Must read! What are the opportunities and challenges for fermentation optimization in the era of synthetic biology?
By inserting exogenous genes to achieve the expression of exogenous proteins, constructing new exogenous pathways to express heterologous products, and expressing mRNA vaccines, the research on fermentation processes has been greatly expanded.
However, the introduction of exogenous genes can cause changes in the metabolic characteristics of host cells, and strains constructed using different strategies for expressing different products in the same host exhibit different behaviors during the fermentation process.
Therefore, the development of these new technologies brings opportunities to the traditional fermentation industry, but also poses many challenges to it.
In the past decade, the development of synthetic biology has driven significant progress in strain construction and high-throughput automated screening technology, enabling the faster acquisition of high-performance strains.
However, the traditional fermentation process based on laboratory scale reactors requires a large amount of manpower, and the development process is time-consuming and labor-intensive, which obviously cannot meet the needs of such a large number of strains in performance validation and process development.
Microfluidic technology has made certain progress in solving high-throughput and automated cultivation, especially in the high-throughput screening of high-performance strains.
However, the flow field environment formed inside microchannel reactors is significantly different from industrial environments. Although it increases the screening flux, there are still certain limitations in the amplification of fermentation processes.
Therefore, developing high-throughput and automated micro parallel reactors, especially those that can accurately reflect industrial production environments, has become a new challenge for fermentation optimization equipment in the current development of synthetic biotechnology.
In addition, the fermentation process is a complex dynamic process that requires a large amount of online parameter detection. Therefore, high-throughput fermentation process optimization equipment brings challenges in storing, visualizing, and analyzing massive process data.
The Past and Present of Fermentation Optimization and Amplification Technology
It is necessary to introduce data science into the optimization research of fermentation processes, and use data science theories and tools to process the massive data formed during high-throughput process development. This can draw on relevant technologies of high-throughput screening data analysis.
Existing data science software includes scikit learn, pandas, and Numpy packages based on Python language, as well as open-source KNIME software package.
On the other hand, in the process of constructing high-performance bacterial strains, metabolic engineering and synthetic biology will integrate a series of exogenous genes on the basis of the host bacteria, or modify the host bacteria's own genes to form a large number of bacterial strains.
Understanding the physiological and metabolic characteristics of wild-type host cells in fermentation tanks, such as optimal pH, optimal temperature, growth rate, nutrient requirements, metabolic reactions under excess substrate and oxygen limitations, is crucial for selecting high-throughput screening models and requires detailed research.
Overview: Overview of Data Collection and Visualization Technologies for Biological Fermentation Processes!
In addition, the unique metabolic characteristics exhibited by a large number of strains formed by different host bacteria or different modification strategies in the reactor are of great reference value for better modification of strains.
During the process of strain modification, researchers can refer to a large number of genomic, transcriptome, proteomic databases, etc. However, there is a serious lack of databases on the metabolic characteristics of strains during fermentation.
This presents a new challenge, which is the construction of a database of metabolic characteristics exhibited by strains formed by different host bacteria, as well as strains formed by different modification targets or strategies of the same host bacteria in bioreactors. Similar research work has not yet been reported.
This relies on high-throughput fermentation equipment on one hand, and efficient data science processing tools on the other hand. With the development of these two aspects, such databases will provide richer data support for subsequent strain modification.
In addition, how to consider the problems encountered during the fermentation process before constructing strains and selecting expression systems is currently lacking in strain construction.
For example, uneven substrate concentration distribution, limited maximum oxygen transfer capacity, high cost of using inducers on a large scale, and negative effects of high dissolved CO2 concentration in large tonnage fermentation tanks on cell activity in production scale reactors should all be considered in strain construction and high-throughput screening.
However, researchers involved in upstream biotechnology development such as strain modification often lack awareness or insufficient attention in this area.
The strains constructed on this basis form an unavoidable bottleneck for optimizing the subsequent fermentation process.
Therefore, it is very important to strengthen communication between strain construction researchers and fermentation engineering researchers.
