Prof Nicolas Voelcker
Program Lead
Monash University
PROGRAM 3
Why This Matters
The value of microphysiological systems (MPS) depends not only on how closely they replicate human biology, but also on the quality of the data they generate. To become trusted tools for medical product development, MPS must provide robust and meaningful information about how tissues and organs respond to drugs and other interventions. Many current MPS platforms rely on endpoint measurements that provide only limited snapshots of biological activity.
MiPSET's Data Integration & Analytics Program is developing advanced sensing, imaging and analytical technologies that enable non-disruptive, continuous monitoring of living tissues and the generation of richer, more informative datasets. These capabilities will improve biological insight, strengthen confidence in MPS-derived data, and support the adoption of MPS technologies across research, drug development and regulatory applications.
WHAT WE ARE EXPLORING
Research Questions
01
How can electrochemical, optical, and mechanical sensors be integrated into existing MPS platforms for continuous, non-destructive monitoring of biochemical and biophysical parameters?
02
What sensor performance criteria (sensitivity, selectivity, stability) are required to validate MPS models against gold-standard animal and clinical benchmarks?
03
How can machine learning algorithms be applied to multimodal biosensor data streams from MPS to generate accurate human response predictions?
04
Can advanced imaging and spectral sensor technologies provide spatial resolution sufficient to map cellular responses within 3D MPS in real time?
OUTCOMES
Potential Impact
Integration of biochemical, electrical, optical and biomechanical sensors to generate richer and more informative biological datasets.
Continuous, non-invasive monitoring of living tissues within MPS platforms, reducing reliance on destructive testing methods.
Improved validation of MPS technologies through comparison with existing laboratory, animal and clinical benchmarks.
Intelligent, data-enabled MPS platforms that leverage machine learning and real-time analytics to predict drug efficacy, toxicity and disease responses and support biomedical research, drug development and future regulatory applications.
PEOPLE IN THIS PROGRAM
Researchers and Investigators
A/Prof Michael Menden
CI – Machine Learning / Precision Medicine
UoM
A/Prof Ranjith Unnithan
CI – Imaging / Spectral Sensor Technologies
UoM
A/Prof Victor Cadarso Busto
CI – Biosurfaces & Microsystems
Monash
Dr Elizabeth Johnstone
CI – BRET / Biophysical Assays
UWA
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