Design of a reliable process for bacterial antigen production requires understanding of and control over critical process parameters. Current methods for process design use extensive screening experiments for determining ranges of critical process parameters yet fail to give clear insights into how they influence antigen potency. To address this gap, we propose to apply constraint-based, genome-scale metabolic models to reduce the need of experimental screening for strain selection and to optimize strains based on model driven iterative Design-Build-Test-Learn (DBTL) cycles. Application of these systematic methods has not only increased the understanding of how metabolic network properties influence antigen potency, but also allows identification of novel critical process parameters that need to be controlled to achieve high process reliability. Risk-Based Process Development for Bacterial Antigen Production Initiatives such as the World Health Organization (WHO) Global Vaccine Action Plan [1] aim to make vaccines more accessible to the human population. Furthermore, there are initiatives to reduce the risks of antibiotic use in livestock. In consequence, there is an increased demand for new, better, and cheaper vaccines. Antigens (see Glossary) in vaccines that confer protection against bacterial infectious diseases are either whole-cell bacteria (inactivated or live-attenuated) or components derived from wild-type bacterial strains (Table 1). Bacterial antigens are produced in a bioprocess, consisting of an upstream part, where the antigens are produced in large-scale fermenter systems, and a downstream part, where multiple methods are used to purify, concentrate, or formulate antigens (Figure 1A). The development of a production process (Figure 1B) for these antigens is a costly (137 million-1.1 billion US$ [2]) and time consuming (5-18 years) process, largely because target bacterial production strains and growth media are not directly optimized for use in a bioprocess. Current methods for process development require extensive empirical assessments of strains, growth media, and growth conditions in the feasibility phase. These empirical assessments are needed to understand the relationship between the growth conditions and the potency of the antigen produced, while also considering production time, volume, and costs. In addition, control over critical process parameters (CPPs) that influence antigen potency is required to ensure process reliability. Here we propose a novel risk-based process development framework (Figure 2) that incorporates systems metabolic engineering techniques for strain and upstream process development for bacterial antigen production (Table 2). This novel workflow combines the Design for Six Sigma (DFSS) methodology [3] for reliable process design with Design-Build-Test-Learn (DBTL) [4] cycles for rational strain improvement (Box 1). Compared with current methods for process development, this workflow has two main advantages: (i) the duration of the feasibility phase can be reduced bec...