Model-based analysis of the role of biological, hydrological and geochemical factors affecting uranium bioremediation

On Modeling Ensemble Transport of Metal Reducing Motile Bacteria

Many metal reducing bacteria are motile with their run-and-tumble behavior exhibiting series of flights and waiting-time spanning multiple orders of magnitude. While several models of bacterial processes do not consider their ensemble motion, some models treat motility using an advection diffusion equation (ADE). In this study, Geobacter and Pelosinus, two metal reducing species, are used in micromodel experiments for study of their motility characteristics. Trajectories of individual cells on the order of several seconds to few minutes in duration are analyzed to provide information on (1) the length of runs, and (2) time needed to complete a run (waiting or residence time). A Continuous Time Random Walk (CTRW) model to predict ensemble breakthrough plots is developed based on the motility statistics. The results of the CTRW model and an ADE model are compared with the real breakthrough plots obtained directly from the trajectories. The ADE model is shown to be insufficient, whereas a coupled CTRW model is found to be good at predicting breakthroughs at short distances and at early times, but not at late time and long distances. The inadequacies of the simple CTRW model can possibly be improved by accounting for correlation in run length and waiting time.

d-OptCom: Dynamic multi-level and multi-objective metabolic modeling of microbial communities

Most microbial communities change with time in response to changes and/or perturbations in environmental conditions. Temporal variations in interspecies metabolic interactions within these communities can significantly affect their structure and function. Here, we introduce d-OptCom, an extension of the OptCom procedure, for the dynamic metabolic modeling of microbial communities. It enables capturing the temporal dynamics of biomass concentration of the community members and extracellular concentration of the shared metabolites, while integrating species- and community-level fitness functions. The applicability of d-OptCom was demonstrated by modeling the dynamic co-growth of auxotrophic mutant pairs of E. coli and by computationally assessing the dynamics and composition of a uranium-reducing community comprised of Geobacter sulfurreducens, Rhodoferax ferrireducens, and Shewanella oneidensis. d-OptCom was also employed to examine the impact of lactate vs acetate addition on the relative abundance of uranium-reducing species. These studies highlight the importance of simultaneously accounting for both species- and community-level fitness functions when modeling microbial communities, and demonstrate that the incorporation of uptake kinetic information can substantially improve the prediction of interspecies flux trafficking. Overall, this study paves the way for the dynamic multi-level and multi-objective analysis of microbial ecosystems.

Analysis and design of a genetic circuit for dynamic metabolic engineering

Recent advances in synthetic biology have equipped us with new tools for bioprocess optimization at the genetic level. Previously, we have presented an integrated in silico design for the dynamic control of gene expression based on a density-sensing unit and a genetic toggle switch. In the present paper, analysis of a serine-producing Escherichia coli mutant shows that an instantaneous ON-OFF switch leads to a maximum theoretical productivity improvement of 29.6% compared to the mutant. To further the design, global sensitivity analysis is applied here to a mathematical model of serine production in E. coli coupled with a genetic circuit. The model of the quorum sensing and the toggle switch involves 13 parameters of which 3 are identified as having a significant effect on serine concentration. Simulations conducted in this reduced parameter space further identified the optimal ranges for these 3 key parameters to achieve productivity values close to the maximum theoretical values. This analysis can now be used to guide the experimental implementation of a dynamic metabolic engineering strategy and reduce the time required to design the genetic circuit components.

The design of long-term effective uranium bioremediation strategy using a community metabolic model

Acetate amendment at uranium contaminated sites in Rifle, CO. leads to an initial bloom of Geobacter accompanied by the removal of U(VI) from the groundwater, followed by an increase of sulfate-reducing bacteria (SRBs) which are poor reducers of U(VI). One of the challenges associated with bioremediation is the decay in Geobacter abundance, which has been attributed to the depletion of bio-accessible Fe(III), motivating the investigation of simultaneous amendments of acetate and Fe(III) as an alternative bioremediation strategy. In order to understand the community metabolism of Geobacter and SRBs during artificial substrate amendment, we have created a genome-scale dynamic community model of Geobacter and SRBs using the previously described Dynamic Multi-species Metabolic Modeling framework. Optimization techniques are used to determine the optimal acetate and Fe(III) addition profile. Field-scale simulation of acetate addition accurately predicted the in situ data. The simulations suggest that batch amendment of Fe(III) along with continuous acetate addition is insufficient to promote long-term bioremediation, while continuous amendment of Fe(III) along with continuous acetate addition is sufficient to promote long-term bioremediation. By computationally minimizing the acetate and Fe(III) addition rates as well as the difference between the predicted and target uranium concentration, we showed that it is possible to maintain the uranium concentration below the environmental safety standard while minimizing the cost of chemical additions. These simulations show that simultaneous addition of acetate and Fe(III) has the potential to be an effective uranium bioremediation strategy. They also show that computational modeling of microbial community is an important tool to design effective strategies for practical applications in environmental biotechnology.