A mechanistic understanding of microcolony morphogenesis: coexistence of mobile and sessile aggregates

Accurate prediction of the kinetic sequence of physicochemical states using generative artificial intelligence

Capturing the time evolution and predicting kinetic sequences of states of physicochemical systems present significant challenges due to the precision and computational effort required. In this study, we demonstrate that 'Generative Pre-trained Transformer (GPT)', an artificial intelligence model renowned for machine translation and natural language processing, can be effectively adapted to predict the dynamical state-to-state transition kinetics of biologically relevant physicochemical systems. Specifically, by using sequences of time-discretized states from Molecular Dynamics (MD) simulation trajectories akin to the vocabulary corpus of a language, we show that a GPT-based model can learn the complex syntactic and semantic relationships within the trajectory. This enables GPT to predict kinetically accurate sequences of states for a diverse set of biomolecules of varying complexity, at a much quicker pace than traditional MD simulations and with a better efficiency than other baseline time-series prediction approaches. More significantly, the approach is found to be equally adept at forecasting the time evolution of out-of-equilibrium active systems that do not maintain detailed balance. An analysis of the mechanism inherent in GPT reveals the crucial role of the 'self-attention mechanism' in capturing the long-range correlations necessary for accurate state-to-state transition predictions. Together, our results highlight generative artificial intelligence's ability to generate kinetic sequences of states of physicochemical systems with statistical precision.

Smectic-like bundle formation of planktonic bacteria upon nutrient starvation

Bacteria aggregate through various intercellular interactions to build biofilms, but the effect of environmental changes on them remains largely unexplored. Here, by using an experimental device that overcomes past difficulties, we observed the collective response of Escherichia coli aggregates to dynamic changes in the growth conditions. We discovered that nutrient starvation caused bacterial cells to arrange themselves into bundle-shaped clusters, developing a structure akin to that of smectic liquid crystals. The degree of the smectic-like bundle order was evaluated by a deep learning approach. Our experiments suggest that both the depletion attraction by extracellular polymeric substances and the growth arrest are essential for the bundle formation. Since these effects of nutrient starvation at the single-cell level are common to many bacterial species, bundle formation might also be a common collective behavior that bacterial cells may exhibit under harsh environments.

Matrix-producing cells' orientation order facilitates Bacillus subtilis biofilm self-healing

During the self-healing process of Bacillus subtilis biofilms on a solid MSgg substrate, large-scale ordered clusters emerge within the biofilm, providing an invasive advantages. To investigate the self-healing mechanism, an agent-based model is employed to simulate the self-healing processes of biofilms at two ages. The study reveals that a uniform cell distribution facilitates the healing of biofilm incisions. The nutrient diffusion rate within the biofilm and the elastic modulus (comprising cell and EPS) play a dominant role in the healing of circumferential incisions, while the diffusion rate outside the biofilm governs the healing of radial and penetrating incisions. These influencing factors can adjust cellular ordering, providing valuable insights for controlling the self-healing of Bacillus subtilis biofilms.

Diverse morphology and motility induced emergent order in bacterial collectives

Microbial communities exhibit complex behaviors driven by species interactions and individual characteristics. In this study, we delve into the dynamics of a mixed bacterial population comprising two distinct species with different morphology and motility aspects. Employing agent-based modeling and computer simulations, we analyze the impacts of size ratios and packing fractions on dispersal patterns, aggregate formation, clustering, and spatial ordering. Notably, we find that motility and anisotropy of elongated bacteria significantly influence the distribution and spatial organization of nonmotile spherical species. Passive spherical cells display a superdiffusive behavior, particularly at larger size ratios in the ballistic regime. As the size ratio increases, clustering of passive cells is observed, accompanied by enhanced alignment and closer packing of active cells in the presence of higher passive cell area fractions. In addition, we identify the pivotal role of passive cell area fraction in influencing the response of active cells toward nematicity, with its dependence on size ratio. These findings shed light on the significance of morphology and motility in shaping the collective behavior of microbial communities, providing valuable insights into complex microbial behaviors with implications for ecology, biotechnology, and bioengineering.

