Bulletin of the American Physical Society
APS March Meeting 2019
Volume 64, Number 2
Monday–Friday, March 4–8, 2019; Boston, Massachusetts
Session S63: Statistical Physics of Large Populations of Cells: from Microbes to Tissues IIFocus

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Sponsoring Units: DBIO GSNP Chair: Sidhartha Goyal, University of Toronto Room: BCEC 259A 
Thursday, March 7, 2019 11:15AM  11:51AM 
S63.00001: Modeling the SolidFluid Transition in Ordered Biological Tissues Invited Speaker: Preeti Sahu Biological functionality of tissues relies on its rheology. Fluidity of a 2D nonproliferating confluent tissue is contingent on cellular rearrangements which are called T1 transitions. In a 2D vertex model for disordered tissues, the tissue fluidizes when the T1 energy barriers disappear as the target shape index approaches a critical value (~3.81) [Bi 2015]. The linear response also becomes fluidlike (i.e. the shear modulus vanishes) at this same value. However, shear modulus of ordered ground states of 2D vertex models vanishes at a lower value (3.72) [Farhadifar 2007, Staple 2010]. Therefore, an interesting open question is whether the ground states of the 2D vertex model are fluidlike or solidlike between 3.72 and 3.81. In other words, does the “equation of state” for these systems have two branches (like glassy particulate matter) or only one? Using fourcell and manycell numerical simulations, we demonstrate that for a hexagonal ground state, T1 energy barriers vanish only at ~3.81, indicating that ordered systems have the same critical point as disordered systems. We also develop a simple geometric argument that predicts the correct scaling of energy barriers with T1 edge length in these systems. 
Thursday, March 7, 2019 11:51AM  12:03PM 
S63.00002: Cell size regulation induces sustained oscillations in the population growth rate Farshid Jafarpour There are negative correlations between the generation time of a biological cell and those of its descendants. If a cell grows for a longer time than expected, its daughter cells will be larger at birth and have to compensate for their sizes by dividing slightly earlier than expected. Otherwise, the noise in the generation times would accumulate over generations in the size of the cells, leading to extremely large cells. This process is known as cell size control. In this talk, I discuss the effect of these correlations on the dynamics of population growth of microorganisms. I show that any nonzero correlation that is due to cellsize control can induce longterm oscillations in the population growth rate. The population only reaches its steady state due to the oftenneglected variability in the growth rates of individual cells. The relaxation time scale of the population to its steady state is determined from the distribution of singlecell growth rates independent of the details of the division process or the cellsize regulation. I propose an experimental method to measure singlecell growth variability by observing how long it takes for the population to reach its steady state, a measurement that is significantly easier and less biased than singlecell measurements. 
Thursday, March 7, 2019 12:03PM  12:15PM 
S63.00003: Celllevel mechanical heterogeneity promotes rigidity in confluent tissues Xinzhi Li, Dapeng(Max) Bi Intratumor heterogeneity is one of the hallmarks of cancer, which describes the phenotypic differences among cells in a tumor or cellular collective. While genetic heterogeneity has been an intense focus of study, how mechanical variations among cells influence tissue mechanics is not well understood. Here, we investigate the effect of celltocell mechanical heterogeneity on the overall bulk mechanics of a confluent 2d tissue using a vertex modelbased approach. We find that the rigidity of a confluent tissue depends on overall statistical properties of singlecell properties such as mean and variance, rather than the specific functional form of its distribution. A single universal parameter  the fraction of mechanically rigid cells, f_{r}, can be used to characterize the tissue mechanical state. As f_{r} is tuned, the tissue undergoes a rigidity percolation at a critical threshold of f_{r}. Remarkably, this rigidity percolation occurs at a much lower value than what is required for rigidcell to form a spanning cluster. A mean field model is proposed to explain the discrepancy between rigidity and contact percolations. 
Thursday, March 7, 2019 12:15PM  12:27PM 
S63.00004: How diverse forms of phenotypic variability affect microbial growth in changing environments Ethan Levien, Ariel Amir, Jane Kondev Numerous organisms utilize phenotypic variability to hedge their bets against unpredictable environmental changes. In microbiology, the most wellstudied example of this is bacterial persistence — the phenomenon by which some fraction of the population grows relatively slowly in exchange for decreased susceptibility to antibiotics. Experimental evidence suggests a distribution of many phenotypes, not just two growth states, plays a role in bacterial growth, yet the quantitive consequences of more general forms of phenotypic variability are not well understood. Here we model various forms of phenotypic variability in changing environments, including deterministic variability arising from asymmetric segregation at cell division, to stochastic variability resulting from noisy gene expression. In our model, singlecell growth rates are functions of the phenotype and the environmental state. We derive conditions on these functions that guarantee phenotypic variability will be beneficial to a population. 
