Razo-Mejia Manuel, Mani Madhav, Petrov Dmitri
Department of Biology, Stanford University.
NSF-Simons Center for Quantitative Biology, Northwestern University.
bioRxiv. 2023 Oct 18:2023.10.14.562365. doi: 10.1101/2023.10.14.562365.
The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.
通过DNA条形码测序追踪谱系频率能够对微生物适应性进行量化。然而，来自生物和非生物源的实验噪声使可靠推断的计算变得复杂。我们提出了一种贝叶斯方法，用于从高通量谱系追踪实验中推断相对微生物适应性。我们的模型考虑了多种噪声源，并以系统的方式在所有参数中传播不确定性。此外，使用基于自动微分的现代变分推理方法，我们能够将推理扩展到大量独特的条形码。我们扩展了这个核心模型，以分析多环境实验、重复实验以及与基因型相关的条形码。在模拟实验中，我们的方法在后验可信区间内恢复了已知参数。这项工作提供了一个可推广的贝叶斯框架来分析谱系追踪实验。随附的开源软件库使在实验进化中采用有原则的统计方法成为可能。
