 Dynamical systems approaches to development, being committed to holism and temporal unfolding, echo classic epigenesis and early twentieth-century organicism. These earlier ideas are brought to bear on new high-throughput quantitative datasets using simulation technology—updating and revitalizing the earlier systems theoretic approaches of D’Arcy Thompson and von Bertalanffy. Although most work in systems biology focuses on single-cell organisms and intracellular networks, some researchers look to dynamical systems theory to construct a general theory of development. Following Goodwin’s theories, Johannes Jaeger and colleagues seek to identify a finite core set of basic developmental processes implemented by genetic-epigenetic-cellular regulatory systems (e.g., Jaeger &amp; Sharpe 2014). They use computer simulation of regulatory network models to identify classes of geometrically similar networks that perform specific functions (such as producing stripe patterns). The goal is a rational classification scheme for functionally-characterized developmental mechanisms, which can in turn serve as groundwork for a general theory of development. It remains unclear, however, if biological functions in development can be characterized in a context-independent (modular) way, allowing for a tractable theory. Relatedly, Jonathan Bard (2011, 2013) posits a relatively small set of developmental networks driving “patterning, signaling, proliferation, differentiation, and morphogenesis”, “themes used over and over again” in networks of signaling pathways. Bard’s graphical network approach is distinctive in cutting across spatial scales and levels of hierarchical organization. Core processes (as networks) are distributed across levels; there is no preferred level of developmental activity, but all involved simultaneously. The resulting network structures are quite complex.