Biological Computation group, Microsoft Research, Cambridge, CB1 2FB, UK.
Department of Genetics, Harvard Medical School, Harvard University, Boston, MA, 02115, USA.
Commun Biol. 2021 Jan 26;4(1):118. doi: 10.1038/s42003-020-01599-5.
Molecular biologists rely on the use of fluorescent probes to take measurements of their model systems. These fluorophores fall into various classes (e.g. fluorescent dyes, fluorescent proteins, etc.), but they all share some general properties (such as excitation and emission spectra, brightness) and require similar equipment for data acquisition. Selecting an ideal set of fluorophores for a particular measurement technology or vice versa is a multidimensional problem that is difficult to solve with ad hoc methods due to the enormous solution space of possible fluorophore panels. Choosing sub-optimal fluorophore panels can result in unreliable or erroneous measurements of biochemical properties in model systems. Here, we describe a set of algorithms, implemented in an open-source software tool, for solving these problems efficiently to arrive at fluorophore panels optimized for maximal signal and minimal bleed-through.
分子生物学家依赖于使用荧光探针来对其模型系统进行测量。这些荧光团属于不同的类别（例如荧光染料、荧光蛋白等），但它们都具有一些共同的特性（如激发和发射光谱、亮度），并且需要类似的设备来进行数据采集。为特定的测量技术选择理想的荧光团集，或者反之，是一个多维问题，由于可能的荧光团面板的巨大解决方案空间，很难用特定方法来解决。选择次优的荧光团面板可能会导致模型系统中生化特性的不可靠或错误测量。在这里，我们描述了一组算法，这些算法在一个开源软件工具中实现，可用于有效地解决这些问题，从而得出针对最大信号和最小串扰优化的荧光团面板。
