Duke University scientists say they have created a framework for helping bioengineers determine when to use multiple lines of cells to manufacture a product. Their study (“Metabolic Division of Labor in Microbial Systems”), published in Proceedings of the National Academy of Sciences (PNAS), could help a variety of industries that use bacteria to produce chemicals ranging from pharmaceuticals to fragrances, according to the researchers.
“Metabolic pathways are often engineered in single microbial populations. However, the introduction of heterologous circuits into the host can create a substantial metabolic burden that limits the overall productivity of the system. This limitation could be overcome by metabolic division of labor (DOL), whereby distinct populations perform different steps in a metabolic pathway, reducing the burden each population will experience. While conceptually appealing, the conditions when DOL is advantageous have not been rigorously established,” write the investigators.
“Here, we have analyzed 24 common architectures of metabolic pathways in which DOL can be implemented. Our analysis reveals general criteria defining the conditions that favor DOL, accounting for the burden or benefit of the pathway activity on the host populations as well as the transport and turnover of enzymes and intermediate metabolites. These criteria can help guide engineering of metabolic pathways and have implications for understanding evolution of natural microbial communities.”
Cells are constantly absorbing nutrients and raw materials and transforming them into something more useful. Often the process provides the cells with energy or some other vital vitamin or mineral, while leaving behind byproducts that can be beneficial for other cells. This is especially true in complex multicellular organisms and ecosystems, where several different types or species of cells can work together to generate a single complex final product.
Scientists have been harnessing these abilities since the 1970s to produce useful substances like human growth hormone, pharmaceuticals, fragrances, and biofuels. Most of the time they rely on a single type of cell for such endeavors for the sake of simplicity. But sometimes the process becomes too complicated.
“Typically, when people are modifying cells to produce something, they use a single population; but when you only use one type of cell to do everything, there's an upper limit on what it can handle, which becomes a limitation to how sophisticated a compound you can ask the cell to make,” said Ryan Tsoi, a graduate student studying biomedical engineering at Duke and first author of the paper. “Having multiple cell types dividing the labor has been explored, but only on a case-by-case basis. This is the first systematic look into what circumstances make multiple cell lines better than one.”
In the study, Tsoi and his advisor, Lingchong You, Ph.D., the Paul Ruffin Scarborough Associate Professor of Engineering at Duke, put together a system of equations to model how important variables interact in these types of systems. For example, they can model the strain that complex tasks put on a single cell's growth rate or the inefficiencies introduced when cells must pass signals, enzymes, and proteins back and forth in a division-of-labor scheme.
They put together more than 20 different variations of how these systems could be built and how they might interact. When they ran the simulations, they discovered that every trial boiled down to how the variables affected two factors—how fast the cells are able to grow and how much efficiency is lost when two types of cells share resources while transporting molecules between them.
“It's comparable to when researchers are working together on a grant proposal or a paper,” said Dr. You. “It's a balance between how easy it is to do by yourself, how efficient it will be in working with other collaborators, and how big of a payoff the collaboration will be at the end of the day.”
Moving forward, Tsoi plans on using the new framework to develop the new bioengineered systems he is planning to study. He hopes others will do the same.
“All of these parameters are measurable and quantifiable,” said Tsoi. “The idea is that for any system you could obtain all of these parameters either through basic experiments or textbooks, throw them into this mathematical model, and not only obtain a basic answer of whether or not to use division of labor, but a measure of how much it would benefit your project.”