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Research_

David James Lab

Understanding metabolic diseases and how to prevent them

Our pioneering work in the areas of metabolic disease and systems biology seeks to understand how diseases such as Type 2 diabetes take hold and how we can prevent them.

We’re developing novel strategies to understand how multiple parameters like genes, the environment and early life exposure influence long-term health.

By focusing on individual diversity, we’ll be able to implement guided preventive health measures to delay or even prevent disease onset.

We’ll also be able to provide more precise medicines, and in turn, avoid unwanted side effects.

This new way of delivering healthcare will not only transform health, but also the industries that support it including food technology, data science and security and policy.

Our interdisciplinary lab features expertise in cell biology, physiology, biochemistry, and bioinformatics.

We work with a range of model systems including flies, mice, cell models, and humans under both normal and perturbed conditions. We then use systems or cybernetic tools to try and understand the deterministic features of these systems.

Our work explores:

  • Which molecular pathways are triggered by exercise to create enduring health benefits
  • How key metabolic hormones, such as insulin, work and how these pathways become abnormal in disease
  • How genes interact with the environment to present particular metabolic phenotypes.

The answers to these questions promise to transform the way medicine is delivered.

Mining protein phosphorylation networks

One of the great challenges of biology is to understand how organisms respond to food, exercise or disease.

This requires analysis of parameters that closely reflect the dynamic behaviour of the system. These parameters must also reflect the integrated output of all system components including the genome, the transcriptome, the proteome, and its modifications.

We focus on protein phosphorylation because every biological process is regulated by altered protein phosphoryaltion. We’ve developed technology to measure this in cells and tissues both comprehensively and quantitively. 

We’re now using this technology to see how systems respond to changes in exercise, diet or drugs.

By combining this technology with new mathematical approaches, we intend to gain insights into the architecture and behaviour of complex systems. 

Insulin action

Insulin is one of the most important hormones in the body.

Mutations in the insulin action pathway present substantial increases in longevity in many animals. We use technologies such as global analysis of proteins and their modifications in cells and metabolomics to explore these pathways.

The systems we explore in our analysis of insulin action include reproductive efficiency in flies, glucose transport and GLUT4 translocation to the membrane in adipocytes, protein synthesis in mammalian cells, the circadian rhythm and gene transcription.

Our ultimate challenge is to use mathematical skills to integrate these approaches so we can visualise new regulatory features in these complex systems.

Gene-environment Interactions

In the last 100 years, most biological research has ignored the impact of gene-environment interaction on various biological outcomes.

This research overlooks the complexity that exists in biology, which, in turn, impacts on our ability to treat disease.

That’s why we’ve established collaborations with a variety of groups including Greg Neely in flies, Jake Lusis in mice and a range of clinical colleagues in humans.

Through this work we’re exploring how individuals of different genetic backgrounds respond to interventions such as exercise, diet and drugs. We hope to discover the optimal environment for individuals that will extend healthy life.

Research group leader

Professor David E James
Professor David E James FAA
"Interdisciplinary collaboration is the way of the future and it will generate unanticipated outcomes. The Charles Perkins Centre is founded upon these principles."
View David James's profile