Amit Kahana, Alasdair MacLeod, Hessam Mehr, Abhishek Sharma, Emma Carrick, Michael Jirasek, Sara Walker, Leroy Cronin

Here we demonstrate the first biochemistry-agnostic approach to map evolutionary relationships at the molecular scale, allowing the construction of phylogenetic models using mass spectrometry (MS) and Assembly Theory (AT) without elucidating molecular identities. AT allows us to estimate the complexity of molecules by deducing the amount of shared information stored within them when . By examining 74 samples from a diverse range of biotic and abiotic sources, we used tandem MS data to detect 24102 analytes (9262 unique) and 59518 molecular fragments (6755 unique). Using this MS dataset, together with AT, we were able to infer the joint assembly spaces (JAS) of samples from molecular analytes. We show how JAS allows agnostic annotation of samples without fingerprinting exact analyte identities, facilitating accurate determination of their biogenicity and taxonomical grouping. Furthermore, we developed an AT-based framework to construct a biochemistry-agnostic phylogenetic tree which is consistent with genome-based models and outperforms other similarity-based algorithms. Finally, we were able to use AT to track colony lineages of a single bacterial species based on phenotypic variation in their molecular composition with high accuracy, which would be challenging to track with genomic data. Our results demonstrate how AT can expand causal molecular inference to non-sequence information without requiring exact molecular identities, thereby opening the possibility to study previously inaccessible biological domains.

Read the full article at: arxiv.org

Boldizsár Balázs, Tamás Vicsek, Gergő Somorjai, Tamás Nepusz & Gábor Vásárhelyi

Swarm Intelligence

Coordination of local and global aerial traffic has become a legal and technological bottleneck as the number of unmanned vehicles in the common airspace continues to grow. To meet this challenge, automation and decentralization of control is an unavoidable requirement. In this paper, we present a solution that enables self-organization of cooperating autonomous agents into an effective traffic flow state in which the common aerial coordination task—filled with conflicts—is resolved. Using realistic simulations, we show that our algorithm is safe, efficient, and scalable regarding the number of drones and their speed range, while it can also handle heterogeneous agents and even pairwise priorities between them. The algorithm works in any sparse or dense traffic scenario in two dimensions and can be made increasingly efficient by a layered flight space structure in three dimensions. To support the feasibility of our solution, we show stable traffic simulations with up to 5000 agents, and experimentally demonstrate coordinated aerial traffic of 100 autonomous drones within a 250 m wide circular area.

Read the full article at: link.springer.com

Stefani A Crabtree, Colin D Wren, Avinash Dixit, Simon A Levin
PNAS Nexus, Volume 3, Issue 7, July 2024, pgae224,

In this paper, we examine how different governance types impact prosocial behaviors in a heterogenous society. We construct a general theoretical framework to examine a game-theoretic model to assess the ease of achieving a cooperative outcome. We then build a dynamic agent-based model to examine three distinct governance types in a heterogenous population: monitoring one’s neighbors, despotic leadership, and influencing one’s neighbors to adapt strategies that lead to better fitness. In our research, we find that while despotic leadership may lead towards high prosociality and high returns it does not exceed the effects of a local individual who can exert positive influence in the community. This may suggest that greater individual gains can be had by cooperating and that global hierarchical leadership may not be essential as long as influential individuals exert their influence for public good and not for public ill.

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Will Deter, Hiroki Sayama

Agent-based models of residential segregation have been of persistent interest to various research communities since their origin with James Sakoda and popularization by Thomas Schelling. Frequently, these models have sought to elucidate the extent to which the collective dynamics of individual preferences may cause segregation to emerge. This open question has sustained relevance in U.S. jurisprudence. Previous investigation of heterogeneity of behaviors (preferences) by Xie & Zhou has shown reductions in segregation. Meanwhile, previous investigation of heterogeneity of social network topologies by Gandica, Gargiulo, and Carletti has shown no significant impact to observed segregation levels. In the present study, we examined effects of the concurrent presence of both behavioral and topological heterogeneities in network segregation models. Simulations were conducted using both Schelling’s and Xie & Zhou’s preference models on 2D lattices with varied levels of densification to create topological heterogeneities (i.e., clusters, hubs). Results show a richer variety of outcomes, including novel differences in resultant segregation levels and hub composition. Notably, with concurrent increased representations of heterogeneous preferences and heterogenous topologies, reduced levels of segregation emerge. Simultaneously, we observe a novel dynamic of segregation between tolerance levels as highly tolerant nodes take residence in dense areas and push intolerant nodes to sparse areas mimicking the urban-rural divide.

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Michael Levin

The dominant paradigm in biomedicine focuses on the genetically-specified components of cells, and their biochemical dynamics. This perspective emphasizes bottom-up emergence of complexity, which constrains interventional approaches to micromanaging the living hardware. Here, I explore the implications for the applied life sciences of a complementary emerging field: diverse intelligence, which studies the capacity of a wide range of systems to reach specific goals in various problem spaces. Using tools from behavioral science and multiscale neuroscience, it is possible to address development, regenerative repair, and cancer as behaviors of a collective intelligence of cells as it navigates the space of possible morphologies and transcriptional and physiological states. This view emphasizes the competencies of living material – from the molecular to the organismal scales – that can be targeted by interventions. Top-down approaches take advantage of memories and homeodynamic goal-seeking behavior, offering the same massive advantages in biomedicine and bioengineering as the emphasis on reprogrammable hardware has had for information technologies. The bioelectric networks that bind individual cells toward large-scale anatomical goals are an especially tractable interface to organ-level plasticity. This suggests a research program to understand and tame the software of life by understanding the many examples of basal cognition that operate throughout living bodies. Tools are now in place to unify the organicist and mechanist perspectives on living systems toward a much-improved therapeutic landscape.

Read the full article at: osf.io