Average time and investment to bring a product to market through strain development:

Failed scale-up runs remain a major factor (Abatte et al., 2023):

Both factors reduce KPIs.

Given ongoing challenges, here are some ideas for strain engineering. I will draw specifically from precision cell simulators called ME-models (metabolism and macromolecular expression).

What is a ME-model?

ME (Metabolism and macromolecular Expression)-models are multi-scale genome-scale models that integrate cellular metabolism with macromolecular expression. They expand metabolic network models with first-principles reconstruction of transcription, translation, and post-translational modification pathways. Proteome allocation constraints are built in, as is variable biomass composition in different environments.

Some notable extensions for E. coli:

ME-models compute holistic factors affecting cell fitness beyond redirecting carbon and energy. They consider various aspects of cellular metabolism and protein expression, including:

Strain engineering needs to address distinct fitness limitations as strain performance improves:

This progression suggests that optimization strategies should match the current limiting factor rather than trying to solve all bottlenecks simultaneously.

It's important to note that this progression also depends on the product.

Protein production likely faces immediate translational bottlenecks, while small molecule production initially struggles with carbon partitioning before protein expression becomes limiting.

This is because reallocating expressed proteins takes hours or longer depending on host, and on protease activity (DynamicME)

The theoretically optimal way for a cell to reallocate its proteome by expressing new proteins and degrading old ones is sequentially expressing the most limiting proteins one by one (Pavlov and Ehrenberg, 2013).

And if proteases are in short supply, the cell still needs to free up more proteome space as you can’t pack infinite amount of protein in a cell. The only way is to dilute out existing proteins – a slow process proportional to cell division (growth) rate.

One practical implication of this: pre-culture conditions can impact the first hours or days of bioreactor performance.

Many proteins expressed by microbial hosts appear to be useless at first glance. In fact, up to nearly half the proteome mass is potentially unused for E. coli in any given growth condition (O'Brien et al., 2016). Reducing this unused proteome is one of the first traits that lab-evolved strains of E. coli exhibit to improve growth rate.

However, this proteome pre-allocation turns out to be a fitness strategy for generalists evolved to survive in multiple conditions. The regulatory network has evolved to maintain elevated levels of:

This is achieved by the general stress response sigma factor RpoS.

Pre-allocated proteins serve as a "reserve capacity" for alternative substrate metabolism, environmental stress responses, and protein quality control systems - similar to maintaining emergency reserves that enable rapid adaptation when needed.

This strategy reflects an evolved trade-off between growth efficiency and environmental responsiveness in generalist organisms.

Understanding this trade-off may inform strategies for optimizing protein expression while maintaining the strain's robustness and adaptability.