Media optimization is important for a number of reasons. Media composition can have a significant impact on growth and production kinetics of a bioprocess. It can be leveraged to tune the transition between exponential growth and stationary phase, which is often used to separate growth and production phases in microbial bioprocesses. Components in the media may also be required for incorporation into a product, or aid in productivity by increasing flux through a metabolic node. In contrast, excess concentrations of components may have an inhibitory impact on strain performance. In combination, the components in the media can have a major impact on the performance of a strain, and a focus on media optimization can yield significant performance gains.
Media can also represent a considerable proportion of costs for many microbial bioprocesses. In this scenario, reducing media costs through optimization can have a positive impact on economics. For microbial processes, raw feedstocks are a common option for economical media compositions. Additionally, optimization can reduce components from the media remaining in the spent medium which consequently has the potential to simplify downstream processes, further reducing costs.
There are some challenges associated with media development. First, media is often a mixture with many different components, which can make it challenging to deconvolve the impact of each component. Often, media also contains complex components, which, by definition, the exact composition of which is not known. In addition, some media components, such as trace metals or vitamins, require specialized analytical methods to quantify. Finally, the difference between an optimal concentration of a media component and an amount that may have an inhibitory effect on growth can be relatively small, meaning that simply adding excess of many components is not a viable option for maximizing strain performance.
Even if the composition of the media is known, seemingly inconsequential factors such as the order of addition of components, and the duration of the autoclave cycle can impact the chemistry and performance of a media. Additionally, there may be differences between lots of complex media components, which can also impact the chemical composition of the media.
There are also interactions between different media components. If one component becomes limiting, it may mask an impending limitation of another component which may not be revealed until other limitations are relieved, which can increase timelines for media development.
There are several strategies that can be used for media optimization. Best practices involve optimizing the media to provide just enough of each component in an intentional manner, but also in a way that is practical in terms of project resources and timelines. The level of optimization required will likely be dependent on the individual product being produced and the complexity of the media; in many cases over optimization may not yield improved performance and require significant resources. However, some level of optimization can have a significant impact on strain performance and downstream processes.
At the beginning of a project, an initial media design can be based on the known physiology of the organism based on reports published in the literature. From here, the desired architecture of the process can be used to inform media design. For example, if the process will be designed to have a separated growth and production phase, nutrient limitations that induce a shift from growth to stationary phase without impacting viability can be an effective method for process design. Similarly, nutrients, either by limitation or addition, can be leveraged to induce protein expression of heterologous pathways. Components that will be incorporated into the product should also be factored into the base medium in concentrations sufficient for the amount of product being produced. Basic media design should also be approached with commercialization in mind; components that cannot be sourced at industrial scale, are too expensive or that have the potential to complicate downstream processes should be minimized or, if possible, eliminated from the media entirely.
If analytical capabilities are available to support media development, a yield coefficient informed approach to basic media development can be both effective and efficient. Here the concentration of each media component is measured over a time course to determine the amount of each component consumed to generate a given amount of biomass generated during the experiment. This data can, in turn, be used to inform a media designed where the concentration of each measured component can be optimized individually based on empirical observations.
Media optimization remains relevant throughout the development of a fermentation process. As the overall strain performance is as a result of the interaction of the strain with the environment, it is plausible that media requirements will shift as other aspects of the process and the strain change over time. Therefore, it can be valuable to periodically perform experiments to identify any media limitations of opportunities for improved performance through media optimization. Here, streamlined approaches such as DOEs can help identify conditions for improved performance.
Many fermentation processes will need to shift to less pure media components from a sourcing or cost perspective for scale-up, and this can represent a significant risk to successful scaling. There are studies that can be performed at bench scale in order to de-risk issues with media during scale-up. This is the preferred path both from an economic and timeline perspective, as media studies can be performed rapidly and with relatively little expense at bench scale. Studies that can be performed at bench scale to de-risk scale-up include testing different vendors, lots and stability of various media components.
Culture’s bioreactor capacity enables many different kinds of media studies. Media can be optimized using a DOE approach, which can identify the main media factors and interactions between components that can impact performance. Bioreactor capacity can be used to test feedstocks from different vendors or lots, or to shift from a defined research media to a complex medium for scale-up, or even to test the impact of different carbon sources and auxiliary feeds.
The data provided with each fermentation run allows deep insight into the metabolic state of the organism. In particular, off-gas data can provide information around the metabolism of an organism. Each of Culture’s bioreactors is fitted with continuous off-gas sensors that provide detailed online data that can be used to identify primary substrates being consumed and visualize the timing of metabolic shifts. This level of visibility allows rapid assessment of cellular metabolism for the purpose of media optimization, and consequently accelerated media development timelines.