 Another approach is to consider explanations in systems biology as mergers of different types of explanations. Some accounts highlight the compatibility of different explanatory aims as ingredients in a pluralistic approach to biological explanation (Brigandt et al. 2018; Mekios 2015). One way to clarify the compatibility is that different approaches highlight constitutive or causal aspects (Fagan 2015). Distinguishing between these can possibly help identify the distinct contributions of causal mechanistic modeling and mathematical analysis of network dynamics. Whereas causal relations are often prioritized in mechanistic accounts, Fagan (2015) suggests a joint account of collaborative explanations that combine descriptions of the spatial organization and the causal relations in the system. The increasing reliance on formal modeling in systems biology also call for an examination of the relation between mathematical and mechanistic explanations (Baker 2005; Brigandt 2013; Mekios 2015). Some have argued that an updated account of dynamic mechanistic explanations is required, highlighting the importance of mathematical and computational modeling for understanding non-linear dynamics, cyclic organization, and complex feedback relations (Bechtel &amp; Abrahamsen 2011, 2012; Brigandt 2013). In this context, idealized models are important for understanding the organization of mechanisms within a larger network structure (Bechtel 2015b; Levy &amp; Bechtel 2013; van Eck &amp; Wright  2021).[8]