Large-scale production using microorganisms has long been recognized as a promising source for sustainable fuels and chemicals. However, monocultures optimized for high metabolic production in a sterile laboratory environment are often not economical at production scale due to high costs of capital and substrates, lack of resilience of the culture, etc. On the other hand, most microorganisms in natural environments do not live in isolation, but exist as part of complex, dynamically changing, microbial consortia. These natural consortia exhibit high productivity combined with high resilience to invasion and can process a wide range of readily available substrates. Hence, synthesis of artificial biological process systems based on microbial consortia seems a promising approach to low cost sustainable production of fuels and chemicals. Nevertheless, it remains a great challenge to realize such multispecies cultures in industrial applications. Using algal production of fuels and chemicals as an illustrative example, we outline a roadmap towards the quantitative design and optimization of low cost resilient artificial ecologies based on microbial consortia. To address this challenge, multi-scale models are proposed, which integrate metabolic information available from high-throughput experiments with the ecological scale of the interactions between multiple species and the process scale of bioreactors. These models are formulated as dynamic systems with optimization problems embedded, and progress towards numerical tools for simulation, sensitivity analysis and optimization will be reported. The long-term goal is a quantitative approach that will enable chemical engineers to design artificial ecologies for a desired purpose in much the same manner as a traditional chemical process.