 One divide that is commonly highlighted is the gap between experimental biologists and modelers with a background in engineering, mathematics, or physics. Empirical analyses of collaborations between modelers and experimentalists in systems biology show that these two groups often have different views on what kinds of data and models are most relevant (Joaquium et al. 2019; Nersessian 2017; Rowbottom 2011). For instance, modelers may not be interested in studying biological systems at the same level of detail as experimental biologists, and experimentalists may find highly abstract and idealized models inspired by physics or engineering misleading (MacLeod 2018). From the perspective of engineers or physicists, the preoccupation with molecular details involves a risk of missing out on dynamic patterns that could be captured with more generic models, while experimentalists may find that such generic models often lack specificity or merely reproduce current biological knowledge. These differences may in part be grounded in different explanatory standards as also discussed in Section 4.2 (Fagan 2016; Green et al. 2015a).