An innovative method to model microorganism growth

Micro-organismes marqués par des sondes fluorescentes © O. Chapleur / Irstea

Irstea scientists have published a paper in the renowned ISME Journal that has been noticed by the microbial ecology community. In the paper, they presented a digital model capable of predicting the growth of microorganism populations within a community, which could be particularly useful in treating wastewater.

Microorganisms, or microbes, are ubiquitous. Active in soil, seas, lakes and even inside the human body, these microscopic organisms (bacteria, fungi, plankton, etc.) play an important role in life in the biosphere. On a daily basis, microbial communities are harnessed for various industrial processes, such as producing beer or cheese, treating wastewater and recovering organic waste, using anaerobic digestion for example. To design and optimize these processes, microbial dynamics must be understood: how fast microbes grow, how they interact, and how they behave. Irstea scientists and their partners are working on a promising theory to model these dynamics.

Improving our understanding of microbial ecosystems

Since the invention of the microscope in the 17th century and the first observation of microorganisms, scientists have tirelessly endeavored to understand microbial dynamics. An initial mathematical model was published in the 1940s. "Jacques Monod, winner of the Nobel prize in physiology or medicine, defined a mathematical relationship that made it possible to predict the rate of growth of a microbe in relation to environmental conditions, specifically substrate levels, which the microorganisms use for food,” explain Hadrien Delattre and Théodore Bouchez, researchers at Irstea's Antony Center.

This model and the others that followed are now used regularly in all biotechnology applications (therapeutic product manufacturing, fermentation, genetic engineering, etc.) that use pure microbe cultures (with only a single species of microbe), making it possible to correctly model the growth of these organisms. However, these models reveal their limitations when applied to mixed cultures, where multiple microbe populations evolve and are organized into complex communities (soil, sea, wastewater treatment plants, etc.), thereby restricting the use of many environmental and biotechnological applications. "At Irstea, we are proposing a new theory capable of predicting the growth of these microorganisms and their behavior in a system containing several microbe populations,” enthuses Bouchez.

Predicting the dynamics of mixed microbe populations

Delattre, a former PhD student at Irstea, has built a digital program capable of simulating theoretically described situations within an activated sludge system, the subject of the publication entitled "Consistent microbial dynamics and functional community patterns derived from first principles."

"In addition to allowing us to model several populations simultaneously, the aim of the process is to create a theoretical model. This can be used to reproduce patterns with minimal hypotheses. While traditional models require various experiments to define their parameters, our model is capable of creating predictions for systems and bioprocesses that have never been tested," explains Delattre. By implementing this model as a digital program that could eventually be integrated into professionally used engineering software, our ability to design and optimize processes in a wastewater treatment plant would be significantly improved. Generally, this method could be applied to a variety of bioprocesses, such as composting or fermentation, as well as to natural environments (water, soil, etc.) in order to predict the order and intensity of occurring reactions. The software is part of the wider Thermomic project, which aims to develop a generic and predictive modeling framework for microbial ecosystems.

Thermomic project profile
  • Purpose: A thermodynamic framework to model microbe growth and dynamics
  • Partners: INRA-LBE Laboratory of Environmental Biotechnology from INRA Narbonne and LISPB Biological Systems and Processes Engineering Laboratory
  • Dates: 2016-2020
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