 Organization is especially important for attempts to understand the activities of cells but, until recently, cell biologists have had limited tools for determining organization. At a coarse-grained level, the combination of cell fractionation and electron microscopy provided information about organization and this provided insight into how cells perform their activities. For example, recognizing that enzymes that can break apart cell components are segregated from the rest of the cell within the lysosome explains why they only carry out their activities on structures that are first transported into the lysosome. And knowing that a collection of enzymes is localized in an adjacent membrane explains how products of one reaction are readily taken up in another. In these cases the organization realized in cells and its consequences can be characterized qualitatively and presented in a mechanism diagram, as discussed in  section 4.3.  In other cases, however, the organization is more complex than can be described in such qualitative terms. Especially when components are organized into feedback loops, and the individual operations are characterized in nonlinear functions, cell biologists turn to computational models to understand their behavior. Bechtel and Abrahamsen (2010) and Brigandt (2013) describe a number of examples from recent research on cell phenomena that require computational models and refer to the resulting explanations that apply dynamical analysis to mechanistic accounts as “dynamical mechanistic explanations”. Using computational modeling of spindle formation in cell division as an example, Gross (2018) shows how computational models can go beyond what has been established experimentally and serve as heuristics guiding further research.