Research Streams | QuantOmics

Stream 1: Quantum Probe Design & Fabrication

Wed, 01 Jan 2025 00:00:00 +0000

Overview

Stream 1 is the hardware engine of the QuantOmics pipeline. Trainees in this stream design and fabricate next-generation quantum biosensor probes that achieve detection sensitivity at the attomolar level — capturing the decisive molecular events that trigger biological cascades long before conventional sensors can detect them.

The core insight: biological systems exhibit immense signal amplification, where just a few molecules can trigger a cascade of events. Stream 1 builds the measurement tools that can capture these initial molecular events. This enables the entire downstream pipeline — genomic analysis and AI-guided therapeutic design — to operate on signals of unprecedented clarity.

Research Focus Areas

Nitrogen-Vacancy (NV) Center Diamond Probes

NV centers in diamond are atomic-scale quantum sensors with extraordinary sensitivity to magnetic fields, temperature, and single molecules. Trainees work on:

Fabrication and functionalization of NV-center diamond nanoparticles for biological labeling
Optically detected magnetic resonance (ODMR) signal readout
Integration with microfluidic delivery systems for single-cell sensing

Quantum Dot Synthesis & Photonic Sensing

Semiconductor quantum dots offer tunable optical properties for highly sensitive biosensing. Research projects include:

Synthesis of biocompatible quantum dots (CdSe, InP, carbon-based) for specific molecular targeting
Photonic crystal resonator integration for signal amplification
Multiplexed detection platforms for simultaneous multi-analyte sensing

CMOS/MEMS Integrated Sensor Systems

Miniaturized, integrated sensor platforms enable practical, deployable biosensors. Trainees engage in:

Design of CMOS read-out circuitry for quantum sensor arrays
MEMS-based microfluidic integration for sample handling
Implantable and injectable wireless biosensor networks for real-time monitoring
Energy-efficient signal conditioning electronics

Spin-Based Magnetometry

Ultra-sensitive detection of magnetic signatures from biological processes:

Spin-based detection of neurotransmitters and cellular signaling molecules
Quantum-enhanced noise-limited detection strategies
Integration with organ-on-a-chip platforms for in vitro validation

Validation Platform

A cornerstone of Stream 1 methodology is the use of patient-derived organoid and organ-on-a-chip platforms. These physiologically relevant 3D microenvironments mimic human tissue far more accurately than traditional 2D cell cultures. Trainees validate their quantum sensors against these biological systems — in partnership with Stream 2 biologists — to confirm performance in contexts that predict human clinical response.

Partner organizations C2MI and Epiloid Biotech provide access to quantum fabrication infrastructure and organoid setups that are otherwise inaccessible to most Canadian universities.

Example Trainee Projects

NV-center nanosensor for attomolar cytokine detection — fabricating and characterizing a diamond NV probe functionalized for IL-6 detection in organoid supernatant
Quantum dot multiplexed assay for cancer biomarkers — designing a photonic chip that simultaneously detects three circulating tumor DNA fragments
Integrated CMOS biosensor for real-time cardiotoxicity screening — miniaturized sensor array for monitoring cardiomyocyte electrical activity in organ-on-chip models
Wireless implantable quantum magnetometer — injectable sensor for continuous in vivo monitoring of immune cell activity

Stream 1 Co-Leads

Dr. Virgilio Valente (TMU) — CMOS/MEMS integrated sensors, wireless biosensor networks
Dr. Shayan Rayan (USask) — Quantum nanotechnology, nanopore integration
Dr. Harry Ruda (UofT) — Photonic sensors, semiconductor nanomaterials (Stanley Meek Chair in Advanced Nanotechnology)
Dr. Sara Mahshid (McGill) — Microfluidics, lab-on-chip biosensing
Dr. Stefania Impellizzeri (TMU) — Quantum dot synthesis, nanomaterial chemistry (Jet Ice Research Chair)

Courses Supporting Stream 1

Course 1.1 — Biosensor Engineering for Precision Health
Course 1.2 — Quantum Nanotechnology for Life Sciences
Bootcamp 1.4 — Multimodal-Omics Data Integration (connecting sensor output to genomic pipelines)

Stream 2: Genomics Signal Integration

Wed, 01 Jan 2025 00:00:00 +0000

Overview

Stream 2 is the data backbone of the QuantOmics pipeline. Trainees in this stream tackle the grand challenge of converting noisy, high-dimensional quantum sensor data into high-fidelity, analytically tractable multi-omic datasets. Without Stream 2, the hardware signals from Stream 1 cannot be interpreted, and the AI models of Stream 3 have no validated data to learn from.

