The nascent field of synthetic biology offers the promise of engineered gene networks with novel biological phenotypes. Numerous synthetic gene circuits have been created in the past decade, including bistable switches, oscillators, and logic gates. Despite a booming field and although recently developed designs of regulatable gene networks are ingenious, there are limitations in routinely engineering synthetic biological systems. Indeed, there is a need for rationalizing the design of novel regulatable gene networks that can be used in useful applications. We are developing multiscale mathematical tools to assist synthetic biology efforts. Why are multiscale models necessary to assist synthetic biology, and not simply apply the mathematics developed to model kinetic and thermodynamic processes in living organisms? Because, although the principles of thermodynamics, kinetics and transport phenomena apply to biological systems, these systems differ from industrial-scale chemical systems in an important, fundamental way: they are occasionally far from the thermodynamic limit. Indeed, using ordinary differential equations for simulating the reaction kinetics of these systems can be distinctly false. The need arises then for stochastic models that account for inherent, thermal noise, which is manifest as phenotypic distributions at the population growth/interaction levels. In the presentation, we will discuss synthetic biological systems, the development of multiscale models to capture the phenotypic behavior of these systems and we will present examples of model-driven synthetic bioengineering.