Immotile Active Matter: Activity from Death and Reproduction

Machine learning identification of Pseudomonas aeruginosa strains from colony image data

When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shapes, edges, textures, and degrees of opacity and color. For over one hundred years, researchers have used these morphology cues to classify bacteria and guide more targeted treatment of pathogens. Advances in genome sequencing technology have revolutionized our ability to classify bacterial isolates and while genomic methods are in the ascendancy, morphological characterization of bacterial species has made a resurgence due to increased computing capacities and widespread application of machine learning tools. In this paper, we revisit the topic of colony morphotype on the within-species scale and apply concepts from image processing, computer vision, and deep learning to a dataset of 69 environmental and clinical Pseudomonas aeruginosa strains. We find that colony morphology and complexity under common laboratory conditions is a robust, repeatable phenotype on the level of individual strains, and therefore forms a potential basis for strain classification. We then use a deep convolutional neural network approach with a combination of data augmentation and transfer learning to overcome the typical data starvation problem in biological applications of deep learning. Using a train/validation/test split, our results achieve an average validation accuracy of 92.9% and an average test accuracy of 90.7% for the classification of individual strains. These results indicate that bacterial strains have characteristic visual 'fingerprints' that can serve as the basis of classification on a sub-species level. Our work illustrates the potential of image-based classification of bacterial pathogens and highlights the potential to use similar approaches to predict medically relevant strain characteristics like antibiotic resistance and virulence from colony data.

Mechanobiology as a tool for addressing the genotype-to-phenotype problem in microbiology

The central hypothesis of the genotype-phenotype relationship is that the phenotype of a developing organism (i.e., its set of observable attributes) depends on its genome and the environment. However, as we learn more about the genetics and biochemistry of living systems, our understanding does not fully extend to the complex multiscale nature of how cells move, interact, and organize; this gap in understanding is referred to as the genotype-to-phenotype problem. The physics of soft matter sets the background on which living organisms evolved, and the cell environment is a strong determinant of cell phenotype. This inevitably leads to challenges as the full function of many genes, and the diversity of cellular behaviors cannot be assessed without wide screens of environmental conditions. Cellular mechanobiology is an emerging field that provides methodologies to understand how cells integrate chemical and physical environmental stress and signals, and how they are transduced to control cell function. Biofilm forming bacteria represent an attractive model because they are fast growing, genetically malleable and can display sophisticated self-organizing developmental behaviors similar to those found in higher organisms. Here, we propose mechanobiology as a new area of study in prokaryotic systems and describe its potential for unveiling new links between an organism's genome and phenome.

Varied solutions to multicellularity: The biophysical and evolutionary consequences of diverse intercellular bonds

The diversity of multicellular organisms is, in large part, due to the fact that multicellularity has independently evolved many times. Nonetheless, multicellular organisms all share a universal biophysical trait: cells are attached to each other. All mechanisms of cellular attachment belong to one of two broad classes; intercellular bonds are either reformable or they are not. Both classes of multicellular assembly are common in nature, having independently evolved dozens of times. In this review, we detail these varied mechanisms as they exist in multicellular organisms. We also discuss the evolutionary implications of different intercellular attachment mechanisms on nascent multicellular organisms. The type of intercellular bond present during early steps in the transition to multicellularity constrains future evolutionary and biophysical dynamics for the lineage, affecting the origin of multicellular life cycles, cell-cell communication, cellular differentiation, and multicellular morphogenesis. The types of intercellular bonds used by multicellular organisms may thus result in some of the most impactful historical constraints on the evolution of multicellularity.

Cellular organization in lab-evolved and extant multicellular species obeys a maximum entropy law

The prevalence of multicellular organisms is due in part to their ability to form complex structures. How cells pack in these structures is a fundamental biophysical issue, underlying their functional properties. However, much remains unknown about how cell packing geometries arise, and how they are affected by random noise during growth - especially absent developmental programs. Here, we quantify the statistics of cellular neighborhoods of two different multicellular eukaryotes: lab-evolved 'snowflake' yeast and the green alga Volvox carteri. We find that despite large differences in cellular organization, the free space associated with individual cells in both organisms closely fits a modified gamma distribution, consistent with maximum entropy predictions originally developed for granular materials. This 'entropic' cellular packing ensures a degree of predictability despite noise, facilitating parent-offspring fidelity even in the absence of developmental regulation. Together with simulations of diverse growth morphologies, these results suggest that gamma-distributed cell neighborhood sizes are a general feature of multicellularity, arising from conserved statistics of cellular packing.

Accumulation of dead cells from contact killing facilitates coexistence in bacterial biofilms

Bacterial communities are governed by a wide variety of social interactions, some of which are antagonistic with potential significance for bacterial warfare. Several antagonistic mechanisms, such as killing via the type VI secretion system (T6SS), require killer cells to directly contact target cells. The T6SS is hypothesized to be a highly potent weapon, capable of facilitating the invasion and defence of bacterial populations. However, we find that the efficacy of contact killing is severely limited by the material consequences of cell death. Through experiments with Vibrio cholerae strains that kill via the T6SS, we show that dead cell debris quickly accumulates at the interface that forms between competing strains, preventing physical contact and thus preventing killing. While previous experiments have shown that T6SS killing can reduce a population of target cells by as much as 106-fold, we find that, as a result of the formation of dead cell debris barriers, the impact of contact killing depends sensitively on the initial concentration of killer cells. Killer cells are incapable of invading or eliminating competitors on a community level. Instead, bacterial warfare itself can facilitate coexistence between nominally antagonistic strains. While a variety of defensive strategies against microbial warfare exist, the material consequences of cell death provide target cells with their first line of defence.

Spatial propagation of electrical signals in circular biofilms: A combined experimental and agent-based fire-diffuse-fire study

Bacterial biofilms are a risk to human health, playing critical roles in persistent infections. Recent studies have observed electrical signaling in biofilms and thus biofilms represent a new class of active excitable matter in which cell division is the active process and the spiking of the individual bacterial cells is the excitable process. Electrophysiological models have predominantly been developed to describe eukaryotic systems, but we demonstrate their use in understanding bacterial biofilms. Our agent-based fire-diffuse-fire (ABFDF) model successfully simulates the propagation of both centrifugal (away from the center) and centripetal (toward the center) electrical signals through biofilms of Bacillus subtilis. Furthermore, the ABFDF model allows realistic spatial positioning of the bacteria in two dimensions to be included in the fire-diffuse-fire model and this is the crucial factor that improves agreement with experiments. The speed of propagation is not constant and depends on the radius of the propagating electrical wave front. Centripetal waves are observed to move faster than centrifugal waves, which is a curvature driven effect and is correctly captured by our simulations.

Complex microbial systems across different levels of description

Complex biological systems offer a variety of interesting phenomena at the different physical scales. With increasing abstraction, details of the microscopic scales can often be extrapolated to average or typical macroscopic properties. However, emergent properties and cross-scale interactions can impede naïve abstractions and necessitate comprehensive investigations of these complex systems. In this review paper, we focus on microbial communities, and first, summarize a general hierarchy of relevant scales and description levels to understand these complex systems: (1) genetic networks, (2) single cells, (3) populations, and (4) emergent multi-cellular properties. Second, we employ two illustrating examples, microbial competition and biofilm formation, to elucidate how cross-scale interactions and emergent properties enrich the observed multi-cellular behavior in these systems. Finally, we conclude with pointing out the necessity of multi-scale investigations to understand complex biological systems and discuss recent investigations.