There is much hype about leveraging 3.5 billion years of evolution to solve pressing problems concerning environment and energy. The basic strategy is to design novel (unnatural) biochemical capabilities for environmental cleanup and energy production by recombining and rationally re-engineering biological circuits from diverse organisms. In principle, it is possible to decipher and re-engineer the complex information processing circuits that dynamically reconfigure the physiology of a particular organism in response to environmental change. In practice, this would require extensive mining of systems measurements for conditional relationships among patterns of changes in gene expression (mRNA, protein and ncRNA), interactions (P-P and P-D), modifications (protein and DNA), and metabolism (metabolite levels, enzyme activities). For synthetic biology applications, it is essential to capture relevant conditional relationships both at a systems level and at a sufficiently high resolution to mechanistically describe and predict how environmental change influences the execution of these cellular algorithms at multiple scales. Clearly, the experimental and computational tools necessary for doing this type of multi-scale network inference and modeling are diverse, disjointed, and constantly changing for a purely monolithic knowledge-base solution to be practical. A knowledge-base that is built upon an architecture of loosely coupled resources, on the other hand, is both highly adaptable to change and essential for large collaborative and interdisciplinary systems biology efforts for rapid inference and re-engineering of biological circuits.