Pagès Guilhem, Kuchel Philip W
Singapore BioImaging Consortium, A STAR, Helios, Singapore.
School of Molecular Bioscience, The University of Sydney, Sydney, Australia.
Magn Reson Insights. 2013 Feb 24;6:13-21. doi: 10.4137/MRI.S11084. eCollection 2013.
Rapid-dissolution dynamic nuclear polarization (DNP) has made significant impact in the characterization and understanding of metabolism that occurs on the sub-minute timescale in several diseases. While significant efforts have been made in developing applications, and in designing rapid-imaging radiofrequency (RF) and magnetic field gradient pulse sequences, very few groups have worked on implementing realistic mathematical/kinetic/relaxation models to fit the emergent data. The critical aspects to consider when modeling DNP experiments depend on both nuclear magnetic resonance (NMR) and (bio)chemical kinetics. The former constraints are due to the relaxation of the NMR signal and the application of 'read' RF pulses, while the kinetic constraints include the total amount of each molecular species present. We describe the model-design strategy we have used to fit and interpret our DNP results. To our knowledge, this is the first report on a systematic analysis of DNP data.
快速溶解动态核极化(DNP)在表征和理解几种疾病中发生在亚分钟时间尺度上的新陈代谢方面产生了重大影响。虽然在开发应用以及设计快速成像射频(RF)和磁场梯度脉冲序列方面已经付出了巨大努力,但很少有团队致力于实现现实的数学/动力学/弛豫模型来拟合新出现的数据。对DNP实验进行建模时需要考虑的关键方面取决于核磁共振(NMR)和(生物)化学动力学。前者的限制源于NMR信号的弛豫和“读取”RF脉冲的应用,而动力学限制包括存在的每种分子物种的总量。我们描述了我们用于拟合和解释DNP结果的模型设计策略。据我们所知,这是关于DNP数据系统分析的第一份报告。