

• Marcelo Batista USP https://orcid.org/0000-0002-9311-0692
• Fabio Montoni USP / Instituto Butantan http://orcid.org/0000-0002-9266-1281
• Cristiano Campos UNICAMP http://orcid.org/0009-0002-2352-247X
• Ronaldo Nogueira USP http://orcid.org/0000-0001-8068-7735
• Hugo A. Armelin Instituto Butantan https://orcid.org/0000-0001-9557-0804
• Marcelo S. Reis Instituto Butantan / UNICAMP http://orcid.org/0000-0002-3754-9115

Resumo

Properly modeling the dynamics of cell signaling pathways requires several steps, such as selecting a subset of chemical reactions, mapping them into a mathematical model that deals with the communication of the pathway with the remainder of the cell (e.g., systems of universal differential equations - UDEs), inferring model parameters, and selecting the best model based on experimental data. However, this entire process can be extremely laborious and time-consuming for many researchers, as they often have to access different and complicated tools to achieve this goal. To address the challenges associated with this process in a more efficient way, we propose a framework that provides a streamlined approach tailored for universal differential equation UDE-based cell signaling pathway modeling. The open-source, free framework (github.com/Dynamic-Systems-Biology/BSB-2023-Framework) combines parameter inference algorithms, model selection techniques, and data importation from public repositories of biochemical reactions into a single tool. We provide an example of the usage of the proposed framework in a Julia Jupyter notebook. We expect that this streamlined approach will enable researchers to design improved cell signaling pathway models more easily, which may lead to new insights and discoveries in the study of biological mechanisms.

Palavras-chave: Cell signaling pathways, Parameter inference, Biochemical reactions, Universal differential equation

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