L. Minati | Searcharxiv

arXiv2026

Parenclitic hypergraphs and their application in personalized cancer therapy

Understanding the differences between individual instances of the same complex system remains a central challenge, particularly in biological contexts. Parenclitic networks constitute a suitable means to detect deviations in correlations with respect to reference populations. Here, we introduce parenclitic hypergraphs, a general framework for identifying anomalies in higher-order correlations across arbitrary interaction orders. After validating the method on synthetic datasets and benchmark ones, we apply it to patient-derived cancer organoids, capturing temporal changes in gene expression between healthy and cancerous tissues as the disease progresses. Our approach not only reproduces known oncogenic signatures, but also reveals a previously unrecognized candidate therapeutic target. Since organoids are generated from individual patients, our method provides, for the first time, a viable protocol for personalized cancer therapy based on higher-order correlation patterns. These findings offer a novel, systems-level strategy for precision oncology grounded in complex systems theory.

q-bio.QM↗

arXiv2019

Hysteresis, neural avalanches and critical behaviour near a first-order transition of a spiking neural network

Many experimental results, both in-vivo and in-vitro, support the idea that the brain cortex operates near a critical point, and at the same time works as a reservoir of precise spatio-temporal patterns. However the mechanism at the basis of these observations is still not clear. In this paper we introduce a model which combines both these features, showing that scale-free avalanches are the signature of a system posed near the spinodal line of a first order transition, with many spatio-temporal patterns stored as dynamical metastable attractors. Specifically, we studied a network of leaky integrate and fire neurons, whose connections are the result of the learning of multiple spatio-temporal dynamical patterns, each with a randomly chosen ordering of the neurons. We found that the network shows a first order transition between a low spiking rate disordered state (down), and a high rate state characterized by the emergence of collective activity and the replay of one of the stored patterns (up). The transition is characterized by hysteresis, or alternation of up and down states, depending on the lifetime of the metastable states. In both cases, critical features and neural avalanches are observed. Notably, critical phenomena occur at the edge of a discontinuous phase transition, as recently observed in a network of glow lamps.

q-bio.NC↗