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1. Urban Metabolism and Digital Twin Technologies for a Sustainable Built Environment: Towards a Framework for a Campus Application

   https://doi.org/10.1088/1755-1315/1363/1/012081  

   Geremicca, F; Fascetti, A; Brigham, J C; Bilec, M M (June 2024, IOP Conference Series: Earth and Environmental Science)

   Abstract With rapid urbanization necessitating innovative strategies for urban adaptation, combining technological advancements and holistic methodologies, this research explored the synergy between urban metabolism and digital twin technologies to foster sustainable urban development. A pilot model representing a university building, including the surrounding streetscape, was constructed using the Unreal Engine. By using available CAD design drawings and GIS technologies, the physical spaces were modelled. The physical and analytical environments were integrated into the digital twin; material flow analysis was also conducted. The developed framework aims to offer a detailed visualization of building behaviour, facilitating comparisons with urban metabolism analysis. This approach holds promise for sustainable urban design by integrating diverse data streams through digital twin technologies. The potential impact of this research extends to the tracking, mapping, and analysis of crucial resource flows, such as materials, water, energy, and waste, fostering circular economy strategies within the built environment. Understanding urban metabolism facilitates the identification of resource-efficient opportunities, promoting resource recovery and reuse to reduce the environmental impact of urban cores. Embracing digital twin technologies and urban metabolism analysis offers cities streamlined data collection processes, supporting standardization and sustainable urban practices. This study marks a critical step towards integrating diverse data streams into urban metabolism analysis, aligning with circularity objectives in the built environment. By adopting this framework, cities can better understand new production and consumption patterns that prioritize the responsible use of natural resources, contributing to a more sustainable and resilient future. 

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2. Urban infrastructure influences dissolved organic matter quality and bacterial metabolism in an urban stream network

   https://doi.org/10.1111/fwb.13035  

   Arango, Clay P.; Beaulieu, Jake J.; Fritz, Ken M.; Hill, Brian H.; Elonen, Colleen M.; Pennino, Michael J.; Mayer, Paul M.; Kaushal, Sujay S.; Balz, Adam D. (October 2017, Freshwater Biology)
3. Essential metabolism for a minimal cell

   https://doi.org/10.7554/eLife.36842  

   Breuer, Marian; Earnest, Tyler M; Merryman, Chuck; Wise, Kim S; Sun, Lijie; Lynott, Michaela R; Hutchison, Clyde A; Smith, Hamilton O; Lapek, John D; Gonzalez, David J; et al (January 2019, eLife)

   One way that researchers can test whether they understand a biological system is to see if they can accurately recreate it as a computer model. The more they learn about living things, the more the researchers can improve their models and the closer the models become to simulating the original. In this approach, it is best to start by trying to model a simple system. Biologists have previously succeeded in creating ‘minimal bacterial cells’. These synthetic cells contain fewer genes than almost all other living things and they are believed to be among the simplest possible forms of life that can grow on their own. The minimal cells can produce all the chemicals that they need to survive – in other words, they have a metabolism. Accurately recreating one of these cells in a computer is a key first step towards simulating a complete living system. Breuer et al. have developed a computer model to simulate the network of the biochemical reactions going on inside a minimal cell with just 493 genes. By altering the parameters of their model and comparing the results to experimental data, Breuer et al. explored the accuracy of their model. Overall, the model reproduces experimental results, but it is not yet perfect. The differences between the model and the experiments suggest new questions and tests that could advance our understanding of biology. In particular, Breuer et al. identified 30 genes that are essential for life in these cells but that currently have no known purpose. Continuing to develop and expand models like these to reproduce more complex living systems provides a tool to test current knowledge of biology. These models may become so advanced that they could predict how living things will respond to changing situations. This would allow scientists to test ideas sooner and make much faster progress in understanding life on Earth. Ultimately, these models could one day help to accelerate medical and industrial processes to save lives and enhance productivity. 

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4. Recovery and resilience of urban stream metabolism following Superstorm Sandy and other floods

   https://doi.org/10.1002/ecs2.1776  

   Reisinger, Alexander J.; Rosi, Emma J.; Bechtold, Heather A.; Doody, Thomas R.; Kaushal, Sujay S.; Groffman, Peter M. (April 2017, Ecosphere)
5. A Molecular Communication model for cellular metabolism

   Zahmeeth, Sakkaff; Freiburger, Andrew P.; Catlett, Jennie L.; Cashman, Mikaela; Immaneni, Aditya; Buan, Nicole R.; Cohen, Myra B.; Henry, Christopher; Pierobon, Massimiliano (July 2023, bioRxiv)

   Understanding cellular engagement with its environment is essential to control and monitor metabolism. Molecular Communication theory (MC) offers a computational means to identify environmental perturbations that direct or signify cellular behaviors by quantifying the information about a molecular environment that is transmitted through a metabolic system. We developed an model that integrates conventional flux balance analysis metabolic modeling (FBA) and MC to mechanistically expand the scope of MC, and thereby uniquely blends mechanistic biology and information theory to understand how substrate consumption is captured reaction activity, metabolite excretion, and biomass growth. This is enabled by defining several channels through which environmental information transmits in a metabolic network. The information flow in bits that is calculated through this workflow further determines the maximal metabolic effect of environmental perturbations on cellular metabolism and behaviors, since FBA simulates maximal efficiency of the metabolic system. We exemplify this method on two intestinal symbionts – Bacteroides thetaiotaomicron and Methanobrevibacter smithii – and visually consolidated the results into constellation diagrams that facilitate interpretation of information flow from given environments and thereby cultivate the design of controllable biological systems. The unique confluence of metabolic modeling and information theory in this model advances basic understanding of cellular metabolism and has applied value for the Internet of Bio-Nano Things, synthetic biology, microbial ecology, and autonomous laboratories. 

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