At the Systems Biology Laboratory, University of Melbourne, 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 cancer and heart disease.
Our research falls broadly into four areas of systems biology:
- Modelling Heart Cells
- Energy-Based Modelling Approaches
- Biochemical Network Inference
- Modelling Bio-Nano Interactions
Within each of these broad areas we are pursuing several different projects, and developing common mathematical approaches and computational tools.
Heart Cell Systems Biology
Cellular function is determined by the complex network of interacting biological processes occurring within and between cells. Integrative modelling provides a means of assessing the quantitative contribution of each of these components, and assessing potential therapeutic strategies in disease. With a focus on understanding regulatory mechanisms, we are developing biophysically-based models of a range of cellular processes in relation to heart cell function and heart disease.
It is well established that cellular structure affects its function and that cellular function can in turn trigger structural remodeling. But can we predict the effect of a structural alteration on cellular function? Do we know the mechanism by which cellular function drives structural remodeling? Working with Vijay Rajagopal’s Cellular Structure and Mechanobiology Group, we are addressing these questions by developing a computational modeling framework for simulating cellular systems biology within the 3D spatial structure of the heart cell and its local environment. This framework integrates structural imaging data and quantitative functional data to create realistic simulations of structure-driven function and function-driven structural remodeling.
K. Tran, J.-C. Han, E.J. Crampin, A.J. Taberner, D.S. Loiselle (2017). Experimental and modelling evidence of shortening heat in cardiac muscle. Journal of Physiology 595, 6313–6326
I. Siekmann, M. Fackrell, E.J. Crampin, P. Taylor (2016). Modelling modal gating of ion channels with hierarchical Markov models. Proceedings of the Royal Society A 472: 20160122
K. Tran, J.-C. Han, A. Taberner, C. Barrett, E.J. Crampin, and D. Loiselle (2016). Myocardial energetics is not compromised during compensated hypertrophy in the Dahl salt-sensitive rat model of hypertension. American Journal of Physiology – Heart and Circulatory Physiology 311 (3) H563-H571
M.L. Neal, B.E. Carlson, C.T. Thompson, R.C. James, K.G. Kim, K. Tran, E.J. Crampin, D.L. Cook, J.H. Gennari (2015). Semantics-Based Composition of Integrated Cardiomyocyte Models Motivated by Real-World Use Cases. PLoS ONE 10(12): e0145621
Energy-Based Modelling in Systems and Synthetic Biology
Energy is fundamental to all life. In systems biology, models typically consider biochemical reaction rates, and hence fluxes of different biochemical species. However energy is almost universally ignored. In our view this significantly restricts the types of questions that models can be used to address, and limits the applicability of systems biology models in design for synthetic biology and biotechnological applications. We are developing energy-based models of biological systems based on multi-domain engineering concepts, which use the bond graph approach to represent both mass and energy flows.
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
P.J. Gawthrop, E.J. Crampin (2017). Energy-based Analysis of Biomolecular Pathways. Proceedings of the Royal Society A 473:20160825
P.J. Gawthrop, I. Siekmann, T. Kameneva, S. Saha, M.R. Ibbotson, E.J. Crampin (2017). Bond Graph Modelling of Chemoelectrical Energy Transduction. IET Systems Biology 11 (5) 127–138
P.J. Gawthrop, E.J. Crampin (2016). Modular Bond Graph Modelling and Analysis of Biomolecular Systems. IET Systems Biology 10 (5) 187-201
Biochemical Reaction Network Inference and Analysis
Despite major advances in molecular biology and genetics, our understanding of the molecular networks that control cell function, and the way in which dysregulation of these networks promotes diseases, including cancer, remains far from complete. Developing avenues for new therapies and diagnostic tests relies on our comprehension of the molecular mechanisms regulating the progress of disease. A Systems Biology approach to studying disease progression is to construct network models from high-quality, high-throughput molecular data, and to interrogate these networks through computational analysis.
We are combining novel methods developed at the Systems Biology Laboratory with other techniques for network-based analysis, and applying this approach to datasets in a variety of cancers and tumour types to uncover potential targets for further investigation. We are also integrating clinical information such as patient history, survival, tumor grade and age, with molecular data to improve the predictive power and clinical applicability of this approach.
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
D.M. Budden, E.J. Crampin (2016). Distributed gene expression modelling for exploring variability in epigenetic function. BMC Bioinformatics 17:446
D.M. Budden, E.J. Crampin (2016). Information theoretic inference of biological networks from continuous-valued data. BMC Systems Biology 10:89
Modelling Bio-Nano Interactions
Understanding how nano-scale materials and cells interact will be key to the future development of improved nanomedicines and vaccines. At the ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, we are leading the ‘Modelling of Bio-Nano Interactions’ theme, where our aim is to understand the rules by which cells interact with nanoengineered particle systems with tailored physical properties. The long term aim is to develop models with which we can design nanoparticles with predictable cellular interactions.
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
J.J. Glass, L. Chen, S. Alcantara, B.E. Rolfe, E.J. Crampin, K.J. Thurecht, R. De Rose, S.J. Kent (2017). Charge has a marked influence on hyperbranched polymer nanoparticle uptake in whole human blood. ACS Macro Letters 6 (6) 586–592
J. Cui, M. Faria, M. Björnmalm, Y. Ju, T. Suma, S.T. Gunawan, J.J. Richardson, S. Bals, E.J. Crampin, F. Caruso (2016). A framework to account for sedimentation and diffusion in particle-cell interactions. Langmuir 32 (47), 12394-12402