Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
IEEE Trans Nanobioscience. 2011 Mar;10(1):16-29. doi: 10.1109/TNB.2010.2103570. Epub 2011 Feb 22.
We propose a microsphere array device with microspheres having controllable positions for error-free target identification. We conduct a statistical design analysis to select the optimal distance between the microspheres as well as the optimal temperature. Our design simplifies the imaging and ensures a desired statistical performance for a given sensor cost. Specifically, we compute the posterior Cramér-Rao bound on the errors in estimating the unknown target concentrations. We use this performance bound to compute the optimal design variables. We discuss both uniform and sparse concentration levels of targets, and replace the unknown imaging parameters with their maximum likelihood estimates. We illustrate our design concept using numerical examples. The proposed microarray has high sensitivity, efficient packing, and guaranteed imaging performance. It simplifies the imaging analysis significantly by identifying targets based on the known positions of the microspheres. Potential applications include molecular recognition, specificity of targeting molecules, protein-protein dimerization, high throughput screening assays for enzyme inhibitors, drug discovery, and gene sequencing.
我们提出了一种具有可控制位置微球的微球阵列装置，可实现无误差的目标识别。我们进行了统计设计分析，以选择微球之间的最佳距离以及最佳温度。我们的设计简化了成像过程，并确保在给定的传感器成本下具有理想的统计性能。具体来说，我们计算了用于估计未知目标浓度的误差的后验克拉美-罗界。我们使用此性能界限来计算最佳设计变量。我们讨论了均匀和稀疏的目标浓度，并将未知的成像参数替换为它们的最大似然估计。我们使用数值示例来说明我们的设计概念。所提出的微阵列具有高灵敏度、高效包装和保证的成像性能。它通过基于微球的已知位置来识别目标，从而大大简化了成像分析。潜在的应用包括分子识别、靶向分子的特异性、蛋白质-蛋白质二聚化、酶抑制剂的高通量筛选测定、药物发现和基因测序。
