<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/D38D03AB-B1B9-43F5-9546-8DA88BCF9525" ns1:id="D38D03AB-B1B9-43F5-9546-8DA88BCF9525"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/2309B439-0127-4B84-9DEB-EA06A7EE1189" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/F6815F35-C351-41D5-9A2E-44BCDFF13A52" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/F6815F35-C351-41D5-9A2E-44BCDFF13A52" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2026-07-30T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/9AAD838B-439C-4600-8DC8-EBBBFAA0DC88" ns1:rel="FUND" ns1:start="2025-07-31T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10160998</ns2:identifier></ns2:identifiers><ns2:title>Mechanomics: scoping potential for a new data modality in disease biomarker and target discovery.</ns2:title><ns2:status>Active</ns2:status><ns2:grantCategory>Feasibility Studies</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>SynLaia Innovations is set to bring mechanical dimension to human biology and drug discovery. Through our unique mechano-transcriptomics computational pipeline, we aim to directly address a significant unmet need in drug development arising from high therapy failure rates, partly due inadequate disease modelling and lack of tissue-scale data. Our innovation is an **AI-driven, patient-centric drug discovery platform leveraging mechanomics** as a novel data modality. This approach uniquely integrates spatial mechano-physical parameters with multi-modal omics data (such as spatial transcriptomics and proteomics) to generate **mechano-signatures and cell and tissue mechanical states in health and disease**. Unlike standard target discovery relying on genetic predictions from single-cell technologies, we obtain spatial multi-modal omics data from human samples for direct translation within the correct tissue context. This approach allows it to pinpoint **biomarkers and drug targets by analysing how tissue mechanics and molecular profiles change across health and disease**, focusing on therapeutic targets which are drivers of tissue mechanical states.</ns2:abstractText></ns2:project>