A5081448028- Gene Regulatory Network Analysis
- Cancer, Hypoxia, and Metabolism
- Mathematical Biology Tumor Growth
- Bioinformatics and Genomic Networks
- CRISPR and Genetic Engineering
- Gene expression and cancer classification
- Statistical Methods in Clinical Trials
- Microbial Metabolic Engineering and Bioproduction
- Computational Drug Discovery Methods
- Diabetes Management and Research
- Pharmacogenetics and Drug Metabolism
- MicroRNA in disease regulation
- Analytical Chemistry and Chromatography
- Ubiquitin and proteasome pathways
- interferon and immune responses
- Statistical Methods and Bayesian Inference
- Cancer Genomics and Diagnostics
- Time Series Analysis and Forecasting
- Statistical Methods and Inference
- Advanced Proteomics Techniques and Applications
- Metabolomics and Mass Spectrometry Studies
- Cell Image Analysis Techniques
- Bacterial Genetics and Biotechnology
- Genomics and Rare Diseases
- Cancer Cells and Metastasis
University of Pavia
2016-2025
MultiMedica
2025
University of Milan
2025
Engineering (Italy)
2024
University of Padua
2005
Azienda Ospedaliera di Desio e Vimercate
2005
NOAA Great Lakes Environmental Research Laboratory
2000
University of Michigan–Ann Arbor
2000
Whitman College
2000
Woods Hole Oceanographic Institution
2000
The available mathematical models describing tumor growth and the effect of anticancer treatments on tumors in animals are limited use within drug industry. A simple effective model would allow applying quantitative thinking to preclinical development oncology drugs. In this article, a minimal pharmacokinetic-pharmacodynamic is presented, based system ordinary differential equations that link dosing regimen compound animal models. nontreated described by an exponential followed linear...
OBJECTIVE After testing of a wearable artificial pancreas (AP) during evening and night (E/N-AP) under free-living conditions in patients with type 1 diabetes (T1D), we investigated AP day (D/N-AP) for month. RESEARCH DESIGN AND METHODS Twenty adult T1D who completed previous randomized crossover study comparing 2-month E/N-AP versus sensor augmented pump (SAP) volunteered 1-month D/N-AP nonrandomized extension. was executed by model predictive control algorithm run modified smartphone...
Abstract Genomic variant interpretation is a critical step of the diagnostic procedure, often supported by application tools that may predict damaging impact each or provide guidelines-based classification. We propose Machine Learning methodologies, in particular Penalized Logistic Regression, to support classification and prioritization. Our approach combines ACMG/AMP guidelines for germline as well annotation features provides probabilistic score pathogenicity, thus supporting...
Genome-scale metabolic models (GEMs) allow predicting phenotypes from limited data on uptake and secretion fluxes by defining the space of all feasible solutions excluding physio-chemically biologically unfeasible behaviors. The integration additional biological information in genome-scale models, e.g., transcriptomic or proteomic profiles, has potential to improve phenotype prediction accuracy. This is particularly important for engineering applications where more accurate model predictions...
CRISPRi-mediated gene regulation allows simultaneous control of many genes. However, highly specific sgRNA-promoter binding is, alone, insufficient to achieve independent transcriptional multiple targets. Indeed, due competition for dCas9, the repression ability one sgRNA changes significantly when another becomes expressed. To solve this problem and decouple sgRNA-mediated regulatory paths, we create a dCas9 concentration regulator that implements negative feedback on level. This any...
Abstract Identifying disease-causing variants in Rare Disease patients’ genome is a challenging problem. To accomplish this task, we describe machine learning framework, that called “Suggested Diagnosis”, whose aim to prioritize genetic an exome/genome based on the probability of being disease-causing. do so, our method leverages standard guidelines for germline variant interpretation as defined by American College Human Genomics (ACMG) and Association Molecular Pathology (AMP), inheritance...
Whey permeate is a lactose-rich effluent remaining after protein extraction from milk-resulting cheese whey, an abundant dairy waste. The lactose to ethanol fermentation can complete whey valorization chain by decreasing waste polluting potential, due its nutritional load, and producing biofuel renewable source at the same time. Wild type engineered microorganisms have been proposed as biocatalysts. However, they present different drawbacks (e.g., supplements requirement, high...
Abstract Polycythemia vera (PV) is a chronic myeloproliferative neoplasm characterized by excessive levels of platelets (PLT), white blood cells (WBC), and hematocrit (HCT). Givinostat (ITF2357) potent histone‐deacetylase inhibitor that showed good safety/efficacy profile in PV patients during phase I/II studies. A III clinical trial had been planned an adaptive dosing protocol proposed where givinostat dose iteratively adjusted every 28 days (one cycle) based on PLT, WBC, HCT. As support,...
Abstract Background A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing genome-wide. To aid the interpretation prioritization of vast number detected, computational methods proliferating. Knowing which tools most effective remains unclear. evaluate performance methods, to encourage innovation method development, we designed Critical...
Understanding the pharmacokinetics, safety and efficacy of candidate drugs is crucial for their success. One key aspect characterization absorption, distribution, metabolism, excretion toxicity (ADMET) properties, which require early assessment in drug discovery development process. This study aims to present an innovative approach predicting ADMET properties using attention-based graph neural networks (GNNs). The model utilizes a graph-based representation molecules directly derived from...
Abstract The global race against antimicrobial resistance requires novel antimicrobials that are not only effective in killing specific bacteria, but also minimize the emergence of new resistances. Recently, CRISPR/Cas-based were proposed to address specificity with encouraging results. However, target sequence mutations triggered by Cas-cleavage was identified as an escape strategy, posing risk generating antibiotic-resistance gene (ARG) variants. Here, we evaluated antibiotic...
Abstract The digenic inheritance hypothesis holds the potential to enhance diagnostic yield in rare diseases. Computational approaches capable of accurately interpreting and prioritizing combinations variants based on proband’s phenotypes family information can provide valuable assistance during process. We developed diVas, a hypothesis-driven machine learning approach that interprets genomic across different gene pairs. DiVas demonstrates strong performance both classifying causative within...
Uncertainty often affects molecular biology experiments and data for different reasons. Heterogeneity of gene or protein expression within the same tumor tissue is an example biological uncertainty which should be taken into account when markers are used in decision making. Tissue Microarray (TMA) allow large scale profiling biopsies, investigating patterns characterizing specific disease states. TMA studies deal with multiple sampling patient, therefore measurements target, to possible...
Abstract Summary: TimeClust is a user-friendly software package to cluster genes according their temporal expression profiles. It can be conveniently used analyze data obtained from DNA microarray time-course experiments. implements two original algorithms specifically designed for clustering short time series together with hierarchical and self-organizing maps. Availability: executable files Windows LINUX platforms downloaded free of charge non-profit institutions the following web site:...
Inducible promoters are widely spread genetic tools for triggering, tuning and optimizing the expression of recombinant genes in engineered biological systems. Most them controlled by addition a specific exogenous chemical inducer that indirectly regulates promoter transcription rate concentration-dependent fashion. In order to have robust predictable degree control on activity, degradation such chemicals should be considered many applications like protein production.In this work, we use...
The lack of a common exchange format for mathematical models in pharmacometrics has been long-standing problem. Such the potential to increase productivity and analysis quality, simplify handling complex workflows, ensure reproducibility research, facilitate reuse existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by Drug Disease Model Resources (DDMoRe) consortium, is intended become an standard providing means encode models, trial designs,...