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 a large number of cell phenotypes with complex interactions and self-organize into three-dimensional structures. While foundational to the vast majority of microbial life on the planet, the basic design principles of consortia biofilms are still poorly understood. We believe that a combination of computational and experimental tools is needed to address the challenge of characterizing, predicting and treating these complex systems. The overarching goal of this project is to develop an experimentally driven, predictive multiscale model that generates quantitatively accurate predictions of biofilm formation dynamics, species distributions and responses to perturbations. The biofilm model will be formulated by combining genome-scale metabolic reconstructions of individual species with reaction-diffusion equations for nutrient, metabolic byproduct and antibiotic transport through the biofilm. The biofilm model will be multiscale with respect to both time and length scales, with the metabolic models linking individual genes to cellular dynamics and the consortia model linking individual cells to community dynamics. The research will be developed using a medically relevant, three species chronic wound model system. Treatment of chronic wounds costs the US in excess of $25 billion per year and the costs are anticipated to grow rapidly due to the rise in diabetes and obesity.
The specific aims of the proposed research are: (1) construct and evaluate a multiscale metabolic modeling framework for multispecies consortia biofilms in a dynamic, spatially resolved format;(2) develop and implement spatially resolved biofilm analytical methods to quantify physiologies of consortia and monoculture biofilms to inform and validate the computational model;and (3) predict and test spatially resolved metabolic responses to culturing perturbations including antibiotic treatments with iterative loops of hypothesis refinement. Expected outcomes of the proposed research are: (1) a general methodology and associated software tools for multiscale modeling of microbial biofilms to predict consortia dynamics in heterogeneous environments suitable for distribution to the multiscale modeling community;(2) experimental tools for spatially-resolved measurement of mass and energy balances within interacting consortia biofilms;(3) the first perturbation analysis of monoculture and multispecies chronic wound biofilms to nutrient variations and antibiotic treatments;and (4) proposed therapeutic strategies for effectively treating microbial consortia biofilms.
Biofilms are polymer encapsulated microorganism communities;they are ubiquitous in medical, environmental, and industrial settings but are poorly understood and are difficult to control. The overarching goal of this project is to develop an experimentally driven, predictive computer model that generates accurate predictions of biofilm behavior. The predictions will be used to develop rational therapeutic strategies for treating biofilms such as those found in chronic nonhealing wounds.
|Henson, Michael A; Phalak, Poonam (2017) Microbiota dysbiosis in inflammatory bowel diseases: in silico investigation of the oxygen hypothesis. BMC Syst Biol 11:145|
|Venters, Michael; Carlson, Ross P; Gedeon, Tomas et al. (2017) Effects of Spatial Localization on Microbial Consortia Growth. PLoS One 12:e0168592|
|Yung, Yeni P; Wickramasinghe, Raveendra; Vaikkinen, Anu et al. (2017) Solid Sampling with a Diode Laser for Portable Ambient Mass Spectrometry. Anal Chem 89:7297-7301|
|Hunt, Kristopher A; Jennings, Ryan deM; Inskeep, William P et al. (2016) Stoichiometric modelling of assimilatory and dissimilatory biomass utilisation in a microbial community. Environ Microbiol 18:4946-4960|
|Chen, Jin; Gomez, Jose A; Höffner, Kai et al. (2016) Spatiotemporal modeling of microbial metabolism. BMC Syst Biol 10:21|
|Phalak, Poonam; Chen, Jin; Carlson, Ross P et al. (2016) Metabolic modeling of a chronic wound biofilm consortium predicts spatial partitioning of bacterial species. BMC Syst Biol 10:90|
|Henson, Michael A (2015) Genome-scale modelling of microbial metabolism with temporal and spatial resolution. Biochem Soc Trans 43:1164-71|