Guim Aguadé-Gorgorió ∙ Alexander R.A. Anderson ∙ Ricard Solé

iScience Volume 27, Issue 9110699September 20, 2024

Many cancers resist therapeutic intervention. This is fundamentally related to intratumor heterogeneity: multiple cell populations, each with different phenotypic signatures, coexist within a tumor and its metastases. Like species in an ecosystem, cancer populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity or predict its consequences. Here, we propose that the generalized Lotka-Volterra model (GLV), a standard tool to describe species-rich ecological communities, provides a suitable framework to model the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties provide a new understanding of the disease. We discuss potential extensions of the model and their application to phenotypic plasticity, cancer-immune interactions, and metastatic growth. Our work outlines a set of questions and a road map for further research in cancer ecology.

Read the full article at: www.cell.com

Iacopo Iacopini, Márton Karsai & Alain Barrat
Nature Communications volume 15, Article number: 7391 (2024)

Representing social systems as networks, starting from the interactions between individuals, sheds light on the mechanisms governing their dynamics. However, networks encode only pairwise interactions, while most social interactions occur among groups of individuals, requiring higher-order network representations. Despite the recent interest in higher-order networks, little is known about the mechanisms that govern the formation and evolution of groups, and how people move between groups. Here, we leverage empirical data on social interactions among children and university students to study their temporal dynamics at both individual and group levels, characterising how individuals navigate groups and how groups form and disaggregate. We find robust patterns across contexts and propose a dynamical model that closely reproduces empirical observations. These results represent a further step in understanding social systems, and open up research directions to study the impact of group dynamics on dynamical processes that evolve on top of them. The structure and dynamics of many social systems where human interactions involve communities can be described by higher-order networks. The authors propose a hypergraph-based model that describes how individuals form groups and navigate between groups of different sizes.

Read the full article at: www.nature.com

Guilherme S. Y. Giardini, John F. Hardy II, Carlo R. da Cunha

Understanding the mechanisms behind emergent behaviors in multi-agent systems is critical for advancing fields such as swarm robotics and artificial intelligence. In this study, we investigate how neural networks evolve to control agents’ behavior in a dynamic environment, focusing on the relationship between the network’s complexity and collective behavior patterns. By performing quantitative and qualitative analyses, we demonstrate that the degree of network non-linearity correlates with the complexity of emergent behaviors. Simpler behaviors, such as lane formation and laminar flow, are characterized by more linear network operations, while complex behaviors like swarming and flocking show highly non-linear neural processing. Moreover, specific environmental parameters, such as moderate noise, broader field of view, and lower agent density, promote the evolution of non-linear networks that drive richer, more intricate collective behaviors. These results highlight the importance of tuning evolutionary conditions to induce desired behaviors in multi-agent systems, offering new pathways for optimizing coordination in autonomous swarms. Our findings contribute to a deeper understanding of how neural mechanisms influence collective dynamics, with implications for the design of intelligent, self-organizing systems.

Read the full article at: arxiv.org

Salva Duran-Nebreda, Blai Vidiella, Andrej Spiridonov, Niles Eldredge, Michael J. O’Brien, R. Alexander Bentley, Sergi Valverde

Palaeontology  Volume 67, Issue 5
September/October 2024
e12731

Punctuated equilibria is a theory of evolution that suggests that species go through periods of stability followed by sudden changes in phenotype. This theory has been debated for decades in evolutionary biology, but recent findings of stasis and punctuated change in evolutionary systems such as tumour dynamics, viral evolution, and artificial evolution have attracted attention from a broad range of researchers. There is a risk of interpreting punctuated change from a phenomenological, or even metaphorical, standpoint and thus opening the possibility of repeating similar debates that have occurred in the past. How to translate the lessons from evolutionary models of the fossil record to explain punctuated changes in other biological scales remains an open question. To minimize confusion, we recommend that the step-like pattern seen in many evolutionary systems be referred to as punctuated evolution rather than punctuated equilibria, which is the theory generally linked with the similar pattern in the fossil record. Punctuated evolution is a complex pattern resulting from the interaction of both external and internal eco-evolutionary feedback. The interplay between these evolutionary drivers can help explain the history of life and the whole spectrum of evolutionary dynamics, including diversification, cyclic changes, and stability.

Read the full article at: onlinelibrary.wiley.com