(2d) Modeling interspecies competition and exchanges in microbial communities

Interest in understanding and manipulating microbial communities is growing across numerous fields in medicine, ecology, agriculture, and industry. The immense chemical complexity of these microbial systems has only recently been elucidated, yet the nuances of interspecies competition and exchanges, and the effects of these interactions upon metabolic phenotypes, remain opaque. This impedes the engineering of communities for research and end-products in all of these application spaces.

Here we describe a series of modeling capabilities that we have developed to aid in resolving the nuances of microbial communities. This includes tools developed to a) build microbial community models based on input genome sequences; b) predict species interactions with a wide range of physiological and ecological constraints; c) correct model errors that misalign with experimental data; d) fit microbial growth parameters to experimental data; and e) predict community responses to changes in environmental conditions and microbial content. Our tools are deployed as point and click apps within the KBase environment, and are conveniently available as Python APIs for which ample documentation and Jupyter Notebooks are available.

We specifically applied these tools to examine community competition between E. coli and P. fluorescens – a relevant simple bacterial community for industry and the environment – where exchanges of primary carbon sources perturb metabolic phenotypes. The modeling tools elucidated a) the kinetics, thermodynamics, and reaction fluxes of trophic interactions and b) the phenotypic perturbations in response to community competition and evolving environmental conditions. The modeling results suggested engineering alterations to offer a competitive advantage to E. coli, which are currently being experimentally validated. This exemplifies the applicability of our modeling library to rationally design and manipulate communities, which will become increasingly important for medicine, industry, and ecology in the coming decades.

Research Interests

My research interests include developing computational tools that examine chemical systems for both basic understanding and engineering applications. This naturally leads to the study of biology, bioengineering, and synthetic biology which can be effectively simulated at the chemical-level. I have also examined abiotic chemical systems, such as the reactive transport of Reverse Osmosis desalination that is defined by complex geochemical equilibria. My tools are primarily open-source Python packages with full documentation that permit non-technical users, including experimentalists, to intuitively utilize the software.

Teaching Interests

I am interested to incorporate more sustainable design and systems-level thinking into conventional curriculum. I believe that this perspective is increasingly attractive to employers and will benefit students in the competitive domestic economy. I designed an upper-level Green Chemistry laboratory course (available on Zenodo) that achieves this goal while rehearsing traditional wet-lab skills in undergraduate curriculum.