scholarly journals Coupling a genome-scale metabolic model with a reactive transport model to describe in situ uranium bioremediation

2009 ◽  
Vol 2 (2) ◽  
pp. 274-286 ◽  
Author(s):  
Timothy D. Scheibe ◽  
Radhakrishnan Mahadevan ◽  
Yilin Fang ◽  
Srinath Garg ◽  
Philip E. Long ◽  
...  
Keyword(s):  
Reactive Transport ◽  
Transport Model ◽  
Metabolic Model ◽  
Reactive Transport Model ◽  
A Genome ◽  
Genome Scale

Direct coupling of a genome-scale microbial in silico model and a groundwater reactive transport model

2011 ◽  
Vol 122 (1-4) ◽  
pp. 96-103 ◽  
Author(s):  
Yilin Fang ◽  
Timothy D. Scheibe ◽  
Radhakrishnan Mahadevan ◽  
Srinath Garg ◽  
Philip E. Long ◽  
...  
Keyword(s):  
In Silico ◽  
Reactive Transport ◽  
Transport Model ◽  
Direct Coupling ◽  
Reactive Transport Model ◽  
In Silico Model ◽  
A Genome ◽  
Genome Scale

Pore-scale simulation of microbial growth using a genome-scale metabolic model: Implications for Darcy-scale reactive transport

2013 ◽  
Vol 59 ◽  
pp. 256-270 ◽  
Author(s):  
G.D. Tartakovsky ◽  
A.M. Tartakovsky ◽  
T.D. Scheibe ◽  
Y. Fang ◽  
R. Mahadevan ◽  
...  
Keyword(s):  
Reactive Transport ◽  
Microbial Growth ◽  
Metabolic Model ◽  
Pore Scale ◽  
A Genome ◽  
Genome Scale

scholarly journals Vertical Distribution of Denitrification in an Estuarine Sediment: Integrating Sediment Flowthrough Reactor Experiments and Microprofiling via Reactive Transport Modeling

2006 ◽  
Vol 73 (1) ◽  
pp. 40-47 ◽  
Author(s):  
Anniet M. Laverman ◽  
Christof Meile ◽  
Philippe Van Cappellen ◽  
Elze B. A. Wieringa
Keyword(s):  
Vertical Distribution ◽  
Reactive Transport ◽  
Nitrate Uptake ◽  
Transport Model ◽  
Depth Interval ◽  
Overlying Water ◽  
Near Surface ◽  
Reactive Transport Model ◽  
Potential Rate

ABSTRACT Denitrifying activity in a sediment from the freshwater part of a polluted estuary in northwest Europe was quantified using two independent approaches. High-resolution N2O microprofiles were recorded in sediment cores to which acetylene was added to the overlying water and injected laterally into the sediment. The vertical distribution of the rate of denitrification supported by nitrate uptake from the overlying water was then derived from the time series N2O concentration profiles. The rates obtained for the core incubations were compared to the rates predicted by a forward reactive transport model, which included rate expression for denitrification calibrated with potential rate measurements obtained in flowthrough reactors containing undisturbed, 1-cm-thick sediment slices. The two approaches yielded comparable rate profiles, with a near-surface, 2- to 3-mm narrow zone of denitrification and maximum in situ rates on the order of 200 to 300 nmol cm−3 h−1. The maximum in situ rates were about twofold lower than the maximum potential rate for the 0- to 1-cm depth interval of the sediment, indicating that in situ denitrification was nitrate limited. The experimentally and model-derived rates of denitrification implied that there was nitrate uptake by the sediment at a rate that was on the order of 50 (± 10) nmol cm−2 h−1, which agreed well with direct nitrate flux measurements for core incubations. Reactive transport model calculations showed that benthic uptake of nitrate at the site is particularly sensitive to the nitrate concentration in the overlying water and the maximum potential rate of denitrification in the sediment.

scholarly journals Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model

2021 ◽  
Vol 412 ◽  
pp. 115390
Author(s):  
Kristopher D. Rawls ◽  
Bonnie V. Dougherty ◽  
Kalyan C. Vinnakota ◽  
Venkat R. Pannala ◽  
Anders Wallqvist ◽  
...  
Keyword(s):  
Metabolic Model ◽  
Renal Metabolism ◽  
A Genome ◽  
Compound Exposure ◽  
Genome Scale

scholarly journals Evaluation of a Genome-ScaleIn SilicoMetabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

2012 ◽  
Vol 78 (24) ◽  
pp. 8735-8742 ◽  
Author(s):  
Yilin Fang ◽  
Michael J. Wilkins ◽  
Steven B. Yabusaki ◽  
Mary S. Lipton ◽  
Philip E. Long
Keyword(s):  
Proteomic Analysis ◽  
In Silico ◽  
Field Experiments ◽  
Metabolic Model ◽  
Geobacter Metallireducens ◽  
Content Type ◽  
Proteomic Data ◽  
Subsurface Environment ◽  
A Genome ◽  
Genome Scale

ABSTRACTAccurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within anin silicomodel using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model ofGeobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-basedin silicomodelof G. metallireducensrelates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637G. metallireducensproteins detected during the 2008 experiment were associated with specific metabolic reactions in thein silicomodel. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through thein silicomodel reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in thein silicomodel that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.

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