Bernstein, D. B. | Search bioRxiv

bioRxiv2021

A co-culture microplate for real-time measurement of microbial interactions

The dynamic structures of microbial communities emerge from the complex network of interactions between their constituent microbial organisms. Quantitative measurements of these microbial interactions are important for understanding and engineering microbial community structure. Here, we present the development and application of the BioMe plate, a redesigned microplate device in which pairs of wells are separated by porous membranes. BioMe facilitates the measurement of dynamic microbial interactions and integrates easily with standard laboratory equipment. We first applied BioMe to recapitulate recently characterized, natural symbiotic interactions between bacteria isolated from the D. melanogaster gut microbiome. Specifically, the BioMe plate allowed us to observe the benefit provided by two Lactobacilli strains to an Acetobacter strain. We next explored the use of BioMe to gain quantitative insight into the engineered obligate syntrophic interaction between a pair of E. coli amino acid auxotrophs. We integrated experimental observations with a mechanistic computational model to quantify key parameters associated with this syntrophic interaction, including metabolite secretion and diffusion rates. This model also allowed us to explain the slow growth observed for auxotrophs growing in adjacent wells, by demonstrating that under the relevant range of parameters, local exchange between auxotrophs is essential for efficient growth. The BioMe plate provides a scalable and flexible approach for the study of dynamic microbial interactions.

bioengineering↗

bioRxiv2020

Stoichiometric Modelling of String Chemistries

Uncovering the general principles that govern the architecture of metabolic networks is key to understanding the emergence and evolution of living systems. Artificial chemistries, in silico representations of chemical reaction networks arising from a defined set of mathematical rules, can help address this challenge by enabling the exploration of alternative chemical universes and the possible metabolic networks that could emerge within them. Here we focus on artificial chemistries in which strings of characters represent simplified molecules, and string concatenation and splitting represent possible chemical reactions. We study string chemistries using tools borrowed from the field of stoichiometric constraint-based modeling of organismal metabolic networks, through a novel Python package, ARtificial CHemistry NEtwork Toolbox (ARCHNET). In addition to exploring the complexity and connectivity properties of different string chemistries, we developed a network-pruning algorithm that can generate minimal metabolic networks capable of producing a specified set of biomass precursors from a given assortment of environmental molecules within the string chemistry framework. We found that the identities of the metabolites in the biomass reaction wield much more influence over the structure of the minimal metabolic networks than the identities of the nutrient metabolites -- a notion that could help us better understand the rise and evolution of biochemical organization. Our work provides a bridge between artificial chemistries and stoichiometric modeling, which can help address a broad range of open questions, from the spontaneous emergence of an organized metabolism to the structure of microbial communities.

bioinformatics↗