A Technology
Platform for
Measuring Hidden
Physical Biology
In Physico combines nanoscale electrostatic sensing, dynamic phenotyping, AI-supported analysis, and predictive disease modeling.
Explore Applications
SUPPORTED BY COLLABORATIONS ACROSS ACADEMIA AND MEDICINE
In Physico connects physical measurement, AI-driven learning, and predictive modeling into a single discovery framework.
It turns physical signals from living systems into interpretable signatures for disease and therapy response modeling.
Physical Measurement Layer
Capture electrostatic and biophysical signals from living biological systems.
AI Science Layer
Identify hidden patterns, knowledge gaps, and biological signatures.
Predictive Modeling Layer
Transform physical signatures into disease and therapeutic response models.
Core Platform Components
Four integrated components work together to transform physical measurements into predictive biological insight.
ChargeViewer™
Capture hidden physical signals from living biological systems.
Dynamic Phenotyping™
Track biological changes continuously across time and conditions.
AI Science Engine™
Reveal hidden patterns through AI-powered scientific analysis.
Digital Disease Models™
Build predictive models from physical biological measurements.
From Physical Measurement
to Predictive Insight
In Physico connects living biological systems, physical measurements, AI-supported interpretation, and predictive modeling into one continuous technology flow.
1. Sample
Living cells, tissues, organoids, or biological interfaces are studied under biologically relevant conditions.
2. Sensing
ChargeViewer uses ultrasensitive probes to image surface charge distributions without deforming samples.
3. AI Analysis
AI-supported analysis identifies meaningful electrostatic patterns and relationships across biological systems.
4. Physical Signature
Measured signals are translated into interpretable physical signatures linked to biological states and responses.
5. Prediction
Physical signatures support predictive modeling of biological behavior, therapeutic response, and disease progression.
From Knowledge Gaps
to Advanced Predictive
Models.
The Discovery Engine extends In Physico technology into a closed-loop system for identifying gaps.
Explore Discovery EngineSCIENTIFIC
LEARNING
1. Knowledge Gaps
2. Hypothesis Generation
3. Experiment Design
4. ChargeViewer Measurements
5. Dynamic Phenotyping
6. Digital Disease ModelsChallenge
Identify unresolved biological questions.
Input
Existing research, data, and literature.
Output
Prioritized knowledge gaps for investigation.
Bring Physical Biology
Into Your Research
Discover how physical measurement, AI-supported analysis,
and predictive modeling can support biomedical research.
