Agbonkonkon Nosa, Wojciechowski Greg, Abbott Derek A, Gaucher Sara P, Yim Daniel R, Thompson Andrew W, Leavell Michael D
Amyris, Inc., 5885 Hollis Street, Suite 100, Emeryville, CA 94608, USA.
J Ind Microbiol Biotechnol. 2021 Jul 1;48(5-6). doi: 10.1093/jimb/kuab033.
Recent innovations in synthetic biology, fermentation, and process development have decreased time to market by reducing strain construction cycle time and effort. Faster analytical methods are required to keep pace with these innovations, but current methods of measuring fermentation titers often involve manual intervention and are slow, time-consuming, and difficult to scale. Spectroscopic methods like near-infrared (NIR) spectroscopy address this shortcoming; however, NIR methods require calibration model development that is often costly and time-consuming. Here, we introduce two approaches that speed up calibration model development. First, generalized calibration modeling (GCM) or sibling modeling, which reduces calibration modeling time and cost by up to 50% by reducing the number of samples required. Instead of constructing analyte-specific models, GCM combines a reduced number of spectra from several individual analytes to produce a large pool of spectra for a generalized model predicting all analyte levels. Second, randomized multicomponent multivariate modeling (RMMM) reduces modeling time by mixing multiple analytes into one sample matrix and then taking the spectral measurements. Afterward, individual calibration methods are developed for the various components in the mixture. Time saved from the use of RMMM is proportional to the number of components or analytes in the mixture. When combined, the two methods effectively reduce the associated cost and time for calibration model development by a factor of 10.
合成生物学、发酵和工艺开发方面的最新创新通过缩短菌株构建周期时间和工作量，减少了产品上市时间。需要更快的分析方法来跟上这些创新的步伐，但目前测量发酵效价的方法通常需要人工干预，而且速度慢、耗时且难以规模化。近红外（NIR）光谱等光谱方法解决了这一缺点；然而，近红外方法需要开发校准模型，这通常成本高昂且耗时。在此，我们介绍两种加快校准模型开发的方法。首先是广义校准建模（GCM）或同胞建模，通过减少所需样本数量，将校准建模时间和成本降低多达50%。广义校准建模不是构建特定分析物的模型，而是将来自几种单个分析物的数量减少的光谱组合起来，以生成用于预测所有分析物水平的广义模型的大量光谱池。其次，随机多组分多元建模（RMMM）通过将多种分析物混合到一个样品基质中，然后进行光谱测量来减少建模时间。之后，针对混合物中的各种成分开发单独的校准方法。使用随机多组分多元建模节省的时间与混合物中成分或分析物的数量成正比。将这两种方法结合使用时，可有效将校准模型开发的相关成本和时间降低10倍。
