Session K36: Collective Behaviors in Biology III

Learning the emergent order of living matter from bacteria

Self-organization is a hallmark of living matter ranging from sub-cellular constituents to multicellular organisms. In recent decades bacteria have served as the premier model system for studying the self-organization of living and active matter. In particular, the behavior and interaction of bacteria can be manipulated by a variety of physical, chemical and biological means, allowing for the exploration of self-organized behavior in a broad parameter space and for timely examination of theories. In this talk we will introduce several physical mechanisms that give rise to spatiotemporal order in diverse bacterial populations, including bacterial colonies, biofilms, and generic bacterial suspensions. The potential biological functions of these forms and mechanisms of ordering in bacterial communities and in other living systems will be discussed. These mechanisms may fuel the development of non-equilibrium physics and provide new strategies to engineer living materials.   

A Multi-layer Neural Network Model to predict the Quorum Sensing behavior of a heterogeneous community of bacteria.

Bacteria communicate through the exchange of molecular signals in a process known as quorum sensing. This process is well understood for populations containing only one species of bacteria. The release of a specific signaling molecule leads to a high concentration of the signal. At a high concentration of signal, the bacteria activate the expression of multiple quorum sensing-regulated genes. In many natural contexts, many species coexist, and some of these species may produce different chemical variants of a particular signal molecule. This leads to crosstalk between species, as signals produced by one species may promote or inhibit the activation of quorum sensing in another species. There are even examples of a species participating in two orthogonal signaling pathways, although often both signaling pathways activate similar sets of genes. Here bacterial communication given the added complexity of multiple species exchanging multiple types of signals is modeled as a multilayer neural network. The network has one or two layers, depending on the number of orthogonal layers of signal exchange utilized within the bacterial community. Nodes in the network represent cells belonging to a particular species with a weighted directed edge representing signal crosstalk. We investigate activity patterns of the nodes at steady state, representing decision states of the network. The robustness of these decision states is probed via perturbations of signal concentrations and the population size. Nodes that are more influential in setting the community decision state are identified. For a network with two layers of signal, the correlation of activity states in each layer is explored and information exchange between the two layers of the network is quantified. The presence of crosstalk and multiple layers of orthogonal has consequences in the overall percentage of active nodes as well as the stability of these networks.   

Interface-mediated three-dimensional microbe expansion

Microbe expansion such as bacterial swarming is commonly found at the interface. Usually, the expansion occurs in a quasi-two-dimensional scenario. However, it is less known that how the microbes expand into the third dimension.  Here, we study a recently discovered mode of vertical growth of bacterial colonies termed as “active interface bulging” (Ref: Liu, Siyu, et al. "Active bulging promotes biofilm formation in a bacterial swarm." bioRxiv (2022): 2022-08.). We characterized the growth and interaction dynamics of this unique process by high resolution imaging.  Our findings shed light on microbe expansion and biofilm formation.   

Migration of living droplets: a novel paradigm for chemotaxis of multicellular communities

Collective cell migration is ubiquitous amongst multicellular communities and contributes to many phenomena, e.g., morphogenesis and cancer metastasis. Nonetheless, it is still poorly understood how cells coordinate to control the emergent collective motion of cell groups (or swarms). Recent experimental data suggests that physical interactions between cells within the swarms can result in emergent fluid-like properties. In this work, we propose a continuum, coarse-grained, active fluid model to study how physical interactions affect the complex spatiotemporal dynamics of cell swarms' collective chemotaxis in response to self-generated chemical gradients. Our results reveal that the interplay between physical interactions, cell proliferation and chemotaxis can lead to a new mode of pattern formation via self-organised shedding: as the swarms move collectively, they can periodically shed groups of cells at the rear. As such, our work offers a new perspective to the study of chemotaxis of multicellular communities revealing the role of physical interactions in mediating their collective dynamics.   

Effects of cell density on swarming SM3 bacteria

Swarming behavior is a common trait observed in many species of flagellated bacteria. It occurs when a growing population of cells expands on an agar surface, resulting in collective motion. We investigate the swarming behavior of Enterobacter sp. SM3, a gut bacterium that has recently been identified to provide a physiological benefit to lab mice suffering intestinal inflammation. We found that the collective dynamics of SM3 are highly sensitive to cell density. This sensitivity underscores how the interaction between individual cells are a key determinant of these dynamic patterns of motion in bacterial swarms. Thus, accurate measurements of cell density, cell positions and orientations, as well as observing flagella within the swarm are all essential to understand the mechanisms governing the observed collective behavior.   

Categorizing spatiotemporal dynamics of bacterial swarms

Low-dimensional effective models have proved to be an essential tool for analyzing extensive high-dimensional complex biophysical data, enabling computationally efficient characterizations of the dynamics of living systems. Recent advances in automated experimental imaging allow simultaneous measurements of spatiotemporal transcriptomes and spatiotemporal phenotypes during the collective motion of Bacillus subtilis' swarms. Here, we use a spectral dimensionality reduction framework to quantitatively characterize the spatiotemporal patterns that emerge and reveal correlations between cellular and collective properties. Using single-gene knockouts, whose morphology exhibits a rich breadth of macroscopic swarming phenomenology, we further quantify the impact of genotype on swarming behavior, by reducing the complex dynamics of the multicellular system to the time evolution of closed curves by representing the swarms by their moving boundary. The curves provide a three-dimensional spacetime surface representation of each mutant's phenomenology. We model these spacetime surfaces using a simple geometric model, utilizing modern inference techniques for dynamical systems to infer model parameters and cluster the spatiotemporal phenotype of the swarm shape dynamics under varying genotypes.   

