AI-Powered Predictive Systems Biology

Focus on decoding the causal rules of life by interpretable AI models from multi-omic perturbation data to predict how complex tissues respond to disease and therapy.

We live in an era of exploding biological data.

We can now sequence genomes, profile single cells, and map entire tissues with extraordinary precision—giving us a detailed “parts list” of life itself.

But here’s the challenge: seeing the parts is not the same as understanding the system. Despite all this data, biology still lacks the ability to predict how complex systems behave. We know the components, but the blueprint remains hidden.

The next frontier in biology is clear: 👉 move from static maps to dynamic models that can learn, forecast, and explain life’s processes. This means shifting from correlation to causality, description to prediction.

Explore how Johnson’s research program is tackling this challenge— while also sharing insights into the cutting-edge biotechnologies shaping tomorrow’s breakthroughs.

Qingzhou Zhang

A computational systems biologist

About

I am a computational systems biologist dedicated to building predictive models of life. I believe that the next era of biological discovery and therapeutic design will be driven by our ability to learn the causal rules that govern complex cellular systems.

My scientific blueprint is to build interpretable AI models from a synthesis of multi-modal data, from single-cell genomics to spatial proteomics. By focusing on perturbation-response experiments, my work moves beyond correlation to infer the causal logic of how cellular networks operate and adapt.

Interests

Predictive Systems Biology
Causal Network Inference
Cancer Therapeutic Resistance
Tissue Homeostasis & Repair
Interpretable Machine Learning

Education

PhD Biochemistry, 2016-2021

University of Regina, Canada
MSc Bioinformatics, 2009-2010

Univeristy of Manchester, England
BSc Biotechnology, 2004-2008

Nanjing Agricultural University, P.R. China

Decoding Therapeutic Resistance

A primary research thrust focused on understanding how cancer cell networks rewire to survive therapy.

The approach uses predictive models, trained on multi-omic perturbation data, to identify the network states that drive resistance and to nominate effective combination therapies.

Modeling Tissue Resilience and Failure

A research pillar dedicated to modeling the tipping points between tissue repair and chronic disease.

By inferring causal communication networks from spatial and single-cell data, this work aims to identify the key decision points that can be targeted to promote regeneration.

Discovering Universal Design Principles

A foundational research direction seeking to identify universal principles in biology.

The program analyzes conserved network motifs and decision-making logic across diverse systems, from development to immunity, to uncover the fundamental grammar of multicellular life.

A Digital Twin Framework

The ultimate objective is to architect a ‘digital twin’ framework for biological tissues.

This involves creating high-fidelity, predictive simulations that can accelerate therapeutic discovery in silico, transforming the path from fundamental insight to clinical impact.