Data driven model discovery and interpretation for CAR T-cell killing using sparse identification and latent variables.

ABSTRACT: In the development of cell-based cancer therapies, quantitative mathematical models of cellular interactions are instrumental in understanding treatment efficacy. Efforts to validate and interpret mathematical models of cancer cell growth and death hinge first on proposing a precise mathematical model, then analyzing experimental data in the context of the chosen model. In this work, we present the first application of the sparse identification of non-linear dynamics (SINDy) algorithm to a real biological system in order discover cell-cell interaction dynamics in in vitro experimental data, using chimeric antigen receptor (CAR) T-cells and patient-derived glioblastoma cells. By combining the techniques of latent variable analysis and SINDy, we infer key aspects of the interaction dy

SUBMITTER: Brummer AB 

PROVIDER: S-EPMC10226275 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature