Building the future of the biodigital industry, closing the loop between in silico and wet-lab.

Reducing the uncertainty of wet-lab experiments through an integrated network perspective of multi-omics and physicochemical conditions.

Discover MoNA
MEET MoNA

A Multi-Omic Network Atlas made to understand and navigate cell types and states.

MoNA tackles the challenge of accurately mapping and digitally representing the intricate relationships between multi-omic phenotypic profiles, transomic dynamics, and the physical-chemical conditions that govern biological processes.

Multi and trans-omic

Genomic, transcriptomic and proteomic information obtained from wet-lab assays.

Knowledge injected

System biology based ontologic relationships are extracted from all available literature.

Environmentally relevant

Culture media, temperature, gases, pH and other cellular environments conditions are included.

Every cell-based network you can imagine, MoNA can turn it into a map.

A structured pipeline to create cell to cell, genes to genes, genes to transcripts, cell identity to overexpressed genes or even experiment to experiment.

How we built itMapping the cellular universe.

Biological systems are truly complex, multilayered and diverse, which makes them especially challenging to represent in a digital form.

1 /
Every model starts with data

We started by creating the Big Omic Database, unifying genetic, transcriptomic, and proteomic data into a machine-learning-ready framework.
To add structure and meaning, we built a Knowledge Graph, layering scientific insights that capture not just correlations, but causal relationships. By combining data-driven approaches with human insight, we transformed raw data into a rich, meaningful representation of cellular behavior.

2 /
Understanding what makes a cell unique

Using a network-based perspective, we define cell identity through hierarchical relationships and causal impact, rather than solely observing the presence or concentration of biomolecules or environmental conditions.
Focusing on network architecture shows how small changes in critical biomolecules can trigger bigger systemic effects than large changes in non key cell elements.

3 /
Extracting the cell identity

We compressed each cell state to its most essential form using autoencoders, a deep learning technique. By compressing cell states through a narrow bottleneck and reconstructing them, we achieved high signal-to-noise ratios, creating minimal yet accurate representations of each cell’s identity.

4 /
Mapping the cellular universe

Having compressed and represented each of our cells into a latent space means we can extract them from that multidimensional representation to whatever map we find useful. The closer two cell identities are, the more their behavior and phenotypes will be alike in the real world. This system can also work in reverse, using a generative approach to simulate and explore potential cell states, expanding our understanding of cellular behavior.

The future of bioinnovation

Exploring the possibilities of transomic, network based analysis.

Cells as factories
Explore the cell space for options in biomanufacturing.

Choose the right cell line, spot the right clone and find the perfect environmental conditions for maximum production for cell products like mAbs, biomaterials and more.

Clone 1250385324.5
Culture conditions: 36C° - 48hsLow oxygen and maximum culture flow
Cells as targets
Simulate the effects of different stimuli on multiple cell identities.

Increase or decrease RNA expression or protein abundance, or even change physicochemical conditions and understand how they will affect specific cell identities.

Cell with GLP-1 agonist
Control cell
Cells as products
Design and grow specific types of cells.

Find the best phenotype for a given therapy, but also the best starting cell types and specific protocols to generate your desired cell product.

Somatic cell
Pluripotent stemm cell
Co-pilot
Optimize wet-lab assays

Wet-lab assay design and decision-making are costly, labor-intensive, and inherently uncertain due to the complexity of biomolecular systems. Use MoNA to search for the most informative wet-lab assays design leveraging knowledge graphs, public literature, and multi-omics data among others.

Interface demonstration

A step forward in the biodigital industry

Explore today what we will be able to do tomorrow.

Bio to digital

The first step towards fully digital cell design, engineering and production

Stamm Logo

Inspired by nature,
driven by data,
built for humanity.

Biomanufacturing everywhere.

Our mission is to revolutionize the biomanufacturing industry through advanced manufacturing solutions for automatized, continuous & improved biologics & cell therapies production.