Biology subjects

Rossi, T.

Publications and source records attributed to Rossi, T..

3 recordsLinked to original sources

bioRxiv2026

A Thermodynamically Consistent Reaction--Diffusion Model of Spatial Proofreading in One and Two Dimensions

Spatial proofreading is a mechanism that can enhance molecular discrimination by exploiting nonequilibrium diffusive transport between spatially separated source and readout regions. Here, we introduce a thermodynamically consistent reaction--diffusion model in which a gradient of active substrates is generated self-consistently by a reversible kinase--phosphatase switch coupled to nucleotide chemostats. The resulting chemical-potential gradient explicitly controls the nonequilibrium driving and allows the discrimination potential to be related to microscopic chemical rates and diffusive transport. A simple one-dimensional analysis shows that the ability of an enzyme to discriminate between wrong and right substrates is governed by a subtle balance between substrate-gradient confinement, controlled by phosphatase activity, the diffusive crossing time across the source--readout domain, and selective complex dissociation. We then extend the model to two-dimensional domains, showing that source, i.e. kinase, localization modulates spatial specificity by shaping the effective diffusive paths to the readout boundary. Finally, stochastic simulations reveal that molecular fluctuations generate intermittent wrong-readout events, which can be characterized through an event-weighted measure of specificity. Interestingly, we find that fluctuations promote frequent transitions to long-residence states in which discrimination is better than predicted by the deterministic estimate. Overall, our work highlights the importance of thermodynamically consistent descriptions of spatial proofreading and clarifies how energy input, transport, and spatial organization jointly shape biochemical discrimination.

biophysics↗

bioRxiv2024

Accelerating joint species distribution modeling with Hmsc-HPC: A 1000x faster GPU deployment

Joint Species Distribution Modelling (JSDM) is a powerful and increasingly widely used statistical methodology in biodiversity modelling, enabling researchers to assess and predict the joint distribution of species across space and time. However, JSDM can be computationally intensive and even prohibitive, especially for large datasets and sophisticated model structures. To address computational limitations of JSDM, we expanded one widely used JSDM framework, Hmsc-R, by developing a Graphical Processing Unit (GPU) -compatible implementation of its model fitting algorithm. While our augmented framework retains the original user interface in R, its new computational core is coded in Python and dominantly uses TensorFlow library. This enhancement primarily targets to enable leveraging high-performance computing resources effectively, though it also accelerates model fitting with consumer-level machines. This upgrade is designed to leverage high-performance computing resources more effectively. We evaluated the performance of the proposed implementation across diverse model configurations and dataset sizes. Our results indicate significant model fitting speed-up compared to the existing Hmsc-R package across most models. Notably, for the largest datasets, we achieved >1000 times speed-ups. This GPU-compatible enhancement boosts the scalability of Hmsc-R package by several orders of magnitude, reaching a significantly higher level. It opens promising opportunities for modeling extensive and intricate datasets, enabling better-informed conservation strategies, environmental management, and climate change adaptation planning. Author summaryOur study addresses the computational challenges associated with Joint Species Distribution Modelling (JSDM), a critical statistical methodology for understanding species distributions in biodiversity research. Despite its utility, JSDM often faces computational limitations, particularly for large datasets. To overcome this hurdle, we enhance the widely used Hmsc-R framework by introducing a GPU-compatible implementation of its model fitting algorithm. Our upgraded framework, while retaining the user-friendly R interface, leverages Python and TensorFlow for its computational core, enabling efficient utilization of high-performance computing resources. Through extensive evaluation across diverse model configurations and dataset sizes, we demonstrate substantial speed-ups compared to the original Hmsc-R package, with over 1000 times speed-ups observed for the largest datasets. This GPU-compatible enhancement significantly improves the scalability of JSDM, enabling the analysis of extensive and complex biodiversity datasets. Our work has far-reaching implications for informing conservation strategies, environmental management, and climate change adaptation planning by facilitating more efficient and accurate biodiversity modeling, ultimately contributing to better-informed decision-making in ecological research and practice.

ecology↗

bioRxiv2018

Glutathione S-Transferase Genotype polymorphism and Primary Open-Angle Glaucoma in an Italian population

PurposeOxidative damage to the trabecular meshwork (TM) represents one of the pathogenic mechanisms leading to primary open angle glaucoma (POAG). Glutathione S-transferase mu 1 (GSTM1) may neutralizes reactive oxygen species protecting the TM. The present paper investigates the prevalence of GSTM1 null genotype in an Italian population, and its association with POAG treated either medically or surgically.\n\nMethodsIn a case-control study, the GSTM1 genotype was identified in POAGs and controls. The POAGs patients were divided in two groups: medical POAGs and surgical POAGS. Medical POAGs consisted of patients with a well-controlled intraocular pressure (IOP) by IOP-lowering medications and a stable visual field (VF). Patients with an uncontrolled IOP and a progressing VF that were submitted to incisional surgery formed the surgical POAGs group.\n\nResultsWe enrolled 104 medical POAGs, 158 surgical POAGs and 263 Controls. No significative differences between the groups existed regarding age and gender (p=0.275 and p=0.950, respectively). All the enrolled subjects were Caucasian of Italian descents. The GSTM1 null genotype was identified in 57 (45.2%) medical POAGs, 91 (57.6%) surgical POAGs and, 119 (45.3%) controls (p=0.033). The association between medical POAG and GSTM1 null status was non-significant (OR= 1.44, 95% IC = 0.86 to 2.39) whereas the association was significant for surgical POAGs (OR= 2.01, 95% IC= 1.26 to 3.21)\n\nConclusionsOur results showed an association between the GSTM1 null genotype and glaucoma that require surgery in an Italian population. GSTM1 null genotype detection may help to identify high-risk glaucoma patients that require a closer follow-up and a more aggressive treatments.

epidemiology↗