Standard analyses see slices. Auperon sees the full molecular universe cells operate in — then translates that universe into recommendations you can actually implement.
Every analytical technology sees a slice. To design better media, better feed, or better recommendations, you need the whole molecular picture — not one slice.
Metabolomics captures small molecules. Proteomics captures protein signals. Standard RNA-seq analysis gives you differential expression tables and pathway lists. Each one shows a fragment of what's happening in your cells.
Miss the trace elements driving stress. Miss the growth factors shaping cell fate. Miss the metabolic pathways connecting expression to phenotype. Miss the mechanism, and you're guessing at recommendations from a partial view.
Auperon was built to unify those signals — mapping the entire molecular weight range of small and large molecules across your cells, then connecting what's measured to the reactome, to biological function, to actionable design.
Auperon maps the entire molecular weight range of large and small molecules for your cells — from trace elements to gene expression, and everything cells use in between to signal, metabolize, and respond.
Three integrated layers translate raw molecular signal into a working model of your cells and a ranked list of things you can do about it.
Multi-omics: bulk RNA-seq at 50–150M paired-end depth (or your data format), plus proteomics and metabolomics where scoped.
What comes out: molecular signal across the entire measured weight range — expression, metabolic state, stress markers, cofactor availability.
Informatics, deep learning, and digital twinning translate raw molecular signal into biological function — pathway-level insight, biomarker candidates, existing-molecule modulator identifications.
What comes out: mechanism-backed insight ranked by expected impact on your target phenotype.
Your specific cells recapitulated in the cloud. Interactive dashboards, conversational query, and hypothesis simulation — built on your data, accessible after the engagement ends.
What comes out: a working model your team can interact with, ask questions of, and use to guide future work.
The cellular digital twin is what separates Auperon from any static analysis. Once your data is loaded, you have a working model of your specific cells that you can interact with — not a report you read once and archive.
You get continued access after the engagement ends. Follow-up questions that don't require new sequencing are typically answerable directly from the existing twin — quick and inexpensive.
Auperon's outputs are engineered to be actionable — validatable independently, sourceable from existing supply, and defensible as IP.
Novel biomarkers linked to specific CQAs — potency, viability, manufacturing success, therapy-relevant outcomes. Discovered from the multi-omic signal in your specific cells, not pulled from a generic panel.
Panel candidates you can validate independently and use for QC, comparability, process release, or regulatory documentation.
Existing, characterized molecules that modulate the pathways driving your target phenotype. Novel IP through novel combinations — not new chemistry that needs to be qualified, characterized, or cleared.
Real, defensible IP with a cleaner CMC path. Your team can source these components from existing supply and go straight to validation.
The most common question we get on intro calls is "how is this different from just running RNA-seq analysis?" Here's how — across the four comparisons that come up most often.
Standard tools give you differential expression tables and pathway enrichment lists. Auperon gives you mechanism-backed recommendations ranked by expected impact — with specific biomarker candidates and existing-molecule modulator identifications you can act on.
Metabolomics catches metabolites. Auperon captures the full molecular universe — including the transcriptional and regulatory signals that connect metabolic state to cell fate, and the digital twin that ties all of it into function.
Proteomics catches proteins. Auperon connects protein signals to the pathways they modulate and the phenotypes they produce — so the output isn't "here's what's expressed," it's "here's what you can change."
Software tools give you dashboards. Auperon gives you dashboards backed by direct scientific-team access — mechanism-backed insight from PhD-level scientists who worked on your specific data, not a support queue.
Case studies show the full workflow on real programs. Getting Started covers what to send us. Try It Now kicks off an engagement.