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PhD Defense: Poonam Phalak, “Metabolic Modeling of Multispecies Microbial Biofilms”

Date/Time: 

Tuesday, July 9, 2019 - 3:00pm

Location: 

N610 Life Science Laboratories

Details: 

Abstract:

Biofilms are ubiquitous in medical, environmental, and engineered microbial systems. The majority of naturally occurring microbes grow as mixed species biofilms. These complicated biofilm consortia are comprised of many cell phenotypes with complex interactions and self-organized into three-dimensional structures. Approximately 2% of the US population suffers from non-healing chronic wounds infected by a combination of commensal and pathogenic bacteria whereas about 500,000 cases of Clostridium difficile infections (CDI) are reported annually. These polymicrobial infections are often resilient to antibiotic treatment due to the nutrient-rich environments within the biofilms and species interactions that promote community stability and robustness. This thesis focuses on developing metabolic modeling framework to study the interactions and the spatial/temporal organizations in the biofilms. The modeling framework is based on solving genome scale metabolic reconstructions of considered species to predict species abundances, growth rates and byproduct secretions. 

The spatiotemporal modeling framework accounts for the nutrient concentration gradients in the biofilm system. Spatiotemporal biofilm metabolic models were formulated by combining genome scale metabolic reconstructions of considered species with uptake kinetics for available nutrients and reaction-diffusion type equations for species biomass, supplied substrates and synthesized metabolic byproducts. The resulting partial differential equations embedded with linear programs were discretized in the space and integrated using a dynamic flux balance method. This framework was used to calculate the spatial and temporal variations in the species, nutrient and byproduct concentrations in biofilms. This framework was applied to analyze the biofilms associated with chronic wound infections, CDI and environmental biofilms. The chronic wound biofilm model was comprising of two most dominant species, Pseudomonas aeruginosa and Staphylococcus aureus. The model predicted partitioning of species based on their nutritional niches consistent with in vitro experiments.  The CDI biofilm model was comprising of representative species from three most common phyla in gut Bacteroidetes thetaiotaomicron, Faecalibacterium prausnitzii, Escherichia coli and pathogen C. difficile. The simulation results were used to study the interspecies interactions, the spatial partitioning in the biofilms and important crossfeeding relationships within the community. These predictions would be useful in devising effective antibiotic treatment strategies to cure the biofilm infections associated with chronic wounds and C. difficile. The environmental biofilm model for cyanobacteria and heterotrophs was developed and validated with the experimental results, this model was used to evaluate the community dynamics under extreme environmental conditions.  

The steady state community modeling framework considered biofilm as a well-mixed homogeneous system at steady state. This framework can be used when the community is large and cannot be easily solved as a spatiotemporal biofilm. Steady state in silico community models were formulated by combining genome scale metabolic reconstructions of the considered species. The community models were solved using SteadyCom method. This method uses community flux balance analysis to calculate the relative abundance of each species with an objective of maximizing the community growth rate.12 species chronic wound community metabolic model covering 74% of 16S rDNA pyrosequencing reads of dominant genera from 2,963 chronic wound patients was developed. The community model was used to predict species abundances averaged across this large patient population. The simulation results from this study were used to identify putative mutualistic interactions between bacteria that could be targeted to enhance treatment efficacy. The frameworks developed in this thesis would be useful in developing patient/disease specific therapeutic treatments. 

 

 

 
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