Metabolism is one of the most complex systems in biology.

Every cell contains a vast network of interconnected chemical reactions, and scientists have developed sophisticated computational models to help make sense of that complexity.

But those models can often be almost as challenging to interpret as the biology itself.

MACSYS hopes to change that with a new online tool. The MACSYS Metabolic Explorer aims to make metabolic modelling more visual, interactive and accessible.

MACSYS Research Fellow Dr Hilary Hunt is leading the development of the Metabolic Explorer.

“Metabolism is fiendishly complicated. Often, we only have the time and the resources to look at a small part of the huge metabolic network that we know is happening,” Hilary said.

Instead of digging through spreadsheets or lines of code, the Metabolic Explorer allows researchers to explore metabolism through interactive maps that reveal reactions, metabolites and the connections between them.

The idea emerged from discussions within MACSYS, when Chief Investigator and RNA biologist Professor Traude Beilharz realised that the mathematical models being developed in the Centre were not always easily accessible to experimental biologists. She proposed creating an interactive tool that would make those models easier to explore.

“For me, the power of exploratory data analysis lies in being able to have a conversation with complexity,” Traude said.

“I need to be able to move things, change parameters, test an intuition and watch the consequences propagate through the system. That interaction turns a model from something I am shown into something I can think with and use to plan experiments.”

The Metabolic Explorer helps cut through complexity by allowing users to hide inactive reactions, visualise pathway activity and automatically generate easy-to-read maps from model outputs.

Researchers can explore key biological processes including central carbon metabolism, investigate amino acid biosynthesis pathways and visualise predicted metabolic fluxes — the rates at which metabolites move through a metabolic network.

One of the tool’s most powerful features is the ability to compare metabolic states. Researchers can visualise the differences between a control and an experimental condition, revealing how metabolic activity changes across an entire network.

“This is what the model of your control experiment is doing. This is what the model of your actual experiment is doing. I can show them the difference,” Hilary said.

The current version of the Metabolic Explorer uses Yeast9, one of the world’s most comprehensive models of yeast metabolism, as its foundation. However, the platform has been designed so it can support additional metabolic models in the future.

The result is a clearer picture of what is happening inside a cell, helping make computational biology less of a black box.

“It’s really important that these models, and the solutions to them, are interpretable by both the computational scientists and the bench scientists,” Hilary said.

The project highlights one of the strengths of MACSYS: bringing computational and biological researchers together to tackle complex scientific questions.

“The promise of MACSYS is not only that mathematicians can build more sophisticated models, but that those models can become accessible thought partners for experimental biologists,” Traude said.

“Interactive exploration allows decades of biological intuition to meet mathematical structure, revealing hidden assumptions, generating better questions and making complex system behaviour visible in ways that static figures and equations cannot.”

The Metabolic Explorer is still a work in progress. Hilary recently demonstrated the platform at the Yeast Products and Discovery Conference in Brisbane, gathering feedback from researchers and potential users.

MACSYS is now inviting other researchers to explore the platform and share their suggestions for future development.

Ultimately, the goal is simple: to make sophisticated metabolic modelling accessible to a much broader research community.