Parametric studies of metabolic cooperativity in Escherichia coli colonies: Strain and geometric confinement effects

Biophysical metabolic modeling of complex bacterial colony morphology

Microbial colony growth is shaped by the physics of biomass propagation and nutrient diffusion, and by the metabolic reactions that organisms activate as a function of the surrounding environment. While microbial colonies have been explored using minimal models of growth and motility, full integration of biomass propagation and metabolism is still lacking. Here, building upon our framework for Computation of Microbial Ecosystems in Time and Space (COMETS), we combine dynamic flux balance modeling of metabolism with collective biomass propagation and demographic fluctuations to provide nuanced simulations of E. coli colonies. Simulations produced realistic colony morphology, consistent with our experiments. They characterize the transition between smooth and furcated colonies and the decay of genetic diversity. Furthermore, we demonstrate that under certain conditions, biomass can accumulate along "metabolic rings" that are reminiscent of coffee-stain rings, but have a completely different origin. Our approach is a key step towards predictive microbial ecosystems modeling.

Cooperative metabolic resource allocation in spatially-structured systems

Natural selection has shaped the evolution of cells and multi-cellular organisms such that social cooperation can often be preferred over an individualistic approach to metabolic regulation. This paper extends a framework for dynamic metabolic resource allocation based on the maximum entropy principle to spatiotemporal models of metabolism with cooperation. Much like the maximum entropy principle encapsulates 'bet-hedging' behaviour displayed by organisms dealing with future uncertainty in a fluctuating environment, its cooperative extension describes how individuals adapt their metabolic resource allocation strategy to further accommodate limited knowledge about the welfare of others within a community. The resulting theory explains why local regulation of metabolic cross-feeding can fulfil a community-wide metabolic objective if individuals take into consideration an ensemble measure of total population performance as the only form of global information. The latter is likely supplied by quorum sensing in microbial systems or signalling molecules such as hormones in multi-cellular eukaryotic organisms.

Using chaotic advection for facile high-throughput fabrication of ordered multilayer micro- and nanostructures: continuous chaotic printing

This paper introduces the concept of continuous chaotic printing, i.e. the use of chaotic flows for deterministic and continuous extrusion of fibers with internal multilayered micro- or nanostructures. Two free-flowing materials are coextruded through a printhead containing a miniaturized Kenics static mixer (KSM) composed of multiple helicoidal elements. This produces a fiber with a well-defined internal multilayer microarchitecture at high-throughput (>1.0 m min^-1). The number of mixing elements and the printhead diameter determine the number and thickness of the internal lamellae, which are generated according to successive bifurcations that yield a vast amount of inter-material surface area (∼10² cm² cm^-3) at high resolution (∼10 µm). This creates structures with extremely high surface area to volume ratio (SAV). Comparison of experimental and computational results demonstrates that continuous chaotic 3D printing is a robust process with predictable output. In an exciting new development, we demonstrate a method for scaling down these microstructures by 3 orders of magnitude, to the nanoscale level (∼150 nm), by feeding the output of a continuous chaotic 3D printhead into an electrospinner. The simplicity and high resolution of continuous chaotic printing strongly supports its potential use in novel applications, including-but not limited to-bioprinting of multi-scale layered biological structures such as bacterial communities, living tissues composed of organized multiple mammalian cell types, and fabrication of smart multi-material and multilayered constructs for biomedical applications.

Metabolism in dense microbial colonies: 13C metabolic flux analysis of E. coli grown on agar identifies two distinct cell populations with acetate cross-feeding

In this study, we have investigated for the first time the metabolism of E. coli grown on agar using ¹³C metabolic flux analysis (¹³C-MFA). To date, all ¹³C-MFA studies on microbes have been performed with cells grown in liquid culture. Here, we extend the scope of ¹³C-MFA to biological systems where cells are grown in dense microbial colonies. First, we identified new optimal ¹³C tracers to quantify fluxes in systems where the acetate yield cannot be easily measured. We determined that three parallel labeling experiments with the tracers [1,2-¹³C]glucose, [1,6-¹³C]glucose, and [4,5,6-¹³C]glucose permit precise estimation of not only intracellular fluxes, but also of the amount of acetate produced from glucose. Parallel labeling experiments were then performed with wild-type E. coli and E. coli ΔackA grown in liquid culture and on agar plates. Initial attempts to fit the labeling data from wild-type E. coli grown on agar did not produce a statistically acceptable fit. To resolve this issue, we employed the recently developed co-culture ¹³C-MFA approach, where two E. coli subpopulations were defined in the model that engaged in metabolite cross-feeding. The flux results identified two distinct E. coli cell populations, a dominant cell population (92% of cells) that metabolized glucose via conventional metabolic pathways and secreted a large amount of acetate (~40% of maximum theoretical yield), and a second smaller cell population (8% of cells) that consumed the secreted acetate without any glucose influx. These experimental results are in good agreement with recent theoretical simulations. Importantly, this study provides a solid foundation for future investigations of a wide range of problems involving microbial biofilms that are of great interest in biotechnology, ecology and medicine, where metabolite cross-feeding between cell populations is a core feature of the communities.

Simulating biological processes: stochastic physics from whole cells to colonies

The last few decades have revealed the living cell to be a crowded spatially heterogeneous space teeming with biomolecules whose concentrations and activities are governed by intrinsically random forces. It is from this randomness, however, that a vast array of precisely timed and intricately coordinated biological functions emerge that give rise to the complex forms and behaviors we see in the biosphere around us. This seemingly paradoxical nature of life has drawn the interest of an increasing number of physicists, and recent years have seen stochastic modeling grow into a major subdiscipline within biological physics. Here we review some of the major advances that have shaped our understanding of stochasticity in biology. We begin with some historical context, outlining a string of important experimental results that motivated the development of stochastic modeling. We then embark upon a fairly rigorous treatment of the simulation methods that are currently available for the treatment of stochastic biological models, with an eye toward comparing and contrasting their realms of applicability, and the care that must be taken when parameterizing them. Following that, we describe how stochasticity impacts several key biological functions, including transcription, translation, ribosome biogenesis, chromosome replication, and metabolism, before considering how the functions may be coupled into a comprehensive model of a 'minimal cell'. Finally, we close with our expectation for the future of the field, focusing on how mesoscopic stochastic methods may be augmented with atomic-scale molecular modeling approaches in order to understand life across a range of length and time scales.