This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Cardiac modelling has been conducted for over 45 years, starting with the first experimentally based electrophysiological model of a cardiac myocyte by Oxford Emeritus Prof. Denis Noble, CBE (Noble, 1960). With the availability, in the early 1990s, of improved experimental techniques, including the recording of membrane currents, single-channel gating properties and intracellular ion concentrations, cellular models have grown in complexity and are increasingly predictive. Knowledge of cell activity is not sufficient, however, to study complex patterns of electrical conduction within a complex organ such as the heart. With the advent, in the early 1990s, of civil-use supercomputers, it became possible to develop and use cardiac tissue models. These models have illustrated the tremendous importance of structural detail for functional prediction and have greatly helped in shaping our understanding of processes underlying cellular excitation, repolarisation, and contraction, and are increasingly becoming an integrated part of experimental research, helping in hypothesis formation, analysis, and prediction (Kohl et al., 2000). In this context, mathematical models have begun to make significant contributions to the refinement of experimental work, reduction in severity of interventions, and partial replacement of 'wet'biological research (Garny &Kohl, 2004). These models have further highlighted the need to account for the multi-scale nature ?both in space and time ?of cardiac function. Relevant spatial scales range from nano (sub-cellular) to micro (cellular) and macro (organ) levels. The above development has benefited from increasingly accurate data in the 'micro-to-macro'domain However, the 'nano-to-micro'level has thus far largely been neglected. Yet in order to understand sub-cellular mechanisms it is imperative to address compartmentalisation of cardiac cells which underlies integrated behaviour, from signalling to ion handling and contraction. To this end, the challenge is to acquire an accurate representation of the cyto-anatomical structure of individual cardiac myocytes. ET is ideal for structures whose dimensions vary significantly within a small volume. It allows for computer-generation of 'virtual slices'that are much thinner than could be cut physically. ET is ideal, therefore, for structures with a complex 3D geometry, such as cytoskeletal arrays or convoluted membrane systems of the T-tubules, sarcoplasmic reticulum and micro-tubules in ventricular myocytes. With this approach, it is possible to begin reconstruction of individual cardiac cells, to model their structure for the simulation of cardiomyocyte activity in a way that goes beyond treating cells as a 'point source'of electrical activity, or a uniform 'building block'of the mechanical machinery. References: Garny A &Kohl P. Cardiac Research at the Interface of Engineering and Computing. The Chemical Engineer September: 31-32 (2004). Kohl P, Noble D, Winslow RL &Hunter PJ. Computational Modelling of Biological Systems: Tools and Visions. PhilTrans R Soc A 358: 579-610 (2000). Noble D. Cardiac Action and Pacemaker Potentials Based on the Hodgkin-Huxley Equations. Nature 188: 495-497 (1960).
| Giddings Jr, Thomas H; Morphew, Mary K; McIntosh, J Richard (2017) Preparing Fission Yeast for Electron Microscopy. Cold Spring Harb Protoc 2017: |
| Zhao, Xiaowei; Schwartz, Cindi L; Pierson, Jason et al. (2017) Three-Dimensional Structure of the Ultraoligotrophic Marine Bacterium ""Candidatus Pelagibacter ubique"". Appl Environ Microbiol 83: |
| Brown, Joanna R; Schwartz, Cindi L; Heumann, John M et al. (2016) A detailed look at the cytoskeletal architecture of the Giardia lamblia ventral disc. J Struct Biol 194:38-48 |
| Saheki, Yasunori; Bian, Xin; Schauder, Curtis M et al. (2016) Control of plasma membrane lipid homeostasis by the extended synaptotagmins. Nat Cell Biol 18:504-15 |
| Höög, Johanna L; Lacomble, Sylvain; Bouchet-Marquis, Cedric et al. (2016) 3D Architecture of the Trypanosoma brucei Flagella Connector, a Mobile Transmembrane Junction. PLoS Negl Trop Dis 10:e0004312 |
| Park, J Genevieve; Palmer, Amy E (2015) Properties and use of genetically encoded FRET sensors for cytosolic and organellar Ca2+ measurements. Cold Spring Harb Protoc 2015:pdb.top066043 |
| McCoy, Kelsey M; Tubman, Emily S; Claas, Allison et al. (2015) Physical limits on kinesin-5-mediated chromosome congression in the smallest mitotic spindles. Mol Biol Cell 26:3999-4014 |
| Höög, Johanna L; Lötvall, Jan (2015) Diversity of extracellular vesicles in human ejaculates revealed by cryo-electron microscopy. J Extracell Vesicles 4:28680 |
| Marc, Robert E; Anderson, James R; Jones, Bryan W et al. (2014) The AII amacrine cell connectome: a dense network hub. Front Neural Circuits 8:104 |
| Weber, Britta; Tranfield, Erin M; Höög, Johanna L et al. (2014) Automated stitching of microtubule centerlines across serial electron tomograms. PLoS One 9:e113222 |
Showing the most recent 10 out of 84 publications
