collective dynamics
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Making connections: how epithelial tissues guarantee folding
Tissue folding is a ubiquitous shape change event during development whereby a cell sheet bends into a curved 3D structure. This mechanical process is remarkably robust, and the correct final form is almost always achieved despite internal fluctuations and external perturbations inherent in living systems. While many genetic and molecular strategies that lead to robust development have been established, much less is known about how mechanical patterns and movements are ensured at the population level. I will describe how quantitative imaging, physical modeling and concepts from network science can uncover collective interactions that govern tissue patterning and shape change. Actin and myosin are two important cytoskeletal proteins involved in the force generation and movement of cells. Both parts of this talk will be about the spontaneous organization of actomyosin networks and their role in collective tissue dynamics. First, I will present how out-of-plane curvature can trigger the global alignment of actin fibers and a novel transition from collective to individual cell migration in culture. I will then describe how tissue-scale cytoskeletal patterns can guide tissue folding in the early fruit fly embryo. I will show that actin and myosin organize into a network that spans a domain of the embryo that will fold. Redundancy in this supracellular network encodes the tissue’s intrinsic robustness to mechanical and molecular perturbations during folding.
Do leader cells drive collective behavior in Dictyostelium Discoideum amoeba colonies?
Dictyostelium Discoideum (DD) are a fascinating single-cellular organism. When nutrients are plentiful, the DD cells act as autonomous individuals foraging their local vicinity. At the onset of starvation, a few (<0.1%) cells begin communicating with others by emitting a spike in the chemoattractant protein cyclic-AMP. Nearby cells sense the chemical gradient and respond by moving toward it and emitting a cyclic-AMP spike of their own. Cyclic-AMP activity increases over time, and eventually a spiral wave emerges, attracting hundreds of thousands of cells to an aggregation center. How DD cells go from autonomous individuals to a collective entity remains an open question for more than 60 years--a question whose answer would shed light on the emergence of multi-cellular life. Recently, trans-scale imaging has allowed the ability to sense the cyclic-AMP activity at both cell and colony levels. Using both the images as well as toy simulation models, this research aims to clarify whether the activity at the colony level is in fact initiated by a few cells, which may be deemed "leader" or "pacemaker" cells. In this talk, I will demonstrate the use of information-theoretic techniques to classify leaders and followers based on trajectory data, as well as to infer the domain of interaction of leader cells. We validate the techniques on toy models where leaders and followers are known, and then try to answer the question in real data--do leader cells drive collective behavior in DD colonies?
Internal structure of honey bee swarms for mechanical stability and division of labor
The western honey bee (Apis mellifera) is a domesticated pollinator famous for living in highly social colonies. In the spring, thousands of worker bees and a queen fly from their hive in search of a new home. They self-assemble into a swarm that hangs from a tree branch for several days. We reconstruct the non-isotropic arrangement of worker bees inside swarms made up of 3000 - 8000 bees using x-ray computed tomography. Some bees are stationary and hang from the attachment board or link their bodies into hanging chains to support the swarm structure. The remaining bees use the chains as pathways to walk around the swarm, potentially to feed the queen or communicate with one another. The top layers of bees bear more weight per bee than the remainder of the swarm, suggesting that bees are optimizing for additional factors besides weight distribution. Despite not having a clear leader, honey bees are able to organize into a swarm that protects the queen and remains stable until scout bees locate a new hive.
Neural network-like collective dynamics of molecules
Collective dynamics in nuclear structure emerges from chromatin crosslinks and motors
Neural network-like collective dynamics in molecules
Neural networks can learn and recognize subtle correlations in high dimensional inputs. However, neural networks are simply many-body systems with strong non-linearities and disordered interactions. Hence, many-body physical systems with similar interactions should be able to show neural network-like behavior. Here we show neural network-like behavior in the nucleation dynamics of promiscuously interacting molecules with multiple stable crystalline phases. Using a combination of theory and experiments, we show how the physics of the system dictates relationships between the difficulty of the pattern recognition task solved, time taken and accuracy. This work shows that high dimensional pattern recognition and learning are not special to software algorithms but can be achieved by the collective dynamics of sufficiently disordered molecular systems.
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