AMBIOM Project
Advanced Metabolomic analysis and Multi-omic Integration for Bioprinted and Microfluidic Models of disease
AMBIOM is a PNRR D3 4 Health research project led by THEOREO to develop robust metabolomic and multi-omic workflows for advanced in vitro disease models.
AMBIOM focuses on the molecular characterization of complex biological systems such as 3D organoids, bioprinted constructs and microfluidic disease models, with the goal of generating reproducible, interpretable and clinically relevant omic data for disease modeling, patient stratification and future digital twin-oriented applications.
Project Overview
The increasing complexity of biomedical research requires experimental models that are more representative of human physiology and disease. Organoids, bioprinted systems and microfluidic platforms offer unprecedented opportunities to reproduce tissue-specific features, disease-associated mechanisms and patient-derived biological variability under controlled conditions.
However, the translational value of these models depends on the quality of the molecular data that can be extracted from them.
AMBIOM addresses this challenge by developing a structured pipeline to pursue the following objectives:-
1. Standardization of sample preparation workflows
To develop and validate robust protocols for metabolite extraction from cellular models, organoids and microfluidic systems, minimizing pre-analytical variability and preserving the biological integrity of the metabolomic profile. -
2. High-resolution metabolomic profiling
To generate metabolomic datasets using LC–HRMS technologies, enabling broad metabolome coverage and supporting the identification of metabolic signatures associated with disease-relevant biological states. -
3. Analytical quality control and reproducibility
To implement quality-control strategies based on internal standards, pooled QC samples, batch monitoring, retention time stability, mass accuracy and multivariate assessment of technical variability. -
4. Multi-omic data integration
To integrate metabolomic data with transcriptomic and epigenomic layers, enabling a deeper understanding of disease mechanisms through combined molecular information. -
5. Predictive modeling and digital health
To establish the analytical and computational foundations for predictive models and future digital twin-oriented approaches in precision medicine. -
6. FAIR and interoperable data structures
To promote data traceability, reproducibility, interoperability and reuse, in alignment with FAIR principles and international standards for metabolomics.
Theoreo's Role
THEOREO is the scientific and operational lead of AMBIOM, providing the analytical, computational and translational expertise required to transform advanced biological models into high-quality omic data.
- High-resolution metabolomicsTHEOREO contributes its expertise in LC–MS/MS technologies, targeted and untargeted metabolomic profiling, and advanced analytical workflows for the molecular characterization of organoids, cellular models and microfluidic systems.1
- Sample preparation and quality governanceThe company develops and applies standardized protocols for sample preparation, metabolite extraction, internal quality controls, batch monitoring and analytical reproducibility, ensuring robust and traceable data generation.2
- Data processing and multi-omic interpretationTHEOREO supports the transformation of raw metabolomic signals into interpretable biological information through preprocessing, normalization, statistical analysis and integration with complementary omic layers.3
- Predictive modeling and digital healthThrough AMBIOM, THEOREO advances computational approaches for biomedical innovation, supporting predictive modeling, precision medicine applications and future digital twin-oriented frameworks.4
Ambiom & Digital Twins
Digital twins represent one of the most promising frontiers of precision medicine: computational models capable of integrating biological data to describe, simulate and potentially predict disease-related processes.
However, a digital twin cannot be built on abstract data alone. It requires reliable molecular information that reflects the functional state of a biological system.
This is where metabolomics plays a key role. By capturing biochemical activity close to the phenotype, metabolomics provides a functional readout of cellular metabolism, stress responses, disease-associated alterations and adaptation to experimental or therapeutic conditions.
AMBIOM works in this direction by generating standardized, quality-controlled metabolomic data from advanced in vitro models such as organoids, bioprinted systems and microfluidic platforms. In this perspective, AMBIOM does not create a digital twin as a single final product, but builds some of the essential foundations required for future digital twin-oriented biomedical frameworks: robust biological models, high-quality omic data, analytical reproducibility and computational readiness.
However, a digital twin cannot be built on abstract data alone. It requires reliable molecular information that reflects the functional state of a biological system.
This is where metabolomics plays a key role. By capturing biochemical activity close to the phenotype, metabolomics provides a functional readout of cellular metabolism, stress responses, disease-associated alterations and adaptation to experimental or therapeutic conditions.
AMBIOM works in this direction by generating standardized, quality-controlled metabolomic data from advanced in vitro models such as organoids, bioprinted systems and microfluidic platforms. In this perspective, AMBIOM does not create a digital twin as a single final product, but builds some of the essential foundations required for future digital twin-oriented biomedical frameworks: robust biological models, high-quality omic data, analytical reproducibility and computational readiness.