MULLIX builds a computational twin of a disease programme from your omics data — ranking druggable hub genes, simulating intervention scenarios, and surfacing candidates worth taking into your next preclinical study.
MULLIX slots ahead of your existing preclinical workflow — it doesn't replace your assays, it tells you which targets are worth running them on.
Transcriptomic, proteomic, or genetic association data from your programme — human, iPSC, or model-organism.
MULLIX builds a network-level model of the disease state, integrating expression, pathway, and regulon information.
Hub genes are scored and ranked; candidate interventions are simulated against the twin's disease trajectory.
A decision-ready report — ranked targets, supporting evidence, and open questions — for your study team.
A shortlist built on network-wide evidence rather than single-gene intuition — so bench time goes toward candidates already stress-tested in silico.
The same twin framework runs across disease models, so hub genes and mechanisms can be compared across a portfolio, not just within one study.
Test a candidate's predicted effect on the disease trajectory before committing wet-lab time and reagents to it.
Every ranked candidate carries its supporting pathway and expression evidence, so your team can interrogate — not just accept — the shortlist.
Disease-stage-resolved expression modelling to capture how a pathway shifts over progression, not just at one timepoint.
Genes are ranked by their position and influence within the reconstructed disease network, not by fold-change alone.
Candidates are cross-checked against pathway and gene-set enrichment to flag mechanism, not just correlation.
Simulated intervention scoring highlights candidates predicted to shift the twin's trajectory back toward a reference state.
Set up a walkthrough with your own dataset, or a representative example from your disease area.