Interactive computer simulation analysis is a powerful tool that can aid biomedical investigators in understanding biological systems, designing effective experiments, and extracting the maximum amount of information from these experiments. Effective use of simulation analysis requires a simulation interface that aids investigators in their use of the mathematical models that underlie the simulation. The interface should be easy to use, should provide ready access to the tools required for complete analysis (e.g. parameter optimizers to fit the model output to experimental data), and should provide complete graphical and tabular reports of the result of the analysis. For several years we have been using an interface named SIMCON that we have developed and have continued to improve. The main strength of SIMCON has been its flexibility and, thus, the ability to apply it to a wide variety of problems, but this also leads to its main weakness. Because it is so flexible, it is also somewhat difficult for new users to use effectively. Also, its graphical output is limited to simple x,y line graphs. This project focuses on three areas of simulation interface development. First is the continued development of SIMCON. Primary areas of development are the improvement of the graphical and parameter optimization capabilities. An X-windows graphical processor will be added that will include contour plots, perspective plots, and special graphics objects that are tailored to specific analyses (e.g. an axially distributed, multiple region, blood-tissue exchange unit for use in display of indicator dilution results). Regarding parameter optimization, SIMCON is currently constrained to modification of four parameters to fit one simulation output to a single experimental curve. The number of parameters and number of curves fitted simultaneously will be increased. The second area of development is the method of communication between the simulation interface and the model. Currently, the interface and model are linked together in a single executable module. This has several disadvantages including ineffective use of disk storage and processor utilization. A new communication mechanism will be developed in which the interface and model run as separate tasks that communicate using standard network protocols. The third, and main, area of development is a new graphical simulation interface, XSIM, that runs under X-windows and provides a model interface that is tailored to the requirement of the specific analysis task at hand. XSIM will provide a true point-and-click interface that is in the style to which X-windows, Macintosh, and PC Windows users have become accustomed. XSIM will incorporate the X-windows graphics processor and IPC that are discussed above.
| Bassingthwaighte, James B; Butterworth, Erik; Jardine, Bartholomew et al. (2012) Compartmental modeling in the analysis of biological systems. Methods Mol Biol 929:391-438 |
| Dash, Ranjan K; Bassingthwaighte, James B (2010) Erratum to: Blood HbO2 and HbCO2 dissociation curves at varied O2, CO2, pH, 2,3-DPG and temperature levels. Ann Biomed Eng 38:1683-701 |
| Bassingthwaighte, James B; Raymond, Gary M; Butterworth, Erik et al. (2010) Multiscale modeling of metabolism, flows, and exchanges in heterogeneous organs. Ann N Y Acad Sci 1188:111-20 |
| Dash, Ranjan K; Bassingthwaighte, James B (2006) Simultaneous blood-tissue exchange of oxygen, carbon dioxide, bicarbonate, and hydrogen ion. Ann Biomed Eng 34:1129-48 |
| Dash, Ranjan K; Bassingthwaighte, James B (2004) Blood HbO2 and HbCO2 dissociation curves at varied O2, CO2, pH, 2,3-DPG and temperature levels. Ann Biomed Eng 32:1676-93 |
| Kellen, Michael R; Bassingthwaighte, James B (2003) Transient transcapillary exchange of water driven by osmotic forces in the heart. Am J Physiol Heart Circ Physiol 285:H1317-31 |
| Kellen, Michael R; Bassingthwaighte, James B (2003) An integrative model of coupled water and solute exchange in the heart. Am J Physiol Heart Circ Physiol 285:H1303-16 |
| Wang, C Y; Bassingthwaighte, J B (2001) Capillary supply regions. Math Biosci 173:103-14 |
| Swanson, K R; True, L D; Lin, D W et al. (2001) A quantitative model for the dynamics of serum prostate-specific antigen as a marker for cancerous growth: an explanation for a medical anomaly. Am J Pathol 158:2195-9 |
| Swanson, K R; Alvord Jr, E C; Murray, J D (2000) A quantitative model for differential motility of gliomas in grey and white matter. Cell Prolif 33:317-29 |
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