Pattern formation by bacteria-phage interactions

The interactions between bacteria and phages---viruses that infect bacteria---play critical roles in agriculture, ecology, and medicine; however, how these interactions influence the spatial organization of both bacteria and phages remain largely unexplored. Here, we address this gap in knowledge by developing a theoretical model of motile, proliferating bacteria that aggregate via motility-induced phase separation (MIPS) and encounter phage that infect and lyse the cells. We find that the non-reciprocal predator-prey interactions between phage and bacteria strongly alter spatial organization, in some cases giving rise to a rich array of finite-scale stationary and dynamic patterns in which bacteria and phage coexist. We establish principles describing the onset and characteristics of these diverse behaviors, thereby helping to provide a biophysical basis for understanding pattern formation in bacteria-phage systems, as well as in a broader range of active and living systems with similar predator-prey or other non-reciprocal interactions.   

Cell scale mechanics of super competition in epithelial tissue

Cell competition is the phenomenon by which cells attempt to eliminate less fit cells through various mechanisms of fitness comparison in order to maintain homeostasis. These mechanisms have been used to describe how a tissue sustains healthy function through embryogenesis, morphogenesis, and aging. Cell competition has also been implicated as a primary mechanism by which some diseases, most notably cancer, function by furthering the proliferation of diseased cells at the expense of the organism's overall fitness. Here we develop a Cellular Potts Model framework to predict the patterns of cell proliferation and elimination within a tissue comprised of normal wild type cells and mutations. We connect single-cell mechanics and cell-cell interactions to tissue-level survivability in the context of competition. Our model incorporates probabilistic rules governing cell growth, division, and elimination, while also taking into account their feedback with tissue mechanics. With these rules and model parameters, we predict how tissue mechanics influence fitness and proliferation dynamics, and how single-cell physical properties influence the spatiotemporal patterns of tumor growth.   

Collective Motion in Vascular Endothelium

Collective cellular motion is a phenomenon wherein cells coordinate their movements via intra- and inter-cellular interactions. While collective motion is common in nature, occurring in systems such as insect colonies, birds, fishes, etc, the presence of cell-cell adhesion and proliferation sets collective cell behavior apart. Consequently, researchers have paid significant attention to this field, conducting in vitro studies and developing in silico models to better understand this multicellular phenomenon. Of these models, Vertex and SPV (Self-Propelled Voronoi) models have been particularly successful as they account for several physical aspects like contraction, adhesion, and changes in cell geometries and topology in confluent monolayers. In addition to cellular processes, the external physical constraints on cells also influence their motion. For example, migrating sheets of vascular endothelial cells in vivo experience directional cues from underlying extracellular matrices. Related in vitro investigations have shown that unconfined endothelial cell layers exhibit a unique form of collective motion, with cells moving in antiparallel streams when cultured on anisotropic micro-grooved substrates. In this presentation, we examine this emergent behavior through a SPV model which not only reproduces experimental findings but also provides deeper insights into how this form of collective motion manifests within cells under different physical and physiological conditions.   

Intercellular Friction and Motility Drive Orientational Order in a Model Cell Monolayer

Cells collectively migrate and form spatiotemporal patterns in many biological processes, including embryonic development, wound healing, and cancer metastasis. Here, we use a multiphase field model of deformable, motile cells to study numerically the role of intercellular friction in tissue dynamics. This endows the tissue with a finite viscosity, which enhances velocity correlations. Varying friction and motility drives a solid-liquid transition, and near the transition boundary we find the emergence of nematic order of cell deformations driven by aligning cellular flows. We also show that hexatic and nematic order are tightly coupled and propose a mechanical-geometric model for the colocalization of +1/2 nematic defects and 5-7 disclination pairs -- the structural defects in the hexatic phase. Furthermore, +1/2 nematic defects coincide with regions of high cell-cell overlap, suggesting that they may mediate cellular extrusion, which has been found experimentally. Our results suggest that intercellular friction may be a physical explanation for the observation of nematic and hexatic order in experiments and simulations of epithelial monolayers.   

Surfing a Signaling Wave for Whole-body Regeneration

Injury induces systemic responses, but their functions remain elusive. Mechanisms that can rapidly synchronize wound responses through long distances are also mostly unknown. Using planarian flatworms capable of whole-body regeneration, we report that injury induces Erk activity wave to travel at a speed 10-100 times faster than those in other multicellular tissues. This ultrafast propagation requires longitudinal body-wall muscles, elongated cells forming dense parallel tracks running the length of the organism. The morphological properties of muscles allow them to act as superhighways for propagating and disseminating wound signals. Inhibiting Erk propagation prevents tissues distant to the wound from responding and blocks regeneration, which can be rescued by a second injury to distal tissues shortly after the first injury. Our findings provide a mechanism for long-range signal propagation in large complex tissues to coordinate responses across cell types and highlight the function of feedback between spatially separated tissues during whole-body regeneration.   

Engineering emergent morphogenetic properties of cell clusters with differentiable programming

Living systems have a remarkable ability to self-organize into increasingly complex structures. Due to the sheer complexity of coordinating this over large numbers of cells via local inputs, it has been challenging to learn how individual cellular decisions can orchestrate emergent behavior. In this work, we use differentiable programming to optimize over physical models of cells, learning local decision functions to drive collective behavior. In our model, cells can interact through morphogen diffusion, adhesive interactions, and mechanical stress and make internal decisions based on local inputs. These cellular decisions, such as whether to divide or not, are parametrized by neural networks and learned via gradient-based optimization. We apply this framework to learn how to program clusters of cells to (i) grow homogeneously in the presence of a growth factor, (ii) grow into elongated and v-shaped structures, and (iii) maintain chemical homeostasis between cell types. The learned decision functions exhibit characteristics not specifically trained for and are robust to perturbations. Our work opens new avenues for learning how to engineer synthetic systems such as organoids, as well as for inferring mechanisms from experimental observations.