Researchers working with BTI faculty members Yiannis Kaznessis and Claudia Schmidt-Dannert have devised a way to accurately predict growth and behavior of synthetic ecological systems – engineered relationships between different organisms that don’t occur in nature. The predictions are based on mathematical models of growth and chemical signaling pathways between bacteria and yeast in a simulated synthetic ecological system.
Synthetic ecology is a relatively new approach to biological processing that relies on a cooperative system of multiple microbial populations living and working together in productive interactions. These biological systems may be designed and constructed from modified organisms, and can establish new relationships between different organisms not normally found living together. The ability to engineer functionality and cooperation between multiple organisms of different species opens the door to producing a broad range of specialty chemicals, pharmaceuticals and biofuels.
“One challenge of engineering these systems is getting the microbial members – especially from different species like yeast and bacteria – to “talk” to each other and coordinate their metabolism,” explained David Babson, a postdoctoral research associate working on the project.
In simulating the synthetic bacteria-yeast ecosystem, researchers examined the role of a couple signaling molecules commonly used in bacterial communication and engineered a strain of yeast that could produce one and respond to the presence of the other generated by an engineered bacteria. They took into account volume and the growth rate differences between the bacteria and yeast and the effect of different transcription rates and random variables in predicting probable behavior.
The developed model allows for the optimization of experimental behavior not only of a yeast-bacteria community, but also of a number of other synthetic microbial communities.
“The ability to predict the interactions and population dynamics of theoretical ecological systems can inform the design of these systems,” concluded Babson. “This is what these models do for us, and that is why this research is so important.”