KAROL A. BACIK, BOGDAN S. BACIK, AND TIM ROGERS

SCIENCE Vol 379, Issue 6635 pp. 923-928

Laning is a paradigmatic example of spontaneous organization in active two-component flows that has been observed in diverse contexts, including pedestrian traffic, driven colloids, complex plasmas, and molecular transport. We introduce a kinetic theory that elucidates the physical origins of laning and quantifies the propensity for lane nucleation in a given physical system. Our theory is valid in the low-density regime, and it makes different predictions about situations in which lanes may form that are not parallel with the direction of flow. We report on experiments with human crowds that verify two notable consequences of this phenomenon: tilting lanes under broken chiral symmetry and lane nucleation along elliptic, parabolic, and hyperbolic curves in the presence of sources or sinks.

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Nicholas W. Landry, Ilya Amburg, Mirah Shi, Sinan G. Aksoy
Many complex systems often contain interactions between more than two nodes, known as higher-order interactions, which can change the structure of these systems in significant ways. Researchers often assume that all interactions paint a consistent picture of a higher-order dataset’s structure. In contrast, the connection patterns of individuals or entities in empirical systems are often stratified by interaction size. Ignoring this fact can aggregate connection patterns that exist only at certain scales of interaction. To isolate these scale-dependent patterns, we present an approach for analyzing higher-order datasets by filtering interactions by their size. We apply this framework to several empirical datasets from three domains to demonstrate that data practitioners can gain valuable information from this approach.

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Emanuela Del Dottore, Barbara Mazzolai

Artificial Life

Plants thrive in virtually all natural and human-adapted environments and are becoming popular models for developing robotics systems because of their strategies of morphological and behavioral adaptation. Such adaptation and high plasticity offer new approaches for designing, modeling, and controlling artificial systems acting in unstructured scenarios. At the same time, the development of artifacts based on their working principles reveals how plants promote innovative approaches for preservation and management plans and opens new applications for engineering-driven plant science. Environmentally mediated growth patterns (e.g., tropisms) are clear examples of adaptive behaviors displayed through morphological phenotyping. Plants also create networks with other plants through subterranean roots–fungi symbiosis and use these networks to exchange resources or warning signals. This article discusses the functional behaviors of plants and shows the close similarities with a perceptron-like model that could act as a behavior-based control model in plants. We begin by analyzing communication rules and growth behaviors of plants; we then show how we translated plant behaviors into algorithmic solutions for bioinspired robot controllers; and finally, we discuss how those solutions can be extended to embrace original approaches to networking and robotics control architectures.

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Philip Ball

Concerned by the speed of research, policymakers are looking for alternative ways to establish rules on genome editing and other technologies.

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G. Ozan Bozdag, Seyed Alireza Zamani-Dahaj, Thomas C. Day, Penelope C. Kahn, Anthony J. Burnetti, Dung T. Lac, Kai Tong, Peter L. Conlin, Aishwarya H. Balwani, Eva L. Dyer, Peter J. Yunker & William C. Ratcliff 

Nature (2023)

While early multicellular lineages necessarily started out as relatively simple groups of cells, little is known about how they became Darwinian entities capable of sustained multicellular evolution1,2,3. Here we investigate this with a multicellularity long-term evolution experiment, selecting for larger group size in the snowflake yeast (Saccharomyces cerevisiae) model system. Given the historical importance of oxygen limitation4, our ongoing experiment consists of three metabolic treatments5—anaerobic, obligately aerobic and mixotrophic yeast. After 600 rounds of selection, snowflake yeast in the anaerobic treatment group evolved to be macroscopic, becoming around 2 × 104 times larger (approximately mm scale) and about 104-fold more biophysically tough, while retaining a clonal multicellular life cycle. This occurred through biophysical adaptation—evolution of increasingly elongate cells that initially reduced the strain of cellular packing and then facilitated branch entanglements that enabled groups of cells to stay together even after many cellular bonds fracture. By contrast, snowflake yeast competing for low oxygen5 remained microscopic, evolving to be only around sixfold larger, underscoring the critical role of oxygen levels in the evolution of multicellular size. Together, this research provides unique insights into an ongoing evolutionary transition in individuality, showing how simple groups of cells overcome fundamental biophysical limitations through gradual, yet sustained, multicellular evolution.

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