The stream bridges two worlds: the messy, real-world signals from quantum probes operating in complex biological matrices, and the clean, structured representations required by state-of-the-art computational genomics and AI methods.

Research Focus Areas

Whole-Genome Sequencing & Variant Analysis

Long- and short-read sequencing data generated by quantum-enhanced nanopore platforms requires sophisticated analysis. Trainees work on:

Applying comprehensive whole-genome sequencing (WGS) analyses to brain organoids to identify therapeutic targets for neurodevelopmental disorders such as autism
Developing improved variant calling algorithms that account for unique noise characteristics of quantum-coupled sequencing
Building tools for structural variant detection in complex genomic regions

Multi-Omic Data Fusion

Integrating data across molecular scales — from DNA to protein to cellular phenotype. Key projects include:

Fusing electrical and optical sensor data streams with nanopore sequencing reads and methylation maps
Building end-to-end Snakemake/Nextflow pipelines for reproducible multi-omic analysis
Developing early vs. late fusion strategies for genomic, transcriptomic, proteomic, and EHR data
Applying representation learning to extract shared latent features across data modalities

Computational Tools for Sensor-Derived Genomic Data

Novel sensors generate novel data formats. Stream 2 trainees develop new bioinformatics tools:

Signal processing algorithms for denoising raw quantum sensor output before genomic analysis
Probabilistic models for base-calling from quantum-coupled nanopore reads
Quality control frameworks tailored to attomolar-sensitivity assay data

Epigenomics & Methylation Analysis

DNA methylation patterns hold rich information about cell state and disease. Trainees build:

Pipelines for integrating methylation data with quantum sensor readout from epigenetic biosensors
Differential methylation analysis in disease-relevant organoid models
Tools for cross-referencing methylation signatures with EHR phenotype data

Validation Against Real Disease Models

Stream 2 research is validated against clinically relevant biological systems, primarily using patient-derived organoids developed in collaboration with Stream 1. Key application areas:

Neurodevelopmental disorders — applying WGS to brain organoids to identify new therapeutic targets for autism, building on existing team expertise in the genomic architecture of these conditions
Cancer — integrating multi-omic data to characterize tumor heterogeneity and identify drug-resistant cell populations in organoid cultures
Cardiotoxicity — developing computational pipelines for analyzing multi-omic data from cardiomyocyte organ-on-chip models under drug perturbation

Example Trainee Projects

Whole-genome sequencing of autism brain organoids — identifying novel de novo variants and gene regulatory disruptions associated with autism spectrum disorder
Snakemake pipeline for multi-omic fusion — building a reproducible, containerized pipeline integrating nanopore sequencing, ATAC-seq, and EHR metadata
Methylation-guided biomarker discovery — developing an algorithm that identifies disease-specific methylation patterns detectable by quantum epigenetic biosensors
Cross-ancestry variant classification — building genomic models that integrate population-diverse genetic marker data to reduce bias in variant pathogenicity prediction

Equity & Diversity in Genomic Data

A critical challenge in Stream 2 is the profound lack of diversity in genomic reference databases, which are overwhelmingly of European origin. Trainees in this stream are explicitly trained to:

Integrate data representing comprehensive genetic markers from diverse populations
Understand how racial bias in genomic AI can lead to misclassification for underrepresented groups
Apply ethical frameworks for Indigenous data sovereignty and community consent in genomic research
Validate tools across diverse cell lines to ensure equitable performance

Stream 2 Co-Leads

Dr. Brett Trost (UofT / SickKids) — Computational genomics, multi-omic pipeline development
Dr. Jacques Corbeil (U Laval / MILA) — Medical genomics, AI-driven data integration, organ-on-chip resources
Dr. Brenda Andrews (UofT) — Functional genomics, systems biology, gene networks (Tier 1 CRC)

Courses Supporting Stream 2

Course 1.3 — AI in Genomics
Bootcamp 1.4 — Multimodal-Omics Data Integration (the core course for this stream)
Course 1.5 — Responsible Innovation & EDI in Precision Health (Indigenous data sovereignty, bias in genomic AI)

Stream 3: AI-Powered Therapeutic Design

Wed, 01 Jan 2025 00:00:00 +0000

Overview

Stream 3 closes the QuantOmics pipeline. Trainees here build the predictive AI models that translate the high-fidelity multi-omic datasets generated by Streams 1 and 2 into actionable therapeutic insights — identifying immunogenic targets, predicting treatment response, and guiding the rational design of personalized vaccines and precision therapeutics.

