Biology across
time & space
Connecting RNA Sequence and Function to design better medicines.
Supported by & in collaboration with
01 Thesis
Better RNA design starts with measured function.
AI can easily propose new RNA designs, but measuring how they actually work remains the bottleneck. Which molecules bind the right target and avoid the wrong one? Which sequences withstand degradation? Current techniques lack the throughput to answer these questions efficiently.
Our engine is designed to connect these measurements to RNA sequences at scale, spotting promising candidates and generating the experimental data needed to train and validate better models of RNA function.
02 Platform
Linking RNA sequence and function
Measure, sequence, learn
-
Single-molecule measurements
Our imaging and analysis pipeline turns single-molecule movies into binding kinetics, revealing molecular interactions in real time.
-
Sequence identity
In situ sequencing to link each RNA molecule's measured behaviour to its underlying sequence.
-
Data for better RNA design
Pairing sequence with measured function will create datasets to train and test AI models, and guide the next rounds of RNA designs.
From molecules to living systems
-
Fluorescent RNA aptamers
Optimising light-up RNA tags for live-cell imaging, giving researchers tools to follow RNA across space and time. Our first optimisation programme for the engine.
-
Mitochondrial RNA targeting
Discovering sequences that could help deliver RNA to mitochondria, through an ARIA-funded programme in collaboration with Brown University.
-
Validation in living systems
Our validation strategy takes promising candidates into living systems to confirm that molecular improvements translate into biological function.
03 Team
A multidisciplinary team
We bring together RNA biology, single-molecule biophysics, advanced imaging and machine learning, based at the MRC Laboratory of Medical Sciences in London.
04 FAQ
Frequently asked questions
We connect detailed measurements of individual RNA molecules with their sequence identities. We compare candidates by how they behave, including binding kinetics and stability, and use those results to guide RNA optimisation and discovery.
The platform is designed to generate paired sequence and function data for training and testing models. Our goal is a learning loop in which experimental results inform the next round of RNA designs, so each campaign can improve both the candidates and the models used to design them.
Light-up aptamers are one of our first optimisation programmes. These RNA tags bind a dye and switch on its fluorescence, helping researchers follow RNA in living cells. They are also a valuable reporter system to validate the activity of other functional RNA where it matters.
We welcome conversations with research teams, drug developers and AI companies working on RNA discovery and optimisation. Get in touch to discuss your research question or a potential partnership.
