Welcome to the Systems Biology Laboratory at the University of Melbourne.

At the Systems Biology Lab we build and analyse mathematical models of biological processes, pathways and networks, and the cellular geometries within which these processes take place. We apply these models to problems in human health and physiology, including heart disease, cancer, nanomedicine and synthetic biology.

We are based in the School of Mathematics and Statistics and in the Department of Biomedical Engineering at the University of Melbourne.

We are also part of the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology.

For more information contact Lab Director Professor Edmund Crampin

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Reference environments: A universal tool for reproducibility in computational biology

Reproducibility of scientific results, or lack thereof, has received increasing attention over recent years. Computational studies, by their nature, should be amongst the most reproducible. However it often proves to be a challenge to reproduce computational results, even when code is made available. The need to adopt standards for reproducibility of claims made based on computational results is now clear to researchers, however there is still a great deal of debate about where responsibility for checking reproducibility lies, and about appropriate tools and approaches to ensure reproducibility of a computational result.

Many technologies exist to support and promote reproduction of computational results: containerisation tools like Docker, literate programming approaches such as Sweave, knitr, iPython or cloud environments like Amazon Web Services. But these technologies are tied to specific programming languages (e.g. Sweave/knitr to R; iPython to Python) or to platforms (e.g. Docker for 64-bit Linux environments only). To date, no single approach is able to span the broad range of technologies and platforms represented in computational biology and biotechnology.

In our recent preprint “Reference environments: A universal tool for reproducibility in computational biology”, now available on arXiv, we demonstrate an approach and provide a set of tools that is suitable for all computational work and is not tied to a particular programming language or platform. We illustrate this approach, which we call ‘Reference Environments’, using examples from a number of published papers in different areas of computational biology, spanning the major languages and technologies in the field (Python/R/MATLAB/Fortran/C/Java).


The Reference Environments approach provides a transparent and flexible process for replication and recomputation of results. Ultimately, the most valuable aspect of this approach is the decoupling of methods in computational biology from their implementation. Separating the ‘how’ (method) of a publication from the ‘where’ (implementation) promotes genuinely open science and benefits the scientific community as a whole.

Read it here:

Daniel G. Hurley, Joseph Cursons, Matthew Faria, David M. Budden, Vijay Rajagopal, Edmund J. Crampin
Reference environments: A universal tool for reproducibility in computational biology

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A thermodynamic framework for modelling membrane transporters – published in Journal of Theoretical Biology

Membrane transporters are proteins which facilitate the entry and exit of molecules into cells. Transport processes often require a source of energy in order to move substances against unfavourable concentration gradients or, in the case of charged species, against electrochemical gradients. This places thermodynamic constraints on the function of transporter proteins.

In our new paper, published in the Journal of Theoretical Biology, we outline an energy-based modelling framework, using the bond graph approach, with which to model and understand transporters. We apply this modelling approach to several key transporters that occur in heart cells (the sodium pump, and the calcium transporter SERCA).


This work has significance for all cell models which involve transport process, as the vast majority of mathematical models of transporter proteins in the scientific literature are not thermodynamically consistent, and may therefore give misleading results.

The paper is available here:

M. Pan, P.J. Gawthrop, J. Cursons, K. Tran, E.J. Crampin (2018)
A thermodynamic framework for modelling membrane transporters
Journal of Theoretical Biology

Congratulations to Michael and coauthors on this work.

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Minimum information reporting in bio-nano experimental literature – published in Nature Nanotechnology

Our new paper setting out minimum information criteria for bio-nano research has been published in Nature 9Nanotechnology.

Nature Nanotechnology 13, 777–785 (2018)

Download the paper here.

Read the Nature Nanotechnology editorial about our work here.

This work, led by Matt Faria, has been a project of the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology.


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Our paper on size dependence of nanoparticle transport – published in Interface

Our new paper on quantifying the influence of the distribution of nanoparticle size (‘polydispersity’) on delivered cellular dose has just appeared in Journal of the Royal Society Interface.

S.T. Johnston, M. Faria, E.J. Crampin (2018)
An analytical approach for quantifying the influence of nanoparticle polydispersity on cellular delivered dose.
J. R. Soc. Interface 15: 20180364

See it here: http://dx.doi.org/10.1098/rsif.2018.0364

Congratulations Stuart and Matt.

This work was funded through the Australian Research Council Centre of Excellence in Convergent Bio-Nano Science and Technology @ARCCoEBionano

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Cell Systems paper on combinatorial miRNA regulation of EMT – now published

Screen Shot 2018-07-12 at 10.58.50 amJoe’s paper on combinatorial targeting by miRNAs in regulating phenotype in breast cancer cells has appeared in Cell Systems.

Micro-RNAs are known to play important roles in driving switching between epithelial and mesenchymal phenotypes in cancer. In the paper we identify co-regulated miRNAs in breast cancer cells with functionally related protein targets, and show that cooperative interaction between these targets leads to highly coordinated regulation of epithelial-mesenchymal transition.

J. Cursons, K.A. Pillman, K.G. Scheer, P.A. Gregory, M. Foroutan, S. Hediyeh-Zadeh, J. Toubia, E.J. Crampin, G.J. Goodall, C.P. Bracken, M.J. Davis (2018)
Combinatorial Targeting by MicroRNAs Co-ordinates Post-transcriptional Control of EMT
Cell Systems 7, 77–91

See it here: https://doi.org/10.1016/j.cels.2018.05.019

Congratulations Joe and all of the authors.

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Are you coming to SMB2018 in Sydney?

Come and hear about our latest research – all on Monday!

Hilary Hunt’s talk on cardiac hypertrophy signalling is at 1130am; Claire Miller’s talk on multicellular modelling of the epidermis is at 4pm; and Stuart Johnston’s talk on nanoparticle delivery follows shortly after at 420pm.

Then from 6pm onwards, we have Michael Pan’s poster on bond graph modelling of the cardiac action potential, Daniel Hurley’s poster on reproducibility in computational biology, and Agne Tilunaite’s poster on modelling intracellular calcium dynamics.

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Bond graph modelling of the cardiac action potential – published in Proceedings of the Royal Society A

Our paper on modelling the cardiac action potential using an energy-based bond graph approach has now appeared in Proc R Soc Lond A. Mathematical models of cardiac action potentials have become increasingly important in the study of heart disease and pharmacology, but concerns linger over their robustness during long periods of simulation, in particular due to issues such as model drift and non-unique steady states. In this paper we develop a general and systematic method of identifying hidden conservation laws that are responsible for these undesirable characteristics in models of cardiac electrophysiology.

M. Pan, P.J. Gawthrop, K. Tran, J. Cursons, E.J. Crampin (2018)
Bond graph modelling of the cardiac action potential: implications for drift and non-unique steady states
Proceedings of the Royal Society A 474: 20180106

Congratulations to Michael and coauthors!

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