Thursday, March 7, 2019 12:27PM  12:39PM 
S63.00005: Grow or Go? Studying fitness of cell populations using a mathematical model Noah Reuter, Moumita Das In nature, no organism lives in an isolated environment. Competition between different organisms is a fact of life, and we are familiar with the famous phrase “Survival of the fittest’’ as the principle behind natural selection. But what factors decide whether one organism is more fit than another? Similar questions can be asked about cell populations. For a long time ``evolutionary fitness’’ was thought to be simply determined by the rate at which an organism (or cell) reproduced. Recent studies have, however, questioned this view, and have suggested fast migration and invasion as a competing mechanism to fast reproduction. It has been observed that many types of cells and organisms favor either growth and proliferation, or rapid and distant migration. This is referred to as the grow or go hypothesis. We test this hypothesis using a computer simulation of populations of two types of cells, modeled as active, interacting particles. The two cell types have different selfpropulsion speeds, and different rates of proliferation and death. The simulation assumes that the faster cells divide at slower rates and have shorter life spans. We investigate the migration and phase separation in this system and look to see which population is more successful in reaching the periphery. 
Thursday, March 7, 2019 12:39PM  12:51PM 
S63.00006: Statistics of single cell trajectories in a bacterial swarm N. S. Karthik Somayaji, Harshitha Shankar Kotian, Amith Z. Abdulla, Shalini Harkar, Varsha Singh, Manoj M. Varma In our work, we analyse the microscopic characteristics of the motion of individual bacterial cells in an advancing bacterial swarm consisting of over 10 million individuals. Statistical analysis of single cell trajectories in the swarm reveals a correlated random walk with a mean growth direction. The trajectory length of the random walk was found to obey a lognormal distribution. The distribution of turning angles revealed an interesting situation with a Gaussian hump centred around zero over a uniform background. The peak in the distribution of turning angles could be reasonably fitted to a truncated normal distribution. The background (angles from [180,100] and its mirror [100, 180]) displayed a uniform distribution. Such a distribution can be thought of as arising from a weakly biased random walk where the bias provides the global mean direction of the swarm while the overall uniform background arises from the Brownian (unbiased) portion. The bias results in finite correlation of the current direction with the previous direction resulting in the Gaussian hump centred around zero. The observed statistics of motion at the microscopic level can thus predict the mean directed swarm motion. 
Thursday, March 7, 2019 12:51PM  1:03PM 
S63.00007: Big data of big tissues:
deep neural networks to accelerate analysis of collective cell behaviors in large populations Julienne LaChance, Daniel Cohen Coordinated cellular motion is crucial for proper tissue organization and function. Biophysical statistics of group behaviors can provide key insights into these behaviors, such as detecting pathological changes. Applying statistical analyses to very large tissues (>50,000 cells) offers exciting potential but requires more versatile feature extraction approaches. Convolutional neural networks are increasingly promising for tasks such as object classification and segmentation (e.g. cells, phenotypes). In this study, we apply a UNet style architecture for labelfree nuclei detection using lowmagnification, transmitted light imaging. We utilize UVexcited nuclear labels to achieve automatic annotation of the data. The benefits of labelfree image segmentation with transmitted light microscopy are numerous: improved accessibility by allowing for feature extraction without fluorescence imaging; eliminating the phototoxicity that results from typical UVexcited nuclear labels; and unambiguous, rapid postprocessing of massive datasets. Here, we assess the accuracy of the reconstructed nuclei, and present preliminary data using this tool to explore cellular distribution and migration statistics (e.g. order, neighbor arrangements, correlations) in large, complex tissue geometries. 
Thursday, March 7, 2019 1:03PM  1:15PM 
S63.00008: Effects of curvature and topology on collective cell migration in dense biological tissues Margherita De Marzio, Jeffrey Fredberg, Dapeng Bi Confluent epithelia line every organ surface and body cavity. The epithelial tissue typically remains quiescent and nonmigratory while performing its routine barrier and immune functions but becomes dynamic and migratory during morphogenesis, repair, invasion and metastasis [1]. To model these processes, recent progress has been made using agentbased simulations [2] of multicellular behavior grown in cultures on flat space [3,4]. However, native epithelia typically comprise curved surfaces, such as embryos, and respiratory bronchioles, airways, intestines and embryos. On such curved geometry outofplane mechanical forces can influence cytoskeletal organization, cellcell interaction and thus could influence migration and development. Here, we develop an agentbased cellular model to simulate the collective behavior on curved surfaces, such as spheres, ellipses and tubular structures. We explore how the curvature alters the nature of unjamming and glass transitions in tissues and how topological defects introduce new motion patterns. 
