 Some systems biologists are optimistic that current limitations in the predictive capacity of models can be overcome by increasing the complexity of computational models, thus presenting a epistemic departure from the methodological principle of Ockham’s razor (Kolodkin &amp; Westerhoff 2011). Others stress that biological complexity forces life scientists to draw on abstract and idealized models (Hofmeyr 2017; Gross 2017; 2019). These debates center on fundamental questions about how far biological research can be taken by integrating more parameters and data points (Kolodkin et al. 2012), and how far we can ‘extend ourselves’ through computational approaches (Humphreys 2004; Vermeulen 2011). But they also concern ontological questions about whether there are principled limitations to our ability to predict and control living systems “bottom-up” (Bassingthwaighte et al. 2009; Green 2018; Noble 2012; Moreno and Suárez 2020). Large-scale modeling projects such as the Virtual Cell, the Physiome Project, and the Virtual Physiological Human offer exciting cases for philosophical analysis of the prospects and challenges associated with parameterizing and validating complex models (Carusi et al. 2012; Carusi 2014; Hunter et al. 2013). Ultimately, such projects may push the boundaries for prediction and control in the life science, or may reveal deeper challenges of biological complexity.