Identification of cell‐to‐cell heterogeneity through systems engineering approaches

Abstract

AbstractCells in a genetically homogeneous cell‐population exhibit a significant degree of heterogeneity in their responses to an external stimulus. To understand origins and importance of this heterogeneity, individual‐based population model (IBPM), where parameters follow probability density functions (PDFs) instead of being constants, has been previously developed. However, parameter identification for an IBPM is challenging as estimating PDFs is computationally expensive. Also, because of experimental limitations and nonlinearity of models, not all parameters' PDFs are identifiable. Motivated by the above considerations, a new methodology is proposed in this study. First, a subset of parameters whose PDFs is identifiable are determined through sensitivity analysis, and only these PDFs are estimated. Second, an artificial neural network model is developed to find an empirical relation between these parameter and output PDFs to reduce computational costs of the parameter identification. The proposed approach is validated by estimating PDFs of parameters of a tumor necrosis factor‐α signaling model.