Biology subjects

Winkle, J. J.

Publications and source records attributed to Winkle, J. J..

5 recordsLinked to original sources

bioRxiv2023

Signaling in microbial communities with open boundaries

Microbial communities such as swarms or biofilms often form at the interfaces of solid substrates and open fluid flows. At the same time, in laboratory environments these communities are commonly studied using microfluidic devices with media flows and open boundaries. Extracellular signaling within these communities is therefore subject to different constraints than signaling within classic, closed-boundary systems such as developing embryos or tissues, yet is understudied by comparison. Here, we use mathematical modeling to show how advective-diffusive boundary flows and population geometry impact cell-cell signaling in monolayer microbial communities. We reveal conditions where the intercellular signaling lengthscale depends solely on the population geometry and not on diffusion or degradation, as commonly expected. We further demonstrate that diffusive coupling with the boundary flow can produce signal gradients within an isogenic population, even when there is no flow within the population. We use our theory to provide new insights into the signaling mechanisms of published experimental results, and we make several experimentally verifiable predictions. Our research highlights the importance of carefully evaluating boundary dynamics and environmental geometry when modeling microbial cell-cell signaling and informs the study of cell behaviors in both natural and synthetic systems. O_TEXTBOXSIGNIFICANCEMicrobial communities in natural environments and microfluidic devices are often exposed to open boundaries and flows, but models used to characterize diffusive signaling in such systems often ignore how device geometry, boundary conditions, and media flow influence signaling behavior. We demonstrate how the effective signaling capacity of these communities can be shaped by population geometry and advective-diffusive boundary flow in quasi-2D environments. Our approach provides a general framework to understand and control advection-reaction-diffusion systems--and their interactions with cellular signaling networks--in both natural and synthetic environments. C_TEXTBOX

biophysics↗

bioRxiv2021

Stochastic Neural Networks for Automatic Cell Tracking in Microscopy Image Sequences of Bacterial Colonies

We describe an automated analysis method to quantify the detailed growth dynamics of a population of bacilliform bacteria. We propose an innovative approach to frame-sequence tracking of deformable-cell motion by the automated minimization of a new, specific cost functional. This minimization is implemented by dedicated Boltzmann machines (stochastic recurrent neural networks). Automated detection of cell divisions is handled similarly by successive minimizations of two cost functions, alternating the identification of children pairs and parent identification. We validate this automatic cell tracking algorithm using recordings of simulated cell colonies that closely mimic the growth dynamics of E. coli in microfluidic traps. On a batch of 1100 image frames, cell registration accuracies per frame ranged from 94.5% to 100%, with a high average. Our initial tests using experimental image sequences of E. coli colonies also yield convincing results, with a registration accuracy ranging from 90% to 100%.

bioinformatics↗

bioRxiv2021

Emergent spatiotemporal population dynamics with cell-length control of synthetic microbial consortia

AO_SCPLOWBSTRACTC_SCPLOWIncreased complexity of engineered microbial biocircuits highlights the need for distributed cell functionality due to concomitant increases of metabolic and regulatory burdens imposed on single-strain topologies. Distributed systems, however, introduce additional challenges since consortium composition and spatiotemporal dynamics of constituent strains must be robustly controlled to achieve desired circuit behaviors. Here, we address these challenges with a modeling-based investigation of emergent spatiotemporal population dynamics that result from cell-length control of monolayer, two-strain bacterial consortia. We demonstrate that with dynamic control of a strains division length, nematic cell alignment in close-packed monolayers can be destabilized. We found this destabilization conferred an emergent, competitive advantage on smaller-length strains--but by mechanisms that differed depending on the spatial patterns of the population. We used complementary modeling approaches to elucidate underlying mechanisms: an agent-based model to simulate detailed mechanical and signaling interactions between the competing strains and a reductive, stochastic lattice model to represent cell-cell interactions with a single rotational parameter. Our modeling suggests that spatial strain-fraction oscillations can be generated when cell-length control is coupled to quorum-sensing signaling in negative feedback topologies. Our research employs novel methods of population control and points the way to programming strain fraction dynamics in consortial synthetic biology. Engineered microbial collectives are more versatile and robust than single strain populations. However, the function of such collectives is sensitive to their spatiotemporal organization. Here, we demonstrate control of the spatiotemporal composition of synthetic microbial consortia by dynamically modulating the average cell length of constituent strains. Such modulation confers an emergent "mechanical fitness" advantage upon the shorter length strain. We used both a biophysically realistic agent-based model to test the impact of cell shape on spatiotemporal dynamics and a conceptually simpler stochastic lattice model to explain the essential mechanisms driving the dynamics.

biophysics↗

bioRxiv2019

Spatiotemporal dynamics of synthetic microbial consortia in microfluidic devices

Synthetic microbial consortia consist of two or more engineered strains that grow together and share the same resources. When intercellular signaling pathways are included in the engineered strains, close proximity of the microbes can generate complex dynamic behaviors that are difficult to obtain using a single strain. However, when a consortium is not cultured in a well-mixed environment the constituent strains passively compete for space as they grow and divide, complicating cell-cell signaling. Here, we explore the temporal dynamics of the spatial distribution of consortia co-cultured in microfluidic devices. To do this, we grew two different strains of Escherichia coli in microfluidic devices with cell-trapping regions (traps) of several different designs. We found that the size and shape of the traps are critical determinants of spatiotemporal dynamics. In small traps, cells can easily signal one another but the relative proportion of each strain within the trap can fluctuate wildly. In large traps, the relative ratio of strains is stabilized, but intercellular signaling can be hindered by distances between cells. This presents a trade-off between the trap size and the effectiveness of intercellular signaling, which can be mitigated by controlling the initial seeding of cells in the large trap. These results show how synthetic microbial consortia behave in microfluidic traps and provide a method to help remedy the spatial heterogeneity inherent to different trap geometries.

synthetic biology↗

bioRxiv2017

Modeling mechanical interactions in growing populations of rod-shaped bacteria

Advances in synthetic biology allow us to engineer bacterial collectives with pre-specified characteristics. However, the behavior of these collectives is difficult to understand, as cellular growth and division as well as extra-cellular fluid flow lead to complex, changing arrangements of cells within the population. To rationally engineer and control the behavior of cell collectives we need theoretical and computational tools to understand their emergent spatiotemporal dynamics. Here, we present an agent-based model that allows growing cells to detect and respond to mechanical interactions. Crucially, our model couples the dynamics of cell growth to the cells environment: Mechanical constraints can affect cellular growth rate and a cell may alter its behavior in response to these constraints. This coupling links the mechanical forces that influence cell growth and emergent behaviors in cell assemblies. We illustrate our approach by showing how mechanical interactions can impact the dynamics of bacterial collectives growing in microfluidic traps.

synthetic biology↗