arXiv subjects

Daniel Gradeci

Publications and source records attributed to Daniel Gradeci.

2 recordsLinked to original sources

arXiv2026

The life and death of football team runs: survival of collective modes shapes Lévy-like transport

Broad run-length distributions and short-lag superdiffusion have recently been reported in football players and team centroids, prompting a collective-foraging interpretation. The mechanism linking these player- and team-level signatures remains unclear. Using SoccerMon GPS data from 66 tracked team-match records across 62 fixtures in the Norwegian women's top-flight Toppserien, we asked whether player transport is inherited from a translating team mode and what controls the lifetime of that mode. We decomposed player displacement into centroid translation and motion within the formation, then modelled the switching and termination of directional centroid runs. At longer lags, centroid translation carried an increasing share of player displacement. Run lifetimes were broad but finite: termination was highest near onset, declined with age and rose modestly later. After accounting for speed and directional persistence, collective order added only limited predictive information. At the population level, an age-dependent switching-and-termination model predicted, on held-out match dates, the early enrichment and later depletion of high-order states among surviving runs. These results link player- and team-level Lévy-like movement through a finite-lived collective mode whose age and internal state shape its survival. Long displacements need not be selected in advance; they can emerge when transient collective modes persist, reorganise and selectively survive. More broadly, trajectory statistics may record which dynamical histories survive, not only the rules by which agents move.

physics.soc-ph↗

arXiv2019

Single-cell approaches to cell competition: high-throughput imaging, machine learning and simulations

Cell competition is a quality control mechanism in tissues that results in the elimination of less fit cells. Over the past decade, the phenomenon of cell competition has been identified in many physiological and pathological contexts, driven either by biochemical signaling or by mechanical forces within the tissue. In both cases, competition has generally been characterized based on the elimination of loser cells at the population level, but significantly less attention has been focused on determining how single-cell dynamics and interactions regulate population-wide changes. In this review, we describe quantitative strategies and outline the outstanding challenges in understanding the single cell rules governing tissue-scale competition dynamics. We propose quantitative metrics to characterize single cell behaviors in competition and use them to distinguish the types and outcomes of competition. We describe how such metrics can be measured experimentally using a novel combination of high-throughput imaging and machine learning algorithms. We outline the experimental challenges to quantify cell fate dynamics with high-statistical precision, and describe the utility of computational modeling in testing hypotheses not easily accessible in experiments. In particular, cell-based modeling approaches that combine mechanical interaction of cells with decision-making rules for cell fate choices provide a powerful framework to understand and reverse-engineer the diverse rules of cell competition.

q-bio.TO↗