Trajectory analysis of Bacillus subtilis in micro-droplets

In order to study Bacillus subtilis biofilm formation in microdroplets, we use microfluidics technology to make the droplets and confocal microscopy to capture bacterial movement and biofilm formation in the droplets. We develop a multi-target tracking methodology, using a YOLOv5 detector to identify cells and a DeepSORT algorithm to track cell movements. We find that Bacillus subtilis bacteria with autonomous migration and biofilm-forming ability prefer to cluster and swarm near the microdroplet surface, rather than in the droplet interior. Bacterial mobility depends on phenotype and spatial location within the droplet. The motile cells move about 3.5 times faster than the matrix-producing cells. When the cells are near the wall of the droplet, the direction of the motion of motile cells is along that wall. When the cells are inside the droplet, the direction of the motion of motile cells is disordered, i.e., there is no clear directional or goal-oriented movement. This contrast increases the cell contact probability and facilitates the formation of a Bacillus subtilis biofilm in the droplet. Furthermore, we develop a mathematical model to describe the motion behavior of Bacillus subtilis in microdroplets, which is useful for exploring the influence of motility on biofilm formation.

Resource limitation and population fluctuation drive spatiotemporal order in microbial communities

Microbial communities display complex spatiotemporal behaviors leading to spatially-structured and ordered organization driven by species interactions and environmental factors. Resource availability plays a pivotal role in shaping the dynamics of bacterial colonies. In this study, we delve into the intricate interplay between resource limitation and the emergent properties of a growing colony of two visually distinct bacterial strains having similar growth and mechanical properties. Employing an agent-based modeling and computer simulations, we analyze the resource-driven effect on segregation and sectoring, cell length regulation and nematic ordering within a growing colony. We introduce a dimensionless parameter referred to as the active layer thickness, derived from nutrient diffusion equations, indicating effective population participation due to local resource availability. Our results reveal that lower values of active layer thickness arising from decreased resource abundance lead to rougher colony fronts, fostering heightened population fluctuations within the colony and faster spatial genetic diversity loss. Our temporal analyses unveil the dynamics of mean cell length and fluctuations, showcasing how initial disturbances evolve as colonies are exposed to nutrients and subsequently settle. Furthermore, examining microscopic details, we find that lower resource levels yield diverse cell lengths and enhanced nematic ordering, driven by the increased prevalence of longer rod-shaped cells. Our investigation sheds light on the multifaceted relationship between resource constraints and bacterial colony dynamics, revealing insights into their spatiotemporal organization.

Interplay of cell motility and self-secreted extracellular polymeric substance induced depletion effects on spatial patterning in a growing microbial colony

Reproducing bacteria self-organize to develop patterned biofilms in various conditions. Various factors contribute to the shaping of a multicellular bacterial organization. Here we investigate how motility force and self-secreted extracellular polymeric substances (EPS) influence bacterial cell aggregation, leading to phase-separated colonies using a particle-based/individual-based model. Our findings highlight the critical role of the interplay between motility force and depletion effects in regulating phase separation within a growing colony under far-from-equilibrium conditions. We observe that increased motility force hinders depletion-induced cell aggregation and phase segregation, necessitating a higher depletion effect for highly motile bacteria to undergo phase separation within a growing biofilm. We present a phase diagram illustrating the systematic variation of motility force and repulsive mechanical force, shedding light on the combined contributions of these two factors: self-propulsive motion and aggregation due to the depletion effect, resulting in the presence of small to large bacterial aggregates. Furthermore, our study reveals the dynamic nature of clustering, marked by changes in cluster size over time. Additionally, our findings suggest that differential dispersion among the components can lead to the localization of EPS at the periphery of a growing colony. Our study enhances the understanding of the collective dynamics of motile bacterial cells within a growing colony, particularly in the presence of a self-secreted polymer-driven depletion effect.