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1. Microbial growth in soil

   https://doi.org/10.32942/X2M31M  

   Foley, Megan; Stone, Bram; Caro, Tristan; Sokol, Noah; Blazewicz, Steven; Dijkstra, Paul; Hayer, Michaela; Hofmockel, Kirsten; Finley, Brianna; Koch, Benjamin; et al (June 2024, EcoEvoRxiv)

   The growth rate of a microorganism is a simple yet profound way to quantify its impact on the world. Microbial fitness in the environment depends on the ability to reproduce quickly when conditions are favorable and adopt a survival physiology when conditions worsen, which cells coordinate by adjusting their growth rate. At the population level, per capita growth rate is a sensitive metric of fitness, linking survival and reproduction to the ecology and evolution of populations. The absolute growth rate of a microbial population reflects rates of resource assimilation, biomass production, and element transformation, some of the many ways that organisms affect Earth’s ecosystems and climate. For soil microorganisms, most of our understanding of growth is based on observations made in culture. This is a crucial limitation given that many soil microbes are not readily cultured and in vitro conditions are unlikely to reflect conditions in the wild. New approaches in ‘omics and stable isotope probing make it possible to sensitively measure growth rates of microbial assemblages and individual taxa in nature, and to couple these measurements to biogeochemical fluxes. Microbial ecologists can now explore how the growth rates of taxa with known traits and evolutionary histories respond to changes in resource availability, environmental conditions, and interactions with other organisms. We anticipate that quantitative and scalable data on the growth rates of soil microorganisms will allow scientists to test and refine ecological theory and advance processbased models of carbon flux, nutrient uptake, and ecosystem productivity. Measurements of in situ microbial growth rates provide insights into the ecology of populations and can be used to quantitatively link microbial diversity to soil biogeochemistry.  

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2. Superconcentration in surface growth

   https://doi.org/10.1002/rsa.21108  

   Chatterjee, Sourav (March 2023, Random Structures & Algorithms)
3. Universal Growth in Production Economies

   Branzei, Simina and (January 2018, Advances in neural information processing systems)
4. Optimizing Cytotoxic T Cell Growth in a Centrifugal Bioreactor through Kinetic Growth Models

   Kitana Kaiphanliam, Brenden Fraser-Hevlin (November 2021, Annual meeting American Institute of Chemical Engineers)

   Cancer is the second leading cause of death globally and remains a significant issue in medicine. Immunotherapy treatments such as Chimeric Antigen Receptor T cell (CAR-T) therapies are becoming a more promising option because of their effectiveness in killing cancer cells without harming healthy tissue in the body. CAR-T therapies, however, are inaccessible to many due to the high cost—a result of inefficient cell expansion and manufacturing methods. To address this issue, we have developed the Centrifugal Fluidized Expansion (CentriFLEX) bioreactor that balances centrifugal and fluid forces, allowing the system to operate in perfusion and maintain a high cell density. Shown in past applications for similar cell types, the CentriFLEX can expand cultures up to 2.1 billion cells in an 11.4 mL chamber over the course of one week. Recently, we have used this system to expand bovine T cells as part of a collaboration with the College of Veterinary Medicine at Washington State University. Through the project, we conducted kinetic studies to model substrate consumption and metabolite production of bovine T cells and have enhanced the bioreactor design by making it more compact to fit entirely within a biosafety cabinet— mitigating contamination concerns. Current efforts have been spent determining the remaining parameters for the kinetic models and using such models to understand how the cells grow over time and in the space of a high-population density chamber. In this presentation, we will share how we use growth models that are based on a series of kinetic studies to predict substrate and metabolite levels over time in the bioreactor, allowing us to alter feed and dosing rates of medium and nutrients to maintain cell growth at the maximum specific growth rate. 

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5. Operator Growth in Open Quantum Systems

   https://doi.org/10.1103/PhysRevLett.131.160402  

   Schuster, Thomas; Yao, Norman Y (October 2023, Physical Review Letters)