Multi-scale Simulation, Calibration, and Optimization of Calcium Carbonate Precipitation in Microbial Communities

Technical Report · Mon Sep 30 04:00:00 EDT 2024

DOI:https://doi.org/10.2172/2462816· OSTI ID:2462816

George, August D. ^[1]; Anthony, Winston E. ^[1]; McNaughton, Andrew D. ^[1]; Lin, Tesia T. ^[1]; Torres-Rodriguez, Victor ^[2]; Mahserejian, Shant M. ^[1]; Altus, Sabina L. ^[1]; Farris, Yuliya ^[1]; McClure, Ryan S. ^[1]; Zucker, Jeremy D. ^[1]

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1. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
2. Univ. of Texas at El Paso, TX (United States)

Ensuring the efficient engineering of microbially induced calcium carbonate precipitation (MICP) is crucial for a variety of environmental and civil engineering applications, such as soil stabilization and carbon sequestration. Addressing this need, we present a comprehensive multi-scale workflow that begins with the isolation of calcium carbonate-producing microbes from soil samples, followed by metagenomic sequencing and metabolic reconstruction. We then characterize microbial growth phenotypes under diverse nutrient conditions, compare observed growth with metabolic model predictions, and apply the Consistent Reproduction of Phenotype (CROP) algorithm to refine these models. Furthermore, we analyze metabolite consumption and production, and develop a consumer-resource model that is calibrated using time-series measurements of growth rates, pH levels, and calcium carbonate precipitation. The primary benefit of our approach lies in its ability to predict and control MICP outcomes, facilitated by a Bayesian methodology that incorporates priors on initial conditions and parameters. This allows us to compute posteriors by integrating experimental data, and to solve a risk optimization problem under uncertainty to identify nutrient conditions that maximize calcium carbonate production. In contrast to non-Bayesian methods, which fail to quantify uncertainty accurately, our approach provides a more reliable pathway to optimizing nutrient conditions, enhancing the likelihood of achieving desired MICP outcomes. This positions our method as a superior alternative in the quest to improve MICP through engineered microbial consortia.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 2462816
• Report Number(s): PNNL--36693
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
59 BASIC BIOLOGICAL SCIENCES
bayesian methodology