Natural killer (NK) cells are innate immune effector cells that play an immediate role in host defense. NK cells contain a variety of stimulatory pathways that induce the release of cytotoxic chemicals when activated. Mathematical modeling of such pathways can inform us about the magnitude and kinetics of activation. This, in turn, can provide insight for NK cell-based therapies. To quantitatively understand the differences between three main stimulatory pathways in NK cells (CD16, 2B4 and NKG2D), we developed a mathematical model that mechanistically describes their intracellular signaling dynamics. In particular, the model predicts the dynamics of the phosphorylated receptors, pSFK, pErk, pAkt and pPLC , as these species contribute to cell activation. The model was fit to published experimental data and validated with separate experimental measurements. Modeling simulations show that the CD16 pathway exhibits rapid activation kinetics for all species, and co-stimulation of CD16 with either NKG2D or 2B4 induces the greatest magnitude of activation. Moreover, the magnitude of cell activation under co-stimulation is more sensitive to CD16 stimulation than either 2B4 or NKG2D stimulation. Overall, the model predicts CD16 stimulation is more influential in cell activation. These modeling results complement ongoing experimental work that applies CD16 stimulation for therapeutic purposes.
IntroductionMathematical modeling of signal transduction pathways provides a framework that enables better cell engineering approaches. Indeed, many models have augmented our understanding of biological processes (Bellouquid and CH-Chaoui, 2014; Eftimie et al., 2016; Pappalardo et al., 2012) by generating quantitative detail and actionable insight. As an example, researchers investigating the EGFR pathway in an in silico cancer cell model demonstrated that inhibition of multiple upstream processes significantly attenuates signal propagation (Araujo et al., 2005). These findings would later support the use of multi-kinase inhibitors as a potential cancer therapeutic (Gridelli et al., 2007;Zhou, 2012). Despite such advances, a few areas of biology provide new opportunities to be explored by quantitative, engineering-based computational models (Baxter and Hodgkin, 2002). Immunology is one example, and in particular, tumor immunology can benefit from robust computational modeling. Recently, the advent of cancer immunotherapy (Bollino and Webb, 2017; Klingemann, 2005) has engendered a new hope for cancer patients and their families. In fact, immunotherapy is meritoriously considered a breakthrough therapeutic approach in the clinic, especially as an effective treatment for hematological cancers (Gattinoni et al., 2006; Mellman et al., 2011;Rosenberg et al., 2004). Ongoing work is aimed at achieving similar success for solid tumors. Mechanistic models of tumor-immune cell interactions can help identify new strategies and potentially enhance the success rate of immunotherapies against solid tumors.