This software development project combines the perspectives and software from three experienced groups to create and maintain what will be the most advanced and complete software package available for systems biology and the analysis of physiological data for research and clinical practice. The project brings together: (1) The JSim system for model based analysis using single or multi-scale models using ordinary and 1- dimensional partial differential equations for transport and metabolism (Butterworth and Bassingthwaighte, University of Washington), (2) the System Biology Workbench and related software coding packages invented by Herbert Sauro (Unversity of Washington), and (3) thermodynamically constrained and detailed equations for biochemical reactions created automatically from databases on reactions equilibria and kinetics (Daniel Beard, Medical College Wisconsin). The coalescence of these three powerful techniques, all of which utilize archival database markup languages like SBML and CellML, will foster model construction using JSim's inbuilt error identification and correction mechanisms. The toolkits and models will be disseminated worldwide in easily understood code, and will provide exactly and directly reproducible solutions on a wide range of computational platforms including Windows, Macintosh, and Linux. The models will be designed for experiment design and analysis, and the open source code for models and the analysis packages will be freely available to investigators everywhere. This powerful combination is relevant to the betterment of health as it provides investigators in genomics, molecular biology, physiology, and pharmacology with practical mechanisms for predicting and understanding integrated cellular organ and body function. Many of the current generation of biologists are not deeply trained in quantitative analysis of complex systems, so the availability of these tools will greatly facilitate their understanding of the biology and their ability to design more precisely targeted experimentation, more efficient data analysis, and improved therapies.
This research will expedite the translation from basic research in genomics, genetic network regulation, biochemical network behavior, and cell function to clinical applications that provide long range public health benefits. It will occur via better directed experimentation through modeling, and through improved prediction of therapy outcomes.
|Bassingthwaighte, James B; Raymond, Gary M; Dash, Ranjan K et al. (2016) The Pathway for Oxygen: Tutorial Modelling on Oxygen Transport from Air to Mitochondrion: The Pathway for Oxygen. Adv Exp Med Biol 876:103-10|
|Raymond, G M; Bassingthwaighte, J B (2015) Diverse Data Sets Can Yield Reliable Information through Mechanistic Modeling: Salicylic Acid Clearance. Br J Pharm Res 7:457-476|
|Smith, Lucian P; Butterworth, Erik; Bassingthwaighte, James B et al. (2014) SBML and CellML translation in antimony and JSim. Bioinformatics 30:903-7|
|Veress, A I; Raymond, G M; Gullberg, G T et al. (2013) Left ventricular finite element model bounded by a systemic circulation model. J Biomech Eng 135:54502|
|Alessio, Adam M; Bassingthwaighte, James B; Glenny, Robb et al. (2013) Validation of an axially distributed model for quantification of myocardial blood flow using Â¹Â³N-ammonia PET. J Nucl Cardiol 20:64-75|
|Jardine, Bartholomew; Bassingthwaighte, James B (2013) Modeling serotonin uptake in the lung shows endothelial transporters dominate over cleft permeation. Am J Physiol Lung Cell Mol Physiol 305:L42-55|
|Bassingthwaighte, James B; Chinn, Tamara M (2013) Reexamining Michaelis-Menten enzyme kinetics for xanthine oxidase. Adv Physiol Educ 37:37-48|
|Beard, Daniel A; Neal, Maxwell L; Tabesh-Saleki, Nazanin et al. (2012) Multiscale modeling and data integration in the virtual physiological rat project. Ann Biomed Eng 40:2365-78|
|Bassingthwaighte, James B; Beard, Daniel A; Carlson, Brian E et al. (2012) Modeling to link regional myocardial work, metabolism and blood flows. Ann Biomed Eng 40:2379-98|
|Alessio, Adam M; Butterworth, Erik; Caldwell, James H et al. (2010) Quantitative imaging of coronary blood flow. Nano Rev 1:|
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