Emergent Spatiotemporal Population Dynamics with Cell-Length Control of Synthetic Microbial Consortia

Abstract

Increased 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 strain's 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.

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

“…The massive effort involved in human expert annotation of cell colony recordings limits the availability of "ground truth tracking" data for dense bacterial colonies. Therefore, we first validated the accuracy of our cell tracking algorithms on recordings of simulated cell colonies, generated by the dedicated cell colony simulation software [12,81]. This provided us with ground truth frame-by-frame registration for cell lineages, enabling us to validate our methodology.…”

“…One key challenge here is to provide adequate training data for these methods to be successful. Here, we describe simulation-based techniques that can be extended to produce training data, which we use for parameter tuning [12,81].…”

“…To validate our cell tracking algorithms, we consider simulated image sequences of dense cell populations. We refer to [12,81] for a detailed description of this mathematical model and its implementation. (The code for generating the synthetic data has been released at https://github.com/jwinkle/eQ, accessed on 15 December 2021) The simulated cell colony dynamics are driven by an agent based model [12,81], which emulates live colonies of growing, moving, and dividing rod-like E. coli cells in a 2D microfluidic trap environment.…”

“…We refer to [12,81] for a detailed description of this mathematical model and its implementation. (The code for generating the synthetic data has been released at https://github.com/jwinkle/eQ, accessed on 15 December 2021) The simulated cell colony dynamics are driven by an agent based model [12,81], which emulates live colonies of growing, moving, and dividing rod-like E. coli cells in a 2D microfluidic trap environment. Between two successive frames J, J + , cells are allowed to move until they nearly bump into each other, and to grow at multiplicative rate denoted g.rate with an average value of 1.05 per minute.…”

“…The cells have a length of approximately 2 µm after division and 4 µm right before division. We refer to [81] for additional details. The simulation keeps track of cell lineage, cell size, and cell location (among other parameters).…”