 Topological or structural explanations emphasize how networks architectures generically determine dynamic behaviors, independently of the causal details of the network (Huneman 2010; Kostić 2020; Kostić et al. 2020; Suárez &amp; Deulofeu 2019). Mathematical analysis of network structures underlying biological robustness has been argued to exemplify this explanatory goal (Jones 2014). Another type of non-mechanistic explanations is what Wouters (2007) calls design explanations, first outlined in the context of comparative physiology. Design explanations do not describe how a biological function is causally produced but instead clarify why a given design (and not an alternative design) is present. They do so by pointing to constraints on the possible designs that make some designs good, some suboptimal, and others impossible. The relevance of design explanations for systems biology lies in the interest to specify relations between functional capacities (e.g., robustness) and system organization (e.g., integral feedback control) via design principles that are independent of specific contexts of implementation (Braillard 2010; Boogerd 2017, see also Shinar &amp; Feinberg 2011 for a candidate example from systems biology). Design explanations highlight explanatory features similar to what Batterman and Rice (2014) call minimal model explanations (see also Gross 2015). These explanatory accounts have been interpreted as varieties of constraint-based explanations (Green and Jones 2016), because they prioritize physical and formal constraints at the expense of mechanistic details. Aside from highlighting differences in the epistemic aims of explanation, some accounts also provide an ontological justification for different explanatory strategies, i.e., that higher-level phenomena are characterized by a functional loss of and autonomy from mechanistic details at lower levels (Batterman and Green 2021; Moreno and Suárez 2020).