Our work combines machine learning, statistical modelling, and multi-omics data integration to uncover biological pathways, protein networks, and molecular signatures associated with health, aging, and disease.
We collaborate with partners to analyze high-quality proteomic datasets, generate new scientific insights, and prioritize candidates for further mechanistic investigation using emerging proteomics technologies.
We develop computational tools to analyze large proteomic datasets and reveal biological patterns that are difficult to detect with traditional methods.
Chief Executive Officer
Pablo is a research scientist with a DPhil in Chemical Biology from the University of Oxford, specializing in proteomics, nanopore sensing, and single-molecule biophysics. His work has advanced emerging protein sequencing technologies, integrating protein chemistry, nanopore engineering, and molecular diagnostics. He has authored peer-reviewed publications and contributed to patented methods for nanopore-based protein characterization.
Chief Scientific Officer
Giacomo is a research scientist with a Chemical Biology PhD from the University of Oxford. His deep expertise in nucleic acid chemistry and nanotechnologies has enabled the creation of gene regulation tools and synthetic biology engineering. He also has extensive experience in protein biochemistry, enzymology, and medicinal chemistry. He brings a highly multidisciplinary approach bridging chemistry, biology, and materials science to engineer complex biological and chemical systems.
Chief Technology Officer
Sara is a data scientist specializing in machine learning, advanced analytics, and large-scale data engineering. With dual master's degrees in Data Science and Machine Learning and a prior background in business strategy, Sara combines technical depth with strategic understanding. Her work focuses on developing robust analytical frameworks, AI models, and scalable data architectures that enable complex decision-making and high-value insights.
Our research focuses on:
Protein biomarker discovery using large-scale population datasets
Pathway and network analysis to understand mechanisms of aging, immunity, and disease
Integration of proteomics with clinical, lifestyle, and demographic data
Machine-learning models to identify protein signatures associated with physiological states
Cross-platform harmonization (Olink, SomaScan, mass spectrometry)
Prioritization of proteins and pathways for experimental follow-up
Our research program explores:
Our goal is to expand scientific understanding of how circulating proteins reflect human biology and to support deeper mechanistic research in collaboration with academic and industry partners.
We combine computational biology with advanced proteomics to generate interpretable biological insights.
Statistical rigor and reproducible science at every step
Advanced machine-learning frameworks for pattern discovery
Validation across diverse populations and platforms
Leveraging publicly available proteomic and multi-omics data
Transparent methods and open scientific practices
Partnerships with labs and technology developers
We aim to bridge large-scale population proteomics with mechanistic biology.
We are committed to open, rigorous, and transparent scientific methods:
Large-scale proteomic datasets (antibody- and aptamer-based platforms)
Public multi-omics resources
Mass spectrometry–based datasets
Advanced machine-learning models
Statistical genetics and causal inference
Pathway enrichment and network inference
Cross-platform data harmonization
We specialize in extracting biological structure from high-dimensional proteomic matrices, identifying coherent molecular signatures across diverse cohorts, and mapping these to mechanistic pathways using multi-omics integration frameworks.
We collaborate with academic groups, biobanks, and technology developers.
Analyze proteomic data
Interpret mechanistic findings
Evaluate new protein measurement technologies
Explore biological variation across populations
Support hypothesis generation for downstream studies
We welcome inquiries from researchers interested in proteomics, multi-omics integration, or computational biology.
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