Haralampos Hatzikirou

The paper maps subgingival microbiome states in 1,355 adults, identifying divergent dysbiosis trajectories linked to periodontitis and host factors.

This study identifies that a synthetic immune niche enhances T cell cytotoxicity by accelerating lytic hit delivery, not by increasing target encounters or conjugate stability.

This study models how Salmonella uses regulatory memory and growth-coupled inheritance to adjust flagellar number based on nutrient availability.

This paper develops a mathematical model to study how local and global inflammation affects hematopoiesis and clonal expansion, identifying feedback thresholds that drive disease progression.

The paper identifies ecological and stochastic factors influencing the growth and persistence of Porphyromonas gingivalis using mathematical modeling and experimental data.

This study uses a lattice-gas cellular automaton model to show inhibitory feedback from differentiated cells affects stem cell invasive potential and local fluctuations create slow-moving unstable fronts.

The paper introduces a data-driven framework combining SINDy, CSP, and neural networks to identify reduced models of biomedical systems from observational data, demonstrating success on the Michaelis-Menten model.

This study examines how smoking affects interactions between the oral microbiome, metabolism, and psychological traits using multi-omics datasets and network analysis.

This paper investigates how somatic evolution and phenotypic plasticity interact in glioblastoma, showing that phenotypic heterogeneity, not genetic diversity, better predicts poor treatment outcomes.

This paper uses an in-silico model to show that localized biopsies and macrophage density at the tumor edge improve predictions of recurrence in high-grade gliomas.

This study compares biomarkers in type 2 diabetes patients with and without comorbidities, identifying key indicators of disease progression and comorbidity impact.

BaM3 improves personalized tumor growth predictions by combining mechanistic modeling and machine learning, showing better performance in simulated and real patient data.

The paper introduces BIO-LGCA, a cellular automaton model for collective cell migration, and demonstrates its ability to predict cluster formation and cancer cell invasion plasticity.

A theory-driven treatment using heat-killed S. aureus eradicates chronic infection, reduces MDSCs, and reverses T-cell dysfunction in mice.

The paper introduces a novel averaging method for random ordinary differential equations and applies it to study how intratumoral heterogeneity affects tumor growth dynamics.

The paper develops a statistical mechanics theory for cell decision-making, applying it to model cell migration force distribution and anomalous diffusion.

The paper investigates how intracellular coupling improves the reliability of cell responses to noisy signals by drawing parallels with information theory.