Mol Syst Biol. 4: 185 The ‘bottom‐up’ approach to systems biology entails quantitatively studying complex biological processes by analyzing their molecular components. A converse system biology approach is to infer properties of biological systems in a ‘top‐down’ fashion, using a variety of network reverse engineering methods, data‐driven modeling and data integration strategies. Application of a top‐down approach to the quantitative biology of a small size system is however less common. In a recent publication, Mettetal et al (2008) have insightfully applied such a strategy to successfully decode critical properties of osmo‐adaptation in the yeast Saccharomyces cerevisiae . A biological system can, in principle, be dissected by successively inactivating each component individually and measuring how the overall input–output characteristics of the system change. For cellular systems, such dissection involves gene knockouts and/or knockdowns. However, given the complexity of living cells, it is difficult to link the function of single components (e.g. a protein) to observed outputs. Moreover, invasive knockout/knockdown often leads to undesired complications (e.g. lethality). Mettetal et al followed a different systems reverse‐engineering approach by which they considered the system first as a ‘black box’ and assumed it to be equivalent to a linear time‐invariant (LTI) system (Oppenheim et al , 1997) (Figure 1A and B). An LTI system has two defining properties: first, the output from a set of inputs represents the linear sum of the outputs from each individual input (Figure 1C). Second, the generated output is independent of the time point at which the causal input was applied (Figure 1D). An LTI system is characterized by a single ‘response function.’ Once the response function is known, the output for any arbitrary input can be …