Bio-Hybrid Metal-Organic Frameworks for Programmable Cellular Interfaces

About the Project

Background and Rationale

Metal–organic frameworks (MOFs) are crystalline, porous, and highly modular materials capable of hosting small molecules, ions, and biomolecules. Over the past decade, bio-MOF research has expanded from simple biomolecule encapsulation to biocompatible scaffolds and enzyme–MOF composites. Yet, a major challenge persists: current MOF–cell interfaces are largely passive. They support, deliver, or protect biological agents, but they rarely respond dynamically to cellular cues or modulate biological behaviour in real time.

Biological systems, by contrast, operate via dynamic and reciprocal signalling. Cells sense mechanical, chemical and biochemical cues and respond with tailored changes in signalling, metabolism, and differentiation. Materials capable of interacting with cells through programmable, conditional responses would open transformative opportunities in regenerative medicine, bioelectronics, immune modulation, and smart implants.

Recent advances in enzyme-responsive linkers, stimuli-responsive coordination chemistry, and MOF thin-film fabrication provide the foundation for creating MOFs that can respond to specific biological signals (e.g., pH shifts, redox gradients, protease secretion, metabolite release). Coupled with machine-learning models that map structure–function relationships, this area is now ready for step-change innovation.

Aim

To design and characterise bio-responsive MOF–cell interfaces capable of dynamically altering their structure or output (molecular release, electronic properties, surface charge, or porosity) in response to specific cellular signals, enabling real-time regulation of cellular behaviour.

Objectives

1. Design and synthesise enzyme-responsive and metabolite-responsive MOFs
   • Develop linkers that respond to enzymes such as matrix metalloproteinases (secreted during tissue remodelling) or lysosomal proteases.
   • Incorporate reversible metal–ligand motifs enabling redox-triggered structural changes.
2. Engineer MOF thin films and micro/nano-structured coatings
   • Fabricate uniform, well-controlled MOF layers on inert and bioactive substrates.
   • Tune porosity, orientation, hydrophilicity, and mechanical stiffness.
3. Investigate dynamic MOF behaviour at biological interfaces.
   • Use live-cell microscopy, operando characterisation methods, such as Raman/IR and QCM to visualise structural or surface changes triggered by cell-secreted cues.
   • Map feedback between cellular processes (adhesion, migration, differentiation) and MOF dynamics.
4. Develop an ML-driven design framework
   • Use Bayesian optimisation and graph neural networks to correlate MOF composition and linker chemistry with biological responsiveness.
   • Predict optimal MOF compositions for specific cell types (e.g., stem cells vs. immune cells).
5. Demonstrate functional applications
   • Smart implant coatings that adapt to inflammatory signals.
   • MOF-based “cell tutoring interfaces” for stem cell differentiation.
   • Bioelectronic platforms where MOFs transduce cellular activity into measurable signals.

Methodology and Work Plan

Year 1:

• Synthesis of responsive MOFs; surface functionalisation; physicochemical characterisation.
• Development of thin films via layer-by-layer deposition, or aqueous methods.
• Initial cytocompatibility tests.

Year 2:

• Real-time monitoring of MOF structural adaptation under cell-driven triggers.
• Creation of dynamic response maps; advanced spectroscopic in situ tracking.
• Machine learning model training from experimental and literature datasets.

Year 3:

• Integration into functional biointerfaces; performance tests (bioelectronic signalling, cell differentiation assays).
• Refinement of predictive modelling and design rules.
• Dissemination and thesis preparation.

Novelty and Impact

• Introduces truly dynamic MOF–cell interfaces, not yet achieved in the field.
• Opens a route to adaptive biomaterials for implants, immunomodulation, bioelectronics, and organ-on-chip systems.
• Strong interdisciplinary fit: materials science, chemical biology, device engineering, and machine learning.
• High potential for patents and industry collaboration.