Thursday, March 7, 2019 1:15PM  1:27PM 
S63.00009: Failure Propagation in Cooperating Multicellular Systems Dervis Can Vural, Pinar Zorlutuna From microbial communities to eukaryotic tissues, biological function often hinges on the exchange of diffusing cooperative factors and public goods. When stressors or statistical fluctuations compromise the population locally, this leads to an interruption of these exchanges near by and creates a cascade of failures. Here we present analytical results on how failure propagates through a multicellular system right before its catastrophic end. We find that the failure of the system progresses on two fronts; through the propagation of a wave from the surface inwards, and simultaneously, a decay of the bulk that may or may not finalize with a sudden collapse. We obtain formulas governing the population dynamics and failure propagation velocity, and fit some of these findings to experimental measurements. 
Thursday, March 7, 2019 1:27PM  1:39PM 
S63.00010: Powerlaw distributions of Tcell clone abundances in a nonneutral birthdeathimmigration model Renaud Dessalles, Maria R D'Orsogna, Thomas Chou Tcells can then die or proliferate to produce new Tcells carrying the same receptor. This process can be described by a stochastic multitype birthdeathimmigration (BDI) process. However, predictions of a simple neutral BDI process, where cells of all receptor types have the same immigration, birth, and death rates, do not reproduce the experimentally measured powerlaw clone size distributions. However, it is known that Tcell proliferation depends on its specific affinity to selfligands and Tcells of certain receptors are more likely to be produced in the thymus. Here, we study a nonneutral BDI model, in which each clone has a specific immigration rate and a specific peripheral proliferation rate arising from different ligand affinities. Realistic distributions of immigration rates are generated from measured DNA, while hypothetical distributions of proliferations rates are tested. We also include a carrying capacity through the death rate to model the competition of lymphocytes for cytokines and to ensure homeostasis of the system. The effects of sampling of Tcells from an organism are also calcuted. We show that a nonneutral model with sampling can describe the experimentally observed clone size distributions provided the appropriate Tcell heterogeneity is employed. 
Thursday, March 7, 2019 1:39PM  1:51PM 
S63.00011: Extreme value analysis of gut microbial alterations in colorectal cancer Stephanie Song, Patricio Jeraldo, Jun Chen, Nicholas Chia Gut microbes play a key role in colorectal carcinogenesis, yet reaching a consensus on which microbes remains challenging in part due to reliance on mean value estimates. We present an extreme value analysis for overcoming these limitations. By characterizing a power law fit to the relative abundances of microbes, we capture the same microbial signatures as more complex metaanalyses. Importantly, we show that our method is robust to the variations inherent in microbial community profiling and point to future directions for developing sensitive, robust analytical methods. 
Thursday, March 7, 2019 1:51PM  2:03PM 
S63.00012: Characterization of Swarmer Cell Differentiation in Proteus mirabilis Emrah Simsek, Minsu Kim Some bacteria translocate across quasisolid surfaces via a multicellular type of motility called swarming. Swarming is performed by highly elongated and hyperflagellated differentiated swarmer cells. It is known that swarmer cell differentiation occurs upon surface contact. However, cellular and environmental factors affecting swarmer cell differentiation remain poorly understood. Here, we studied swarmer cell differentiation in Proteus mirabilis. We found that swarmer differentiation occurs abruptly at a critical cell density. In order to understand this tight regulation, we have analyzed the gene regulatory network controlling the expression of the master regulator flhDC. Interestingly, our singlecelllevel experiments show that both initiation of and commitment to differentiation are stochastic. Our efforts to mechanistically bridge this singlecelllevel stochasticity in differentiation and its populationlevel tight regulation will also be discussed. 
Thursday, March 7, 2019 2:03PM  2:15PM 
S63.00013: Optimal segregation of proteins: phase transitions and symmetry breaking Jie Lin, Jiseon Min, Ariel Amir In stressed environments, microbial cells such as bacteria or yeast utilize various mechanisms to survive. One important mechanism is the asymmetric segregation of key proteins at cell division, placing one of the two daughter cells in a more favorable condition. We provide a general framework to describe the evolutionary origin of this asymmetric segregation. We compute the population fitness as a function of the protein segregation asymmetry a and show that the value of a which optimizes the population growth manifests a phase transition between symmetric and asymmetric partitioning phases. Surprisingly, the nature of phase transition is different for the case of beneficial proteins as opposed to deleterious proteins: a smooth (second order) transition from purely symmetric to asymmetric segregation is found in the former, while a sharp transition occurs in the latter. Our study elucidates the optimization problem faced by evolution in the context of protein segregation and motivates further investigation of asymmetric protein segregation in biological systems. 
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