Our research falls broadly into four areas:
- The Virtual Heart Cell
- Energy-Based Modelling for Systems and Synthetic Biology
- Modelling Bio-Nano Interactions for Nanomedicine
- Genetic and Epigenetic Network Inference in Cancer
Within each of these broad areas we are pursuing several different projects, and developing common mathematical approaches and computational tools.
The Virtual Heart Cell
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.
D. Ladd, A. Tilunaite, H.L. Roderick, C. Soeller, E.J. Crampin, V. Rajagopal (2019). Assessing cardiomyocyte excitation-contraction coupling site detection from live cell imaging using a structurally-realistic computational model of calcium release. Frontiers in Physiology 10:1263
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
S. Ghosh. K. Tran, L. Delbridge, A. Hickey, E. Hanssen, E.J. Crampin, V. Rajagopal (2018). Insights on the impact of mitochondrial organisation on bioenergetics in high-resolution computational models of cardiac cell architecture. PLoS Computational Biology 14(12): e1006640
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
Energy-Based Approach to 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.
P.J. Gawthrop, P. Cudmore, E.J. Crampin (2020). Physically-Plausible Modelling of Biomolecular Systems: A Simplified, Energy-Based Model of the Mitochondrial Electron Transport Chain. Journal of Theoretical Biology
M. Pan, P.J. Gawthrop, J. Cursons, K. Tran, E.J. Crampin (2019). A thermodynamic framework for modelling membrane transporters. Journal of Theoretical Biology 481, 10-23
P.J. Gawthrop, E.J. Crampin (2017). Energy-based Analysis of Biomolecular Pathways. Proceedings of the Royal Society A 473:20160825
P.J. Gawthrop, E.J. Crampin (2016). Modular Bond Graph Modelling and Analysis of Biomolecular Systems. IET Systems Biology 10 (5) 187-201
Modelling Bio-Nano Interactions for Nanomedicine
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.
M. Faria, K.F. Noi, Q. Dai, M. Björnmalm, S.T. Johnston, K. Kempe, F. Caruso, E.J. Crampin (2019). Revisiting cell–particle association in vitro: A quantitative method to compare particle performance. Journal of Controlled Release 307, 355-367
A.C.G. Weiss, H.G. Kelly, M. Faria, Q.A. Besford, A.K. Wheatley, C.-S. Ang, E.J. Crampin, F. Caruso, S.J. Kent (2019). Link between Low-Fouling and Stealth ‒ A Whole Blood Biomolecular Corona and Cellular Association Analysis on Nanoengineered Particles. ACS Nano 13 (5), 4980–4991
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
M. Faria, M. Björnmalm, K.J. Thurecht, S.J. Kent, R.G. Parton, M. Kavallaris, A.P.R. Johnston, J.J. Gooding, S.R. Corrie, B.J. Boyd, P. Thordarson, A.K. Whittaker, M.M. Stevens, C.A. Prestidge, C.J.H. Porter, W.J. Parak, T.P. Davis, E.J. Crampin*, F. Caruso* (2018)
Minimum Information Reporting in Bio–Nano Experimental Literature
Nature Nanotechnology 13, 777–785
- Supplementary Information
- MIRIBEL website
- Nature Nanotechnology editorial about MIRIBEL
- Responses from the community: Nature Nanotechnology 14, 625 (2019)
- Our response to community commentaries: Nature Nanotechnology 15, 2-3 (2020)
Genetic and Epigenetic 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. Lin, A. Kan, J. Gao, E.J. Crampin, P.D. Hodgkin, S.H. Naik (2018). DiSNE Movie Visualization and Assessment of Clonal Kinetics Reveals Multiple Trajectories of Dendritic Cell Development. Cell Reports 22 (10), 2557–2566
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