This stream operates at the intersection of machine learning, structural biology, and clinical translation. It is where the quantum-to-clinic vision of QuantOmics is most directly realized.

Research Focus Areas

Neoantigen Prediction & Vaccine Design

Personalized cancer vaccines require identifying tumor-specific neoantigens — mutated peptides that the immune system can be trained to recognize. Trainees work on:

Benchmarking and adapting next-generation pathogenicity predictors to identify immunogenic neoantigens for personalized vaccine design
Developing multimodal fusion models that integrate genomic variant data with structural protein features and HLA binding affinity predictions
Building end-to-end vaccine design pipelines from quantum-enhanced sequencing data to candidate antigen ranking

Generative AI for Drug Discovery

Using generative models to explore vast chemical spaces and identify novel therapeutic candidates:

Applying generative AI models (VAEs, diffusion models) for anomaly detection to identify drug-resistant cells in organoid cultures
Designing latent-space representations of molecular structures conditioned on multi-omic signatures
Generating and evaluating novel small molecule candidates for precision targeting

Multimodal Fusion for Clinical Diagnostics

Combining signals across modalities for robust clinical prediction:

Creating deep learning frameworks for the multimodal fusion of electrical, optical, and genomic sensor data for cardiotoxicity screening
Developing transformer-based models for multimodal representation learning across genomic, imaging, and clinical data
Building uncertainty-aware prediction models for clinical deployment in high-stakes settings

Immune Cell Profiling & Treatment Efficacy

Quantifying the immune response is critical to evaluating therapeutic efficacy:

Using advanced loss functions and segmentation models to precisely identify and quantify immune cell infiltration in microscopy images
Providing a direct, AI-based measure of therapeutic efficacy in organoid and in vitro models
Developing self-supervised AI models to automatically classify cellular phenotypes from high-content imaging data, enabling large-scale unbiased analysis

Trustworthy & Responsible Clinical AI

AI deployed in clinical settings must be fair, interpretable, and robust:

Developing explainability frameworks (SHAP, integrated gradients, concept-based explanations) for genomic AI models
Quantifying and mitigating performance disparities across patient demographic groups
Building validation frameworks for quantum-AI systems navigating Health Canada regulatory pathways

Example Trainee Projects

Personalized neoantigen vaccine pipeline — an end-to-end system from quantum-enhanced WGS of tumor organoids to a ranked list of candidate immunogenic peptides
Generative molecular design for drug resistance — applying a diffusion model to identify novel inhibitor candidates for drug-resistant cancer cells detected in organoid assays
Multimodal cardiotoxicity classifier — a fusion model combining quantum sensor electrical signals, microscopy images, and gene expression data to predict drug-induced cardiac toxicity
Immune infiltration quantification tool — a deep learning segmentation system deployed on multiplexed fluorescence images of treated organoids to measure therapeutic response
Bias audit framework for genomic AI — a systematic evaluation tool that measures and corrects performance disparities of genomic AI models across ancestrally diverse patient populations

The “Personalized Vaccine Design Challenge”

Stream 3 forms the intellectual core of QuantOmics’ signature annual Personalized Vaccine Design Challenge — a biennial team hackathon where interdisciplinary trainee teams tackle the complete sensor-to-vaccine pipeline, culminating in a presentation to a panel of academic, clinical, and industry judges. This challenge uniquely forces integration across all three streams.

Equity & Responsible AI in Therapeutic Design

QuantOmics explicitly trains Stream 3 trainees to address AI ethics in clinical applications. Key training elements include:

Course 1.5 — Responsible Innovation & EDI in Precision Health: regulatory pathways, inclusive AI design, Indigenous data sovereignty
Frameworks for ethical AI in oncology and emerging quantum technologies
Proactive identification and correction of demographic biases at the model design stage, not as an afterthought

Stream 3 Co-Leads

Dr. Naimul Khan (TMU) — Multimodal ML, AI in healthcare, biosensing (Program Director)
Dr. Amber Simpson (Queen’s) — Biomedical computing, cancer informatics, AI for clinical data (Tier 2 CRC)

Contributing Faculty:

Dr. Brenda Andrews (UofT) — Systems genetics, functional genomics (Tier 1 CRC)
Dr. Jacques Corbeil (U Laval / MILA) — Genomic AI, multi-omics

Courses Supporting Stream 3

Course 1.3 — AI in Genomics (primary course for this stream)
Bootcamp 1.4 — Multimodal-Omics Data Integration
Course 1.5 — Responsible Innovation & EDI in Precision Health