Biology subjects

Bernstein, D. B.

Publications and source records attributed to Bernstein, D. B..

4 recordsLinked to original sources

bioRxiv2026

Equation-Based Integration of Flux Balance Analysis with Diffusion for Spatio-Temporal Simulation of Microbial Communities

Spatio-temporal interactions shape microbial community dynamics. Metabolism, through competition and cross-feeding, is a foundational mechanism of these interactions. Flux balance analysis enables efficient simulation of steady-state metabolism. Integrating these simulations through time, using dynamic flux balance analysis, provides temporal predictions of growth and metabolism. Incorporating spatial context, through partial differential equations, enables spatio-temporal simulation of microbial communities. In this chapter, we step through this sequential process, moving from steady-state, to temporal, to spatio-temporal simulation of microbial community metabolism. We provide an illustrative example using the modeling software COMETS (Computation of Microbial Ecosystems in Time and Space) to simulate interacting bacterial colonies of Bifidobacterium longum subsp. infantis and Anaerobutyricum hallii (previously Eubacterium hallii). Within this simulation, both competition and cross-feeding influenced the production of butyrate leading to an intermediate optimal interaction distance for metabolite production. We outline each step and provide open-source code such that this simulation can serve as a template for future spatio-temporal simulations of microbial community metabolism.

systems biology↗

bioRxiv2023

Critical assessment of E. coli genome-scale metabolic model with high-throughput mutant fitness data

The E. coli genome-scale metabolic model (GEM) is a gold standard for the simulation of cellular metabolism. Experimental validation of model predictions is essential to pinpoint model uncertainty and ensure continued development of accurate models. Here we assessed the accuracy of the E. coli GEM using published mutant fitness data for the growth of gene knockout mutants across thousands of genes and 25 different carbon sources. We explored the progress of the E. coli GEM versions over time and further investigated errors in the latest version of the model (iML1515). We observed that model size is increasing while prediction accuracy is decreasing. We identified several adjustments that improve model accuracy - the addition of vitamins/cofactors and re-assignment of reaction reversibility and isoenzyme gene to reaction mapping. Furthermore, we applied a machine learning approach which identified hydrogen ion exchange and central metabolism branch points as important determinants of model accuracy. Continued integration of experimental data to validate GEMs will improve predictive modeling of the mapping from genotype to metabolic phenotype in E. coli and beyond. Synopsis O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY E. coli genome-scale metabolic model flux balance analysis (FBA) prediction accuracy was quantified with published experimental data assaying gene knockout mutant growth across different carbon sources. Insights into model development trends and sources of inaccuracy were revealed. O_LIModel representational power (size) has been increasing over time, while accuracy has been decreasing. C_LIO_LIAdding vitamins/cofactors to the model environment and re-assigning reaction reversibility and isoenzyme gene-to-reaction mapping improves correspondence between model predictions and experimental data. C_LIO_LIMachine learning reveals hydrogen ion exchange and central metabolism branch points as important features in the determination of model accuracy. C_LI

systems biology